Enterprise Cybersecurity in the Age of AI: Why Legacy Security Is Failing as Attackers Move Faster
Cybersecurity has always been an asymmetric game. But with the rise of AI‑enabled attacks, that imbalance has widened dramatically. Microsoft Threat Intelligence and Microsoft Defender Security Research have publicly reported a clear shift in how attackers operate: AI is now being embedded across the entire attack lifecycle. Threat actors are using it to accelerate reconnaissance, generate highly targeted phishing at scale, automate infrastructure, and adapt their techniques in real time - reducing the time and effort required to move from initial access to impact. In recent months, Microsoft has documented AI‑enabled phishing campaigns abusing legitimate authentication mechanisms - including OAuth and device‑code flows - to compromise enterprise accounts at scale. These campaigns rely on automation, dynamic code generation, and highly personalised lures, rather than on stealing passwords or exploiting traditional vulnerabilities. Meanwhile, many large enterprises are still defending themselves with security controls designed for a very different threat model - one rooted in predictability, static signatures, and trusted perimeters. These approaches were built to stop repeatable attacks, not adversaries that continuously adapt and blend into normal business activity. The result is a dangerous gap: highly adaptive attackers versus static, legacy defences. Below are some of the most common outdated security practices still widely used by enterprises today - and why they are no longer sufficient against modern, AI‑driven threats. 1. Signature‑Based Antivirus Traditional antivirus solutions rely on known signatures and hashes, assuming malware looks the same each time it is deployed. AI has completely broken that assumption. Modern malware families now automatically mutate their code, generate new variants on execution, and adapt behaviour based on the environment they encounter. Microsoft Threat Intelligence has observed multiple actors using AI‑assisted tooling to rapidly rewrite payload components during development and testing, making each deployment look subtly different. In this model, there is no stable signature to detect. By the time a pattern exists, the attacker has already iterated past it. Signature‑based detection is not just slow - it is structurally mismatched to how modern threats operate. What to adopt instead Shift from artifact‑based detection to behavior‑based endpoint protection: EDR/XDR platforms that analyse process behaviour, memory activity, and execution chains Machine‑learning models trained on what attackers do, not what binaries look like Continuous monitoring with automated response, not one‑time blocking 2. Firewalls Many enterprises still rely on firewalls that enforce static allow/deny rules based on ports and IP addresses. That approach worked when applications were predictable and networks were clearly segmented. Today, traffic is encrypted, cloud‑based, API‑driven, and deeply intertwined with legitimate SaaS and identity services. Recent AI‑assisted phishing campaigns abusing legitimate OAuth and device‑code authentication flows illustrate this perfectly. From a network perspective, everything looks allowed: HTTPS traffic to trusted identity providers. There is no suspicious port, no malicious domain, no obvious anomaly - yet the attacker successfully hijacks the authentication process itself. What to adopt instead Move from perimeter controls to identity‑ and context‑aware network security: Application‑aware firewalls with behavioural and risk‑based inspection Integration with identity signals (user, device, location, risk score) Continuous evaluation of sessions, not one‑time allow/deny decisions In modern environments, identity is the new control plane. 3. Single‑Factor Authentication Despite years of guidance, single‑factor passwords remain common - especially for legacy applications, VPN access, and service accounts. AI‑powered credential abuse changes the economics of these attacks entirely. Threat actors now operate credential‑stuffing and phishing campaigns that adapt lures in real time, testing millions of combinations with minimal cost. In multiple Microsoft‑observed campaigns, attackers didn’t brute‑force access broadly. Instead, they used AI to identify which compromised identities were financially or operationally valuable - executives, payroll, procurement - and focused only on those accounts. What to adopt instead Replace static authentication with phishing‑resistant, risk‑based identity controls: Phishing‑resistant MFA (hardware‑backed or passkeys) Conditional access based on user behaviour, device health, and risk Continuous authentication instead of a single login event 4. VPN‑Centric Security VPNs were designed to extend the corporate network to remote users, based on the assumption that “inside” meant trustworthy. That assumption no longer holds. AI‑assisted attacks increasingly exploit VPN access post‑compromise. Once credentials are obtained, automation is used to map internal resources, identify privilege escalation paths, and move laterally - often without triggering traditional alerts. In parallel, Microsoft has observed nation‑state actors using AI to create highly convincing fake employee personas, complete with AI‑generated resumes, consistent communication styles, and synthetic media, allowing them to pass hiring and onboarding processes and gain long‑term, trusted access. In these scenarios, VPN access is not breached - it is granted. What to adopt instead Transition from network trust to Zero Trust access models: Identity‑based access to applications, not networks Least‑privilege, per‑app/user/service access instead of broad internal connectivity Continuous verification using behavioural signals In modern enterprises, access should be explicit, scoped, and continuously re‑evaluated. 5. Treating Unencrypted Data as “Low‑Risk” It is still common to find sensitive data stored unencrypted in older databases, file shares, and backups. In an AI‑driven threat landscape, data discovery is no longer manual or slow. After compromise, attackers increasingly use AI as an on‑demand analyst - summarizing directory structures, classifying stolen datasets, and prioritizing what matters most for impact or monetization. Unencrypted data dramatically lowers the cost and consequence of breach activity, turning what could have been a limited incident into a full‑scale exposure. What to adopt instead Shift from passive data storage to data‑centric security: Encryption by default, both at rest and in transit Data classification and sensitivity labeling built into platforms Access controls tied to data sensitivity, not just system location Begin preparing for post‑quantum cryptography (PQC) as part of long‑term data protection and crypto‑agility strategy 6. Intrusion Detection Systems (IDS) Built on Known Patterns Traditional IDS platforms look for known indicators of compromise - assuming attackers reuse the same tools and techniques. AI‑driven attacks deliberately avoid that assumption. Microsoft Threat Intelligence reports actors using large language models to quickly analyse publicly disclosed vulnerabilities, understand exploitation paths, and compress the time between disclosure and weaponization. This isn’t about zero‑days - it’s about speed. What once took days or weeks now takes hours. Legacy IDS platforms often fail silently in these scenarios, detecting only what they already know how to recognize. What to adopt instead Move from static detection to adaptive, correlation‑based threat detection: Graph‑based XDR platforms correlating signals across identity, endpoint, email, cloud, and network Anomaly detection that focuses on deviation from normal behaviour Automated investigation and response to match attacker speed Closing Thought: Security Is a Journey, Not a Destination AI is not a future cybersecurity problem. It is a current force multiplier for attackers - and it is exposing the limits of legacy security architectures faster than many organisations are willing to admit. A realistic security strategy starts with an uncomfortable but necessary acknowledgement: no organisation can be 100% secure. Intrusions will happen. Credentials will be compromised. Controls will be tested. The difference between a resilient enterprise and a vulnerable one is not the absence of incidents, but how effectively risk is managed when they occur. In mature organisations, this means assuming breach and designing for containment. Strong access controls limit blast radius. Least privilege and conditional access reduce what an attacker can reach. Data Loss Prevention (DLP) ensures that even when access is misused, sensitive data cannot be freely exfiltrated. Just as importantly, leaders understand the business consequences of compromise - which data matters most, which systems are critical, and which risks are acceptable versus existential. As a cybersecurity architect, I see this moment as a unique opportunity. AI adoption does not have to repeat the mistakes of earlier technology waves, where innovation moved fast and security followed years later. AI gives organisations the chance to introduce a new class of service while embedding security from day one - designing access, data boundaries, monitoring, and governance into the platform before it becomes business‑critical. When security is built in upfront, enterprises don’t just reduce risk - they gain confidence to move faster and truly leverage AI’s value. Security, especially in the age of AI, is not about preventing every intrusion. It is about controlling impact, preserving trust, and maintaining operational continuity in a world where attackers move faster than ever. In the age of AI, standing still is the same as falling behind. References: Inside an AI‑enabled device code phishing campaign | Microsoft Security Blog AI as tradecraft: How threat actors operationalize AI | Microsoft Security Blog Detecting and analyzing prompt abuse in AI tools | Microsoft Security Blog Post-Quantum Cryptography | CSRC Microsoft Digital Defense Report 2025 | MicrosoftDriving DevSecOps Standards: NIST’s Live Guidelines for Secure Software Development, Security, and Operations Practices
Microsoft appreciates the opportunity to participate in the National Institute of Standards and Technology’s (NIST) effort to evolve the Live Guidelines for Secure Software Development, Security, and Operations (DevSecOps) Practices, building on the original NIST SP 1800-44 publication. This living guidance reflects ongoing, collaborative work to document practical approaches for securing the software development lifecycle, addressing challenges such as open-source risk, software supply chain integrity, Software Bill of Materials (SBOM), insider threats, and Zero Trust principles. This project is led by the National Cybersecurity Center of Excellence (NCCoE) through the National Cybersecurity Excellence Partnership (NCEP) consortium, with contributions from government, industry, and academia. The resulting guidance is intended to help organizations apply standards-based DevSecOps practices using reference implementations developed under NCCoE leadership. Our team at Microsoft was honored to share frameworks, tools, and expertise to help deploy and configure secure Azure DevOps and GitHub environments. These efforts were complemented by open-source tooling and partner solutions, resulting in CI/CD examples that reflect industry's best practices. Some of the contributions from Microsoft included: OpenSSF Secure Supply Chain Consumption Framework (S2C2F) – this is a framework of requirements, organized into a maturity model that is hyper-focused on how to securely consume open-source dependencies into the developer’s workflow. Microsoft SBOM tool – General purpose, cross-platform, open source SBOM generator that produces SPDX SBOMs at build time. GitHub Advanced Security – suite of tools available on GitHub and Azure DevOps that perform static code analysis scans, software composition analysis, automated dependency updates, and more. Defender for Cloud DevOps security – provides a centralized console to empower security teams to protect applications and resources from code to cloud across multi-pipeline environments, including Azure DevOps, GitHub, and GitLab. Note: These tools are referenced solely as examples used in the NCCoE reference implementations. NIST and NCCoE do not evaluate, recommend, or endorse any commercial product or service. The Live Guidelines for DevSecOps Practices also explore how AI can automate requirements management, code generation, vulnerability analysis, and risk mitigation across the software development lifecycle. These AI-assisted capabilities, embedded within a Zero Trust framework, enforce least privilege and continuous validation. With human oversight, transparency, and audit trails, this approach aims to support secure, compliant automation—reflecting our ongoing commitment to trustworthy DevSecOps. These examples are intended to inform discussion and public feedback as the guidance evolves, rather than prescribe specific implementations. This project is a collaborative effort led by the National Cybersecurity Center of Excellence (NCCoE) through the National Cybersecurity Excellence Partnership (NCEP) consortium, with NIST guiding the work. We are one of many contributors, and we value the broader industry partnership that makes this work possible. The National Cybersecurity Center of Excellence (NCCoE), a part of the National Institute of Standards and Technology (NIST), is a collaborative hub where industry organizations, government agencies, and academic institutions work together to address businesses’ most pressing cybersecurity challenges. Through this collaboration, the NCCoE develops modular, adaptable example cybersecurity solutions demonstrating how to apply standards and best practices using commercially available technology. Information is available at: https://nccoe.nist.gov. Why NIST’s Live Guidelines for Secure Software Development, Security, and Operations (DevSecOps) Practices Matters The Live Guidelines for DevSecOps Practices provide a practical blueprint for secure development that organizations can adopt with confidence. Many small and medium-sized businesses struggle to understand what a secure DevOps configuration should look like, or how the DevSecOps lifecycle differs from DevOps. The work in the Live Guidelines for DevSecOps Practices addresses this challenge by describing the industry best practices for the components and activities in each lifecycle phase, mapping them to NIST SP 800-218 Secure Software Development Framework (SSDF) and noting where AI integrates with activities. This work was validated against two reference builds—one exercising Microsoft’s entire developer stack, and a similar industry stack, deployed on the Azure platform—ensuring NIST guidance reflects real-world, proven practices. The Live Guidelines also explore how AI-assisted capabilities may support activities such as requirements management, code analysis, vulnerability identification, and risk mitigation across the software development lifecycle. When applied within a Zero Trust framework, these capabilities emphasize least-privileged access, continuous validation, transparency, and auditability, with appropriate human oversight. As the Live Guidelines for DevSecOps Practices enters its public comment phase, we encourage the community to participate and help shape its future direction. Microsoft’s Contributions As part of the NCEP consortium, Microsoft is one of many contributors supporting the development of reference implementations used to validate the Live Guidelines for DevSecOps Practices. Contributors shared engineering experience, architectural patterns, and example configurations to help ensure the guidance reflects real-world deployment considerations across the software development lifecycle. Through participation in the NCEP consortium’s work with NCCoE, we have shared solutions that can be adopted across sectors, supporting the nation’s critical infrastructure by fostering innovation and collaboration among stakeholders. Key contributions include: Contributing engineering expertise and implementation experience to one of the reference builds developed under NCCoE leadership for NIST’s Live Guidelines for Secure Software Development, Security, and Operations (DevSecOps) Practices Supporting the elevation of the SSDF to national and international standards Sharing practical insights from our engineering practices to ensure guidance is actionable and scalable Providing real-world examples of tools and configurations to achieve end-to-end supply chain security, fused with DevSecOps, and extended through deployment into the operational phase in Azure We see our role as both solution builder and platform provider, and we strive to support standards that matter most to customers and regulators. Connecting DevSecOps, Zero Trust, and the Secure Future Initiative While DevSecOps is the focus, for Microsoft it is built on foundational principles: Zero Trust Architecture (ZTA): The security model underpinning modern DevSecOps. Secure Future Initiative (SFI): Microsoft’s implementation of Zero Trust, now mapped to NIST Cybersecurity Framework (CSF) for global alignment. This integration ensures that DevSecOps guidance is secure-by-design and consistent with widely recognized frameworks—boosting customer confidence worldwide. Looking Ahead This is just the beginning. NIST SP1800-44 DevSecOps Practices started the journey, and the updates in the Live Guidelines for DevSecOps Practices continue the momentum, with more resources to follow. As future resources roll out, Microsoft will continue to share tools, insights, and best practices to help organizations adopt secure development at scale. By partnering with government institutions and industry participants, we’re shaping the future of cybersecurity—together. Next Steps Engage in the public comment phase for the Live Guidelines for Secure Software Development, Security, and Operations (DevSecOps) Practices document and help define the next generation of secure software development. Learn more about Microsoft Security solutions here.
Building Secure, Enterprise Ready AI Agents with Purview SDK and Agent Framework
At Microsoft Ignite, we announced the public preview of Purview integration with the Agent Framework SDK—making it easier to build AI agents that are secure, compliant, and enterprise‑ready from day one. AI agents are quickly moving from demos to production. They reason over enterprise data, collaborate with other agents, and take real actions. As that happens, one thing becomes non‑negotiable: Governance has to be built in. That’s where Purview SDK comes in. Agentic AI Changes the Security Model Traditional apps expose risks at the UI or API layer. AI agents are different. Agents can: Process sensitive enterprise data in prompts and responses Collaborate with other agents across workflows Act autonomously on behalf of users Without built‑in controls, even a well‑designed agent can create compliance gaps. Purview SDK brings Microsoft’s enterprise data security and compliance directly into the agent runtime, so governance travels with the agent—not after it. What You Get with Purview SDK + Agent Framework This integration delivers a few key things developers and enterprises care about most: Inline Data Protection Evaluate prompts and responses against Data Loss Prevention (DLP) policies in real time. Content can be allowed or blocked automatically. Built‑In Governance Send AI interactions to Purview for audit, eDiscovery, communication compliance, and lifecycle management—without custom plumbing. Enterprise‑Ready by Design Ship agents that meet enterprise security expectations from the start, not as a follow‑up project. All of this is done natively through Agent Framework middleware, so governance feels like part of the platform—not an add‑on. How Enforcement Works (Quickly) When an agent runs: Prompts and responses flow through the Agent Framework pipeline Purview SDK evaluates content against configured policies A decision is returned: allow, redact, or block Governance signals are logged for audit and compliance This same model works for: User‑to‑agent interactions Agent‑to‑agent communication Multi‑agent workflows Try It: Add Purview SDK in Minutes Here’s a minimal Python example using Agent Framework: That’s it! From that point on: Prompts and responses are evaluated against Purview policies setup within the enterprise tenant Sensitive data can be automatically blocked Interactions are logged for governance and audit Designed for Real Agent Systems Most production AI apps aren’t single‑agent systems. Purview SDK supports: Agent‑level enforcement for fine‑grained control Workflow‑level enforcement across orchestration steps Agent‑to‑agent governance to protect data as agents collaborate This makes it a natural fit for enterprise‑scale, multi‑agent architectures. Get Started Today You can start experimenting right away: Try the Purview SDK with Agent Framework Follow the Microsoft Learn docs to configure Purview SDK with Agent Framework. Explore the GitHub samples See examples of policy‑enforced agents in Python and .NET. Secure AI, Without Slowing It Down AI agents are quickly becoming production systems—not experiments. By integrating Purview SDK directly into the Agent Framework, Microsoft is making governance a default capability, not a deployment blocker. Build intelligent agents. Protect sensitive data. Scale with confidence.Driving DevSecOps Standards: Microsoft’s Role in Shaping NIST SP 1800-44 Volume B
We are grateful for the opportunity to contribute to the National Institute of Standards and Technology’s (NIST) SP 1800-44 Volume B – DevSecOps Architecture. This publication represents a significant step forward in defining a reference architecture for secure software development, and we appreciate the collaborative efforts of all involved. Security is woven throughout the development lifecycle, addressing challenges such as open-source risk, software supply chain integrity, Software Bill of Materials (SBOM) requirements, insider threats, and Zero Trust principles. Our team at Microsoft was honored to share frameworks, tools, and expertise to help deploy and configure secure Azure DevOps and GitHub environments. These efforts were complemented by open-source tooling and partner solutions, resulting in CI/CD examples that reflect industry's best practices. Some of the contributions from Microsoft included: OpenSSF Secure Supply Chain Consumption Framework (S2C2F) – this is a framework of requirements, organized into a maturity model that is hyper-focused on how to securely consume open-source dependencies into the developer’s workflow. Microsoft SBOM tool – General purpose, cross-platform, open source SBOM generator that produces SPDX SBOMs at build time. Copacetic – Open-source tool that automates patching containers at build time. GitHub Advanced Security – suite of tools available on GitHub and Azure DevOps that perform static code analysis scans, software composition analysis, automated dependency updates, and more. Defender for Cloud DevOps security – provides a centralized console to empower security teams to protect applications and resources from code to cloud across multi-pipeline environments, including Azure DevOps, GitHub, and GitLab. Volume B also explores how AI can automate requirements management, code generation, vulnerability analysis, and risk mitigation across the software development lifecycle. These AI-assisted capabilities, embedded within a Zero Trust framework, enforce least privilege and continuous validation. With human oversight, transparency, and audit trails, this approach aims to support secure, compliant automation—reflecting our ongoing commitment to trustworthy DevSecOps. This project is a collaborative effort led by the National Cybersecurity Center of Excellence (NCCoE) through the National Cybersecurity Excellence Partnership (NCEP) consortium, with NIST guiding the work. We are one of many contributors, and we value the broader industry partnership that makes this work possible. The NCCoE brings together government, industry, and academia to address critical cybersecurity challenges and develop practical, standards-based solutions. Why NIST’s SP 1800-44 Volume B Matters Volume B provides a practical blueprint for secure development that organizations can adopt with confidence. Many small and medium-sized businesses struggle to understand what a secure DevOps configuration should look like, or how the DevSecOps lifecycle differs from DevOps. The work in Volume B addresses this challenge by describing the industry best practices for the components and activities in each lifecycle phase, mapping them to NIST SP 800-218 Secure Software Development Framework (SSDF) and noting where AI integrates with activities. This work was validated against two reference builds—one exercising Microsoft’s entire developer stack, and a similar industry stack, deployed on the Azure platform—ensuring NIST guidance reflects real-world, proven practices. As Volume B enters its public comment phase, we encourage the community to participate and help shape its future direction. Microsoft’s Contributions Through participation in the NCEP consortium’s work with NCCoE, we have shared solutions that can be adopted across sectors, supporting the nation’s critical infrastructure by fostering innovation and collaboration among stakeholders. Key contributions include: Helping develop the first reference build for NIST’s SP 1800-44 Volume B Supporting the elevation of the SSDF to national and international standards Sharing practical insights from our engineering practices to ensure guidance is actionable and scalable Providing real-world examples of tools and configurations to achieve end-to-end supply chain security, fused with DevSecOps, and extended through deployment into the operational phase in Azure We see our role as both solution builder and platform provider, and we strive to support standards that matter most to customers and regulators. Connecting DevSecOps, Zero Trust, and the Secure Future Initiative While DevSecOps is the focus, for Microsoft it is built on foundational principles: Zero Trust Architecture (ZTA): The security model underpinning modern DevSecOps. Secure Future Initiative (SFI): Microsoft’s implementation of Zero Trust, now mapped to NIST CSF for global alignment. This integration ensures that DevSecOps guidance is secure-by-design and consistent with widely recognized frameworks—boosting customer confidence worldwide. Looking Ahead This is just the beginning. Volume A started the journey, and Volume B continues the momentum, with more volumes to follow. As future volumes roll out, Microsoft will continue to share tools, insights, and best practices to help organizations adopt secure development at scale. By partnering with government institutions and industry participants, we’re shaping the future of cybersecurity—together. Call to Action Engage in the public comment phase for SP 1800-44 Volume B and help define the next generation of secure software development. Learn more about Microsoft Security solutions
Artificial Intelligence & Security
Understanding Artificial Intelligence Artificial intelligence (AI) is a computational system that perform human‑intelligence tasks, learning, reasoning, problem‑solving, perception, and language understanding by leveraging algorithmic and statistical methods to analyse data and make informed decisions. Artificial Intelligence (AI) can also be abbreviated as is the simulation of human intelligence through machines programmed to learn, reason, and act. It blends statistics, machine learning, and robotics to deliver following outcomes: Prediction: The application of statistical modelling and machine learning techniques to anticipate future outcomes, such as detecting fraudulent transactions. Automation: The utilisation of robotics and artificial intelligence to streamline and execute routine processes, exemplified by automated invoice processing. Augmentation: The enhancement of human decision-making and operational capabilities through AI-driven tools, for instance, AI-assisted sales enablement. Artificial Intelligence: Core Capabilities and Market Outlook Key capabilities of AI include: Data-driven decision-making: Analysing large datasets to generate actionable insights and optimise outcomes. Anomaly detection: Identifying irregular patterns or deviations in data for risk mitigation and quality assurance. Visual interpretation: Processing and understanding visual inputs such as images and videos for applications like computer vision. Natural language understanding: Comprehending and interpreting human language to enable accurate information extraction and contextual responses. Conversational engagement: Facilitating human-like interactions through chatbots, virtual assistants, and dialogue systems. With the exponential growth of data, ML learning models and computing power. AI is advancing much faster and as According to industry analyst reports breakthroughs in deep learning and neural network architectures have enabled highly sophisticated applications across diverse sectors, including healthcare, finance, manufacturing, and retail. The global AI market is on a trajectory of significant expansion, projected to increase nearly 5X by 2030, from $391 billion in 2025 to $1.81 trillion. This growth corresponds to a compound annual growth rate (CAGR) of 35.9% during the forecast period. These projections are estimates and subject to change as per rapid growth and advancement in the AI Era. AI and Cloud Synergy AI, and cloud computing form a powerful technological mixture. Digital assistants are offering scalable, cloud-powered intelligence. Cloud platforms such as Azure provide pre-trained models and services, enabling businesses to deploy AI solutions efficiently. Core AI Workloads Capabilities Machine Learning Machine learning (ML) underpins most AI systems by enabling models to learn from historical and real-time data to make predictions, classifications, and recommendations. These models adapt over time as they are exposed to new data, improving accuracy and robustness. Example use cases: Credit risk scoring in banking, demand forecasting in retail, and predictive maintenance in manufacturing. Anomaly Detection Anomaly detection techniques identify deviations from expected patterns in data, systems, or processes. This capability is critical for risk management and operational resilience, as it enables early detection of fraud, security breaches, or equipment failures. Example use cases: Fraud detection in financial transactions, network intrusion monitoring in cybersecurity, and quality control in industrial production. Natural Language Processing (NLP) NLP focuses on enabling machines to understand, interpret, and generate human language in both text and speech formats. This capability powers a wide range of applications that require contextual comprehension and semantic accuracy. Example use cases: Sentiment analysis for customer feedback, document summarisation for legal and compliance teams, and multilingual translation for global operations. Principles of Responsible AI To ensure ethical and trustworthy AI, organisations must embrace: Reliability & Safety Privacy & Security Inclusiveness Fairness Transparency Accountability These principles are embedded in frameworks like the Responsible-AI-Standard and reinforced by governance models such as Microsoft AI Governance Framework. Responsible AI Principles and Approach | Microsoft AI AI and Security AI introduces both opportunities and risks. A responsible approach to AI security involves three dimensions: Risk Mitigation: It Is addressing threats from immature or malicious AI applications. Security Applications: These are used to enhance AI security and public safety. Governance Systems: Establishing frameworks to manage AI risks and ensure safe development. Security Risks and Opportunities Due to AI Transformation AI’s transformative nature brings new challenges: Cybersecurity: This brings the opportunities and advancement to track, detect and act against Vulnerabilities in infrastructure and learning models. Data Security: This helps the tool and solutions such as Microsoft Purview to prevent data security by performing assessments, creating Data loss prevention policies applying sensitivity labels. Information Security: The biggest risk is securing the information and due to the AI era of transformation securing IS using various AI security frameworks. These concerns are echoed in The Crucial Role of Data Security Posture Management in the AI Era, which highlights insider threats, generative AI risks, and the need for robust data governance. AI in Security Applications AI’s capabilities in data analysis and decision-making enable innovative security solutions: Network Protection: applications include use of AI algorithms for intrusion detection, malware detection, security situational awareness, and threat early warning, etc. Data Management: applications refer to the use of AI technologies to achieve data protection objectives such as hierarchical classification, leak prevention, and leak traceability. Intelligent Security: applications refer to the use of AI technology to upgrade the security field from passive defence toward the intelligent direction, developing of active judgment and timely early warning. Financial Risk Control: applications use AI technology to improve the efficiency and accuracy of credit assessment, risk management, etc., and assisting governments in the regulation of financial transactions. AI Security Management Effective AI security requires: Regulations & Policies: Establish and safety management laws specifically designed to for governance by regulatory authorities and management policies for key application domains of AI and prominent security risks. Standards & Specifications: Industry-wide benchmarks, along with international and domestic standards can be used to support AI safety. Technological Methods: Early detection with Modern set of tools such as Defender for AI can be used to support to detect and mitigate and remediate AI threats. Security Assessments: Organization should use proper tools and platforms for evaluating AI risks and perform assessments regularly using automated tools approach Conclusion AI is transforming how organizations operate, innovate, and secure their environments. As AI capabilities evolve, integrating security and governance considerations from the outset remains critical. By combining responsible AI principles, effective governance, and appropriate security measures, organizations can work toward deploying AI technologies in a manner that supports both innovation and trust. Industry projections suggest continued growth in AI‑related security investments over the coming years, reflecting increased focus on managing AI risks alongside its benefits. These estimates are subject to change and should be interpreted in the context of evolving technologies and regulatory developments. Disclaimer References to Microsoft products and frameworks are for informational purposes only and do not imply endorsement, guarantee, or contractual commitment. Market projections referenced are based on publicly available industry analyses and are subject to change.Microsoft Ignite 2025: Top Security Innovations You Need to Know
🤖 Security & AI -The Big Story This Year 2025 marks a turning point for cybersecurity. Rapid adoption of AI across enterprises has unlocked innovation but introduced new risks. AI agents are now part of everyday workflows-automating tasks and interacting with sensitive data—creating new attack surfaces that traditional security models cannot fully address. Threat actors are leveraging AI to accelerate attacks, making speed and automation critical for defense. Organizations need solutions that deliver visibility, governance, and proactive risk management for both human and machine identities. Microsoft Ignite 2025 reflects this shift with announcements focused on securing AI at scale, extending Zero Trust principles to AI agents, and embedding intelligent automation into security operations. As a Senior Cybersecurity Solution Architect, I’ve curated the top security announcements from Microsoft Ignite 2025 to help you stay ahead of evolving threats and understand the latest innovations in enterprise security. Agent 365: Control Plane for AI Agents Agent 365 is a centralized platform that gives organizations full visibility, governance, and risk management over AI agents across Microsoft and third-party ecosystems. Why it matters: Unmanaged AI agents can introduce compliance gaps and security risks. Agent 365 ensures full lifecycle control. Key Features: Complete agent registry and discovery Access control and conditional policies Visualization of agent interactions and risk posture Built-in integration with Defender, Entra, and Purview Available via the Frontier Program Microsoft Agent 365: The control plane for AI agents Deep dive blog on Agent 365 Entra Agent ID: Zero Trust for AI Identities Microsoft Entra is the identity and access management suite (covering Azure AD, permissions, and secure access). Entra Agent ID extends Zero Trust identity principles to AI agents, ensuring they are governed like human identities. Why it matters: Unmanaged or over-privileged AI agents can create major security gaps. Agent ID enforces identity governance on AI agents and reduces automation risks. Key Features: Provides unique identities for AI agents Lifecycle governance and sponsorship for agents Conditional access policies applied to agent activity Integrated with open SDKs/APIs for third‑party platforms Microsoft Entra Agent ID Overview Entra Ignite 2025 announcements Public Preview details Security Copilot Expansion Security Copilot is Microsoft’s AI assistant for security teams, now expanded to automate threat hunting, phishing triage, identity risk remediation, and compliance tasks. Why it matters: Security teams face alert fatigue and resource constraints. Copilot accelerates response and reduces manual effort. Key Features: 12 new Microsoft-built agents across Defender, Entra, Intune, and Purview. 30+ partner-built agents available in the Microsoft Security Store. Automates threat hunting, phishing triage, identity risk remediation, and compliance tasks. Included for Microsoft 365 E5 customers at no extra cost. Security Copilot inclusion in Microsoft 365 E5 Security Copilot Ignite blog Security Dashboard for AI A unified dashboard for CISOs and risk leaders to monitor AI risks, aggregate signals from Microsoft security services, and assign tasks via Security Copilot - included at no extra cost. Why it matters: Provides a single pane of glass for AI risk management, improving visibility and decision-making. Key Features: Aggregates signals from Entra, Defender, and Purview Supports natural language queries for risk insights Enables task assignment via Security Copilot Ignite Session: Securing AI at Scale Microsoft Security Blog Microsoft Defender Innovations Microsoft Defender serves as Microsoft’s CNAPP solution, offering comprehensive, AI-driven threat protection that spans endpoints, email, cloud workloads, and SIEM/SOAR integrations. Why It Matters Modern attacks target multi-cloud environments and software supply chains. These innovations provide proactive defense, reduce breach risks before exploitation, and extend protection beyond Microsoft ecosystems-helping organizations secure endpoints, identities, and workloads at scale. Key Features: Predictive Shielding: Proactively hardens attack paths before adversaries pivot. Automatic Attack Disruption: Extended to AWS, Okta, and Proofpoint via Sentinel. Supply Chain Security: Defender for Cloud now integrates with GitHub Advanced Security. What’s new in Microsoft Defender at Ignite Defender for Cloud innovations Global Secure Access & AI Gateway Part of Microsoft Entra’s secure access portfolio, providing secure connectivity and inspection for web and AI traffic. Why it matters: Protects against lateral movement and AI-specific threats while maintaining secure connectivity. Key Features: TLS inspection, URL/file filtering AI Prompt Injection protection Private access for domain controllers to prevent lateral movement attacks. Learn about Secure Web and AI Gateway for agents Microsoft Entra: What’s new in secure access on the AI frontier Purview Enhancements Microsoft Purview is the data governance and compliance platform, ensuring sensitive data is classified, protected, and monitored. Why it matters: Ensures sensitive data remains protected and compliant in AI-driven environments. Key Features: AI Observability: Monitor agent activities and prevent sensitive data leakage. Compliance Guardrails: Communication compliance for AI interactions. Expanded DSPM: Data Security Posture Management for AI workloads. Announcing new Microsoft Purview capabilities to protect GenAI agents Intune Updates Microsoft Intune is a cloud-based endpoint device management solution that secures apps, devices, and data across platforms. It simplifies endpoint security management and accelerates response to device risks using AI. Why it matters: Endpoint security is critical as organizations manage diverse devices in hybrid environments. These updates reduce complexity, speed up remediation, and leverage AI-driven automation-helping security teams stay ahead of evolving threats. Key Features: Security Copilot agents automate policy reviews, device offboarding, and risk-based remediation. Enhanced remote management for Windows Recovery Environment (WinRE). Policy Configuration Agent in Intune lets IT admins create and validate policies with natural language What’s new in Microsoft Intune at Ignite Your guide to Intune at Ignite Closing Thoughts Microsoft Ignite 2025 signals the start of an AI-driven security era. From visibility and governance for AI agents to Zero Trust for machine identities, automation in security operations, and stronger compliance for AI workloads-these innovations empower organizations to anticipate threats, simplify governance, and accelerate secure AI adoption without compromising compliance or control. 📘 Full Coverage: Microsoft Ignite 2025 Book of News
Transforming Security Analysis into a Repeatable, Auditable, and Agentic Workflow
Author(s): Animesh Jain, Vinay Yadav Shaped by investigations into the strategic question of what it takes for Windows to achieve world-leading security—and the practical engineering needed to explore agentic workflows at scale and their interfaces. Our work in Windows Servicing & Delivery (WSD) is shaped by two guiding prompts from leadership: "what does it take for Windows to achieve world-leading security", and "how do we responsibly integrate AI into systems as large and high-churn as Windows?". Reasoning models open new possibilities on both fronts. As we continue experimenting, one issue repeatedly surfaces as the bottleneck for scalable security assurance: variant vulnerabilities. They are subtle, recurring, and easy to miss—making them an ideal proving ground for the enterprise-grade workflow we present here. Security Analysis at Windows Scale Security analysis shouldn’t be an afterthought—it should be a continuous, auditable, and intelligence-driven process built directly into the engineering workflow. This work introduces an agentic security analysis pipeline that uses reasoning models and tool-based agents to detect variant vulnerabilities across large, fast-changing codebases. By combining automation with explainability, it transforms security validation from a manual, point-in-time task into a repeatable and trustworthy part of every build. Why are variants the hard part? Security flaws rarely occur in isolation. Once a vulnerability is fixed, its logical or structural pattern often reappears elsewhere in the codebase—hidden behind different variables, layers, or call paths. These recurring patterns are variants—the quiet echoes of known issues that can persist across millions of lines of code. Finding them manually is slow, repetitive, and incomplete. As engineering velocity increases, so does the likelihood of variant drift—the same vulnerability class re-emerging in a slightly altered form. Each missed variant carries a downstream cost: regression, re-servicing, or, in the worst cases, re-exploitation. Modern large systems like Windows are too large, too interconnected, and ship too frequently for manual vulnerability discovery to keep pace. Traditional static analyzers and deterministic class-based scanners struggle to generalize these patterns or create too much noise, while targeted fuzzing campaigns often fail to trigger the nuanced runtime conditions that expose them. To stay ahead, automation must evolve. We need systems that reason—not just scan—systems capable of understanding relationships between code regions and applying logical analogies instead of brute-force enumeration. Reasoning Models: A Turning Point in Security Research Recent advances in AI reasoning have demonstrated that large language models can uncover vulnerabilities previously missed by deterministic tools. For example, Google’s Big Sleep agent surfaced an exploitable SQLite flaw (CVE-2025-6965) that bypassed traditional fuzzers due to configuration-sensitive logic. Similarly, an o-series reasoning model helped identify a critical Linux SMB logoff use-after-free (CVE-2025-37899), proving that reasoning-driven automation can detect complex, context-dependent flaws in mature kernel code. These breakthroughs show what’s possible when systems can form, test, and refine hypotheses about software behavior. The challenge now is scaling that intelligence into repeatable, auditable, enterprise-grade workflows—where every result is traceable, reviewable, and integrated into the developer’s daily workflow. A Framework for Agentic Security Analysis To address this challenge, we’ve developed an agentic security analysis framework that applies reasoning models within structured, enterprise grade workflow pattern. It combines large language model agents, specialized analysis tools, and structured artifact generation to make vulnerability discovery continuous, explainable, and auditable. It is interfaced as a first-class Azure DevOps (ADO) pipeline and can be integrated natively into enterprise CI/CD processes. For security analysis, it continuously reasons over large, evolving codebases to identify and validate variant vulnerabilities earlier in the release cycle. Together, these components form a repeatable workflow that helps surface variant patterns with greater consistency and clarity. Core Technical Pillars Scale – Autonomous Code Reasoning Long-context models extend analysis across massive, evolving codebases. They infer analogies, relationships, and behavioral patterns between code regions, enabling scalable reasoning that adapts as systems grow. Tool–Agent Collaboration Specialized agents coordinate to perform semantic search, graph traversal, and both static and dynamic interpretation. This distributed reasoning approach ensures resilience and precision across diverse enterprise environments. Structured Artifact Generation Every step produces versioned, auditable artifacts that document the reasoning process. These artifacts help provide reproducibility, compliance, and transparency—critical for enterprise governance and regulated industries. Together, these pillars enable scalable, explainable, and repeatable vulnerability discovery across large software ecosystems such as Windows. Every stage—from reasoning to validation—is logged and traceable, designed to make each discovery reproducible and reviewable. Inside the framework Agent-Led, Human-Reviewed The system is agent-led from start to finish and human-reviewed only at decision boundaries. Agents form hypotheses from recent fixes or vulnerability classes, test them against context, perform validation passes, and generate evidence-backed reports for reviewer confirmation. The workflow mirrors how seasoned security engineers operate—only faster and continuously. n tasks based on templatized prompts. Tool Specialists as Agents Each analytical tool functions as a domain-specific agent—performing semantic search, file inspection, or function-graph traversal. These agents collaborate through structured orchestration, maintaining specialization without sacrificing coherence. Agentic Patterns and Orchestration The framework employs reusable reasoning patterns—reflective reasoning, actor–validator loops, and parallel tool dialogues—for accuracy and scale. A central conductor agent governs task coordination, context flow, and artifact persistence across runs. Auditability Through Artifacts Every investigation yields a transparent chain of artifacts: Analysis Notes – summarize candidate issues Critique Notes – document reasoning and counter-evidence Synthesis Reports – provide developer-ready summaries, diffs, call graphs, and exploitability insights Agentic Conversation Logs - provides conversation logs so developers can backtrack on reasoning and get more context This structure makes each discovery fully traceable and auditable. CI/CD-Native Integration The interface operates as a first-class Azure DevOps pipeline, attachable to pull requests, nightly builds, or release triggers. Each run publishes versioned artifacts and validation notes directly into the developer workflow—making reasoning-driven security a seamless part of software delivery. What It Can Do Today Seeded Variant Hunts: Start from a recent fix or known pattern to enumerate analogous cases, analyze helper functions, and test reachability. Evidence-First Reporting: Every finding includes reproducible evidence—code snippets, diffs, and caller graphs—delivered within the PR or work item. Scalable Coverage: Runs across servicing branches, producing consistent and auditable validation artifacts. Improved Precision: A reasoning-based validation pass has significantly reduced false positives in internal testing. Case Study: CVE-2025-55325 During a sweep of “*_DEFAULTS” deserializers, the agentic pipeline independently identified GetPoolDefaults trusting a user-controlled size field and copying that many bytes from a caller buffer. The missing runtime bounds check—guarded only by an assertion in debug builds—enabled a potential read access violation and information disclosure. The mitigation mirrored a hardened sibling helper: enforcing runtime bounds on Size versus BytesAvailable/Version before allocation and copy. The finding was later validated by the servicing teams, confirming it matched an issue already under active investigation—illustrating how the automated reasoning process can independently surface real-world vulnerabilities that align with expert analysis. Beyond Variant Analysis The underlying architecture of this framework extends naturally beyond variant detection: Net-new vulnerability discovery through cross-binary pattern matching Model-assisted fuzzing & static analysis orchestrated through CI/CD integration Regression detection via historical code comparisons Security Development Lifecycle (SDL) enforcement and reproducibility checks The agentic patterns and tooling can support net-new vulnerability discovery through cross-binary pattern matching, regression detection using historical code comparisons, reproducibility checks aligned with SDL requirements, and model-assisted fuzzing orchestrated through CI/CD processes. These capabilities open the door to applying reasoning-driven workflows across a broader range of security & validation tasks. The Road Ahead Looking ahead, this trajectory naturally leads toward autonomous cybersecurity pipelines powered by reasoning agents that apply reflective analysis, validation loops, and structured tool interactions to complex codebases. By structuring each step as an auditable artifact, the approach supports security & validation analysis that is both explainable and repeatable. These agents could help validate security posture, analyze historical and real-time signals, and detect anomalous patterns early in the lifecycle. References Google Cloud Blog – Big Sleep and AI-Assisted Vulnerability Discovery “A summer of security: empowering cyber defenders with AI.” https://blog.google/technology/safety-security/cybersecurity-updates-summer-2025 The Hacker News – Google AI ‘Big Sleep’ Stops Exploitation of Critical SQLite Flaw https://thehackernews.com/2025/07/google-ai-big-sleep-stops-exploitation.html NIST National Vulnerability Database – CVE-2025-6965 (SQLite) https://nvd.nist.gov/vuln/detail/CVE-2025-6965 Sean Heelan – “Reasoning Models and the ksmbd Use-After-Free” https://simonwillison.net/2025/May/24/sean-heelan The Cyber Express – AI Finds CVE-2025-37899 Zero-Day in Linux SMB Kernel https://thecyberexpress.com/cve-2025-37899-zero-day-in-linux-smb-kernel NIST National Vulnerability Database – CVE-2025-37899 (Linux SMB Use-After-Free) https://nvd.nist.gov/vuln/detail/CVE-2025-37899 NIST National Vulnerability Database – CVE-2025-55325 (Windows Storage Management Provider Buffer Over-read) https://nvd.nist.gov/vuln/detail/CVE-2025-55325 NVD Microsoft Security Response Center – Vulnerability Details for CVE-2025-55325 https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-55325Microsoft Security Store: Now Generally Available
When we launched the Microsoft Security Store in public preview on September 30, our goal was simple: make it easier for organizations to discover, purchase, and deploy trusted security solutions and AI agents that integrate seamlessly with Microsoft Security products. Today, Microsoft Security Store is generally available—with three major enhancements: Embedded where you work: Security Store is now built into Microsoft Defender, featuring SOC-focused agents, and into Microsoft Entra for Verified ID and External ID scenarios like fraud protection. By bringing these capabilities into familiar workflows, organizations can combine Microsoft and partner innovation to strengthen security operations and outcomes. Expanded catalog: Security Store now offers more than 100 third-party solutions, including advanced fraud prevention, forensic analysis, and threat intelligence agents. Security services available: Partners can now list and sell services such as managed detection and response and threat hunting directly through Security Store. Real-World Impact: What We Learned in Public Preview Thousands of customers explored Microsoft Security Store and tried a growing catalog of agents and SaaS solutions. While we are at the beginning of our journey, customer feedback shows these solutions are helping teams apply AI to improve security operations and reduce manual effort. Spairliners, a cloud-first aviation services joint venture between Air France and Lufthansa, strengthened identity and access controls by deploying Glueckkanja’s Privileged Admin Watchdog to enforce just-in-time access. “Using the Security Store felt easy, like adding an app in Entra. For a small team, being able to find and deploy security innovations in minutes is huge.” – Jonathan Mayer, Head of Innovation, Data and Quality GTD, a Chilean technology and telecommunications company, is testing a variety of agents from the Security Store: “As any security team, we’re always looking for ways to automate and simplify our operations. We are exploring and applying the world of agents more and more each day so having the Security Store is convenient—it’s easy to find and deploy agents. We’re excited about the possibilities for further automation and integrations into our workflows, like event-triggered agents, deeper Outlook integration, and more." – Jonathan Lopez Saez, Cybersecurity Architect Partners echoed the momentum they are seeing with the Security Store: “We’re excited by the early momentum with Security Store. We’ve already received multiple new leads since going live, including one in a new market for us, and we have multiple large deals we’re looking to drive through Security Store this quarter.” - Kim Brault, Head of Alliances, Delinea “Partnering with Microsoft through the Security Store has unlocked new ways to reach enterprise customers at scale. The store is pivotal as the industry shifts toward AI, enabling us to monetize agents without building our own billing infrastructure. With the new embedded experience, our solutions appear at the exact moment customers are looking to solve real problems. And by working with Microsoft’s vetting process, we help provide customers confidence to adopt AI agents” – Milan Patel, Co-founder and CEO, BlueVoyant “Agents and the Microsoft Security Store represent a major step forward in bringing AI into security operations. We’ve turned years of service experience into agentic automations, and it’s resonating with customers—we’ve been positively surprised by how quickly they’re adopting these solutions and embedding our automated agentic expertise into their workflows.” – Christian Kanja, Founder and CEO of glueckkanja New at GA: Embedded in Defender, Entra—Security Solutions right where you work Microsoft Security Store is now embedded in the Defender and Entra portals with partner solutions that extend your Microsoft Security products. By placing Security Store in front of security practitioners, it’s now easier than ever to use the best of partner and Microsoft capabilities in combination to drive stronger security outcomes. As Dorothy Li, Corporate Vice President of Security Copilot and Ecosystem put it, “Embedding the Security Store in our core security products is about giving customers access to innovative solutions that tap into the expertise of our partners. These solutions integrate with Microsoft Security products to complete end-to-end workflows, helping customers improve their security” Within the Microsoft Defender portal, SOC teams can now discover Copilot agents from both Microsoft and partners in the embedded Security Store, and run them all from a single, familiar interface. Let’s look at an example of how these agents might help in the day of the life of a SOC analyst. The day starts with Watchtower (BlueVoyant) confirming Sentinel connectors and Defender sensors are healthy, so investigations begin with full visibility. As alerts arrive, the Microsoft Defender Copilot Alert Triage Agent groups related signals, extracts key evidence, and proposes next steps; identity related cases are then validated with Login Investigator (adaQuest), which baselines recent sign-in behavior and device posture to cut false positives. To stay ahead of emerging campaigns, the analyst checks the Microsoft Threat Intelligence Briefing Agent for concise threat rundowns tied to relevant indicators, informing hunts and temporary hardening. When HR flags an offboarding, GuardianIQ (People Tech Group) correlates activity across Entra ID, email, and files to surface possible data exfiltration with evidence and risk scores. After containment, Automated Closing Comment Generator (Ascent Global Inc.) produces clear, consistent closure notes from Defender incident details, keeping documentation tight without hours of writing. Together, these Microsoft and partner agents maintain platform health, accelerate triage, sharpen identity decisions, add timely threat context, reduce insider risk blind spots, and standardize reporting—all inside the Defender portal. You can read more about the new agents available in the Defender portal in this blog. In addition, Security Store is now integrated into Microsoft Entra, focused on identity-centric solutions. Identity admins can discover and activate partner offerings for DDoS protection, intelligent bot defense, and government ID–based verification for account recovery —all within the Entra portal. With these capabilities, Microsoft Entra delivers a seamless, multi-layered defense that combines built-in identity protection with best-in-class partner technologies, making it easier than ever for enterprises to strengthen resilience against modern identity threats. Learn more here. Levent Besik, VP of Microsoft Entra, shared that “This sets a new benchmark for identity security and partner innovation at Microsoft. Attacks on digital identities can come from anywhere. True security comes from defense in depth, layering protection across the entire user journey so every interaction, from the first request to identity recovery, stays secure. This launch marks only the beginning; we will continue to introduce additional layers of protection to safeguard every aspect of the identity journey” New at GA: Services Added to a Growing Catalog of Agents and SaaS For the first time, partners can offer their security services directly through the Security Store. Customers can now find, buy, and activate managed detection and response, threat hunting, and other expert services—making it easier to augment internal teams and scale security operations. Every listing has a MXDR Verification that certifies they are providing next generation advanced threat detection and response services. You can browse all the services available at launch here, and read about some of our exciting partners below: Avanade is proud to be a launch partner for professional services in the Microsoft Security Store. As a leading global Microsoft Security Services provider, we’re excited to make our offerings easier to find and help clients strengthen cyber defenses faster through this streamlined platform - Jason Revill, Avanade Global Security Technology Lead ProServeIT partnering with Microsoft to have our offers in the Microsoft Security Store helps ProServeIT protect our joint customers and allows us to sell better with Microsoft sellers. It shows customers how our technology and services support each other to create a safe and secure platform - Eric Sugar, President Having Reply’s security services showcased in the Microsoft Security Store is a significant milestone for us. It amplifies our ability to reach customers at the exact point where they evaluate and activate Microsoft security solutions, ensuring our offerings are visible alongside Microsoft’s trusted technologies. Notable New Selections Since public preview, the Security Store catalog has grown significantly. Customers can now choose from over 100 third-party solutions, including 60+ SaaS offerings and 50+ Security Copilot agents, with new additions every week. Recent highlights include Cisco Duo and Rubrik: Cisco Duo IAM delivers comprehensive, AI-driven identity protection combining MFA, SSO, passwordless and unified directory management. Duo IAM seamlessly integrates across the Microsoft Security suite—enhancing Entra ID with risk-based authentication and unified access policy management across cloud and on-premises applications seamlessly in just a few clicks. Intune for device compliance and access enforcement. Sentinel for centralized security monitoring and threat detection through critical log ingestion about authentication events, administrator actions, and risk-based alerts, providing real-time visibility across the identity stack. Rubrik's data security platform delivers complete cyber resilience across enterprise, cloud, and SaaS alongside Microsoft. Through the Microsoft Sentinel integration, Rubrik’s data management capabilities are combined with Sentinel’s security analytics to accelerate issue resolution, enabling unified visibility and streamlined responses. Furthermore, Rubrik empowers organizations to reduce identity risk and ensure operational continuity with real-time protection, unified visibility and rapid recovery across Microsoft Active Directory and Entra ID infrastructure. The Road Ahead This is just the beginning. Microsoft Security Store will continue to make it even easier for customers to improve their security outcomes by tapping into the innovation and expertise of our growing partner ecosystem. The momentum we’re seeing is clear—customers are already gaining real efficiencies and stronger outcomes by adopting AI-powered agents. As we work together with partners, we’ll unlock even more automation, deeper integrations, and new capabilities that help security teams move faster and respond smarter. Explore the Security Store today to see what’s possible. For a more detailed walk-through of the capabilities, read our previous public preview Tech Community post If you’re a partner, now is the time to list your solutions and join us in shaping the future of security.Cybersecurity: What Every Business Leader Needs to Know Now
As a Senior Cybersecurity Solution Architect, I’ve had the privilege of supporting organisations across the United Kingdom, Europe, and the United States—spanning sectors from finance to healthcare—in strengthening their security posture. One thing has become abundantly clear: cybersecurity is no longer the sole domain of IT departments. It is a strategic imperative that demands attention at board-level. This guide distils five key lessons drawn from real-world engagements to help executive leaders navigate today’s evolving threat landscape. These insights are not merely technical—they are cultural, operational, and strategic. If you’re a C-level executive, this article is a call to action: reassess how your organisation approaches cybersecurity before the next breach forces the conversation. In this article, I share five lessons (and quotes) from the field that help demystify how to enhance an organisation’s security posture. 1. Shift the Mindset “This has always been our approach, and we’ve never experienced a breach—so why should we change it?” A significant barrier to effective cybersecurity lies not in the sophistication of attackers, but in the predictability of human behaviour. If you’ve never experienced a breach, it’s tempting to maintain the status quo. However, as threats evolve, so too must your defences. Many cyber threats exploit well-known vulnerabilities that remain unpatched or rely on individuals performing routine tasks in familiar ways. Human nature tends to favour comfort and habit—traits that adversaries are adept at exploiting. Unlike many organisations, attackers readily adopt new technologies to advance their objectives, including AI-powered ransomware to execute increasingly sophisticated attacks. It is therefore imperative to recognise—without delay—that the advent of AI has dramatically reduced both the effort and time required to compromise systems. As the UK’s National Cyber Security Centre (NCSC) has stated: “AI lowers the barrier for novice cyber criminals, hackers-for-hire and hacktivists to carry out effective access and information gathering operations. This enhanced access will likely contribute to the global ransomware threat over the next two years.” Similarly, McKinsey & Company observed: “As AI quickly advances cyber threats, organisations seem to be taking a more cautious approach, balancing the benefits and risks of the new technology while trying to keep pace with attackers’ increasing sophistication.” To counter this evolving threat landscape, organisations must proactively leverage AI in their cyber defence strategies. Examples include: Identity and Access Management (IAM): AI enhances IAM by analysing real-time signals across systems to detect risky sign-ins and enforce adaptive access controls. Example: Microsoft Entra Agents for Conditional Access use AI to automate policy recommendations, streamlining access decisions with minimal manual input. Figure 1: Microsoft Entra Agents Threat Detection: AI accelerates detection, response, and recovery, helping organisations stay ahead of sophisticated threats. Example: Microsoft Defender for Cloud’s AI threat protection identifies prompt injection, data poisoning, and wallet attacks in real time. Incident Response: AI facilitates real-time decision-making, removing emotional bias and accelerating containment and recovery during security incidents. Example: Automatic Attack Disruption in Defender XDR, which can automatically contain a breach in progress. AI Security Posture Management AI workloads require continuous discovery, classification, and protection across multi-cloud environments. Example: Microsoft Defender for Cloud’s AI Security Posture Management secures custom AI apps across Azure, AWS, and GCP by detecting misconfigurations, vulnerabilities, and compliance gaps. Data Security Posture Management (DSPM) for AI AI interactions must be governed to ensure privacy, compliance, and insider risk mitigation. Example: Microsoft Purview DSPM for AI enables prompt auditing, applies Data Loss Prevention (DLP) policies to third-party AI apps like ChatGPT, and supports eDiscovery and lifecycle management. AI Threat Protection Organisations must address emerging AI threat vectors, including prompt injection, data leakage, and model exploitation. Example: Defender for AI (private preview) provides model-level security, including governance, anomaly detection, and lifecycle protection. Embracing innovation, automation, and intelligent defence is the secret sauce for cyber resilience in 2026. 2. Avoid One-Off Purchases – Invest with a Strategy “One MDE and one Sentinel to go, please.” Organisations often approach me intending to purchase a specific cybersecurity product—such as Microsoft Defender for Endpoint (MDE)—without a clearly articulated strategic rationale. My immediate question is: what is the broader objective behind this purchase? Is it driven by perceived value or popularity, or does it form part of a well-considered strategy to enhance endpoint security? Cybersecurity investments should be guided by a long-term, holistic strategy that spans multiple years and is periodically reassessed to reflect evolving threats. Strengthening endpoint protection must be integrated into a wider effort to improve the organisation’s overall security posture. This includes ensuring seamless integration between security solutions and avoiding operational silos. For example, deploying robust endpoint protection is of limited value if identities are not safeguarded with multi-factor authentication (MFA), or if storage accounts remain publicly accessible. A cohesive and forward-looking approach ensures that all components of the security architecture work in concert to mitigate risk effectively. Security Adoption Journey (Based on Zero Trust Framework) Assess – Evaluate the threat landscape, attack surface, vulnerabilities, compliance obligations, and critical assets. Align – Link security objectives to broader business goals to ensure strategic coherence. Architect – Design integrated and scalable security solutions, addressing gaps and eliminating operational silos. Activate – Implement tools with robust governance and automation to ensure consistent policy enforcement. Advance – Continuously monitor, test, and refine the security posture to stay ahead of evolving threats. Security tools are not fast food—they work best as part of a long-term plan, not a one-off order. This piecemeal approach runs counter to the modern Zero Trust security model, which assumes no single tool will prevent every breach and instead implements layered defences and integration. 3. Legacy Systems Are Holding You Back “Unfortunately, we are unable to implement phishing-resistant MFA, as our legacy app does not support integration with the required protocols.” A common challenge faced by many organisations I have worked with is the constraint on innovation within their cybersecurity architecture, primarily due to continued reliance on legacy applications—often driven by budgetary or operational necessity. These outdated systems frequently lack compatibility with modern security technologies and may introduce significant vulnerabilities. A notable example is the deployment of phishing-resistant multi-factor authentication (MFA)—such as FIDO2 security keys or certificate-based authentication—which requires advanced identity protocols and conditional access policies. These capabilities are available exclusively through Microsoft Entra ID. To address this issue effectively, it is essential to design security frameworks based on the organisation’s future aspirations rather than its current limitations. By adopting a forward-thinking approach, organisations can remain receptive to emerging technologies that align with their strategic cybersecurity objectives. Moreover, this perspective encourages investment in acquiring the necessary talent, thereby reducing reliance on extensive change management and staff retraining. I advise designing for where you want to be in the next 1–3 years—ideally cloud-first and identity-driven—essentially adopting a Zero Trust architecture, rather than being constrained by the limitations of legacy systems. 4. Collaboration Is a Security Imperative “This item will need to be added to the dev team's backlog. Given their current workload, they will do their best to implement GitHub Security in Q3, subject to capacity.” Cybersecurity threats may originate from various parts of an organisation, and one of the principal challenges many face is the fragmented nature of their defence strategies. To effectively mitigate such risks, cybersecurity must be embedded across all departments and functions, rather than being confined to a single team or role. In many organisations, the Chief Information Security Officer (CISO) operates in isolation from other C-level executives, which can limit their influence and complicate the implementation of security measures across the enterprise. Furthermore, some teams may lack the requisite expertise to execute essential security practices. For instance, an R&D lead responsible for managing developers may not possess the necessary skills in DevSecOps. To address these challenges, it is vital to ensure that the CISO is empowered to act without political or organisational barriers and is supported in implementing security measures across all business units. When the CISO has backing from the COO and HR, initiatives such as MFA rollout happen faster and more thoroughly. Cross-Functional Security Responsibilities Role Security Responsibilities R&D - Adopt DevSecOps practices - Identify vulnerabilities early - Manage code dependencies - Detect exposed secrets - Embed security in CI/CD pipelines CIO - Ensure visibility over organizational data - Implement Data Loss Prevention (DLP) - Safeguard sensitive data lifecycle - Ensure regulatory compliance CTO - Secure cloud environments (CSPM) - Manage SaaS security posture (SSPM) - Ensure hardware and endpoint protection COO - Protect digital assets - Secure domain management - Mitigate impersonation threats - Safeguard digital marketing channels and customer PII Support & Vendors - Deliver targeted training - Prevent social engineering attacks - Improve awareness of threat vectors HR - Train employees on AI-related threats - Manage insider risks - Secure employee data - Oversee cybersecurity across the employee lifecycle Empowering the CISO to act across departments helps organisations shift towards a security-first culture—embedding cybersecurity into every function, not just IT. 5. Compliance Is Not Security “We’re compliant, so we must be secure.” Many organisations mistakenly equate passing audits—such as ISO 27001 or SOC 2—with being secure. While compliance frameworks help establish a baseline for security, they are not a guarantee of protection. Determined attackers are not deterred by audit checklists; they exploit gaps, misconfigurations, and human error regardless of whether an organisation is certified. Moreover, due to the rapidly evolving nature of the cyber threat landscape, compliance frameworks often struggle to keep pace. By the time a standard is updated, attackers may already be exploiting new techniques that fall outside its scope. This lag creates a false sense of security for organisations that rely solely on regulatory checkboxes. Security is a continuous risk management process—not a one-time certification. It must be embedded into every layer of the enterprise and treated with the same urgency as other core business priorities. Compliance may be the starting line, not the finish line. Effective security goes beyond meeting regulatory requirements—it demands ongoing vigilance, adaptability, and a proactive mindset. Conclusion: Cybersecurity Is a Continuous Discipline Cybersecurity is not a destination—it is a continuous journey. By embracing strategic thinking, cross-functional collaboration, and emerging technologies, organisations can build resilience against today’s threats and tomorrow’s unknowns. The lessons shared throughout this article are not merely technical—they are cultural, operational, and strategic. If there is one key takeaway, it is this: avoid piecemeal fixes and instead adopt an integrated, future-ready security strategy. Due to the rapidly evolving nature of the cyber threat landscape, compliance frameworks alone cannot keep pace. Security must be treated as a dynamic, ongoing process—one that is embedded into every layer of the enterprise and reviewed regularly. Organisations should conduct periodic security posture reviews, leveraging tools such as Microsoft Secure Score or monthly risk reports, and stay informed about emerging threats through threat intelligence feeds and resources like the Microsoft Digital Defence Report, CISA (Cybersecurity and Infrastructure Security Agency), NCSC (UK National Cyber Security Centre), and other open-source intelligence platforms. As Ann Johnson aptly stated in her blog: “The most prepared organisations are those that keep asking the right questions and refining their approach together.” Cyber resilience demands ongoing investment—in people (through training and simulation drills), in processes (via playbooks and frameworks), and in technology (through updates and adoption of AI-driven defences). To reduce cybersecurity risk over time, resilient organisations must continually refine their approach and treat cybersecurity as an ongoing discipline. The time to act is now. Resources: https://www.ncsc.gov.uk/report/impact-of-ai-on-cyber-threat Defend against cyber threats with AI solutions from Microsoft - Microsoft Industry Blogs Generative AI Cybersecurity Solutions | Microsoft Security Require phishing-resistant multifactor authentication for Microsoft Entra administrator roles - Microsoft Entra ID | Microsoft Learn AI is the greatest threat—and defense—in cybersecurity today. Here’s why. Microsoft Entra Agents - Microsoft Entra | Microsoft Learn Smarter identity security starts with AI https://www.microsoft.com/en-us/security/blog/2025/06/12/cyber-resilience-begins-before-the-crisis/ https://www.microsoft.com/en-us/security/security-insider/threat-landscape/microsoft-digital-defense-report-2023-critical-cybersecurity-challenges https://www.microsoft.com/en-us/security/blog/2025/06/12/cyber-resilience-begins-before-the-crisis/Hacking Made Easy, Patching Made Optional: A Modern Cyber Tragedy
In today’s cyber threat landscape, the tools and techniques required to compromise enterprise environments are no longer confined to highly skilled adversaries or state-sponsored actors. While artificial intelligence is increasingly being used to enhance the sophistication of attacks, the majority of breaches still rely on simple, publicly accessible tools and well-established social engineering tactics. Another major issue is the persistent failure of enterprises to patch common vulnerabilities in a timely manner—despite the availability of fixes and public warnings. This negligence continues to be a key enabler of large-scale breaches, as demonstrated in several recent incidents. The Rise of AI-Enhanced Attacks Attackers are now leveraging AI to increase the credibility and effectiveness of their campaigns. One notable example is the use of deepfake technology—synthetic media generated using AI—to impersonate individuals in video or voice calls. North Korean threat actors, for instance, have been observed using deepfake videos and AI-generated personas to conduct fraudulent job interviews with HR departments at Western technology companies. These scams are designed to gain insider access to corporate systems or to exfiltrate sensitive intellectual property under the guise of legitimate employment. Social Engineering: Still the Most Effective Entry Point And yet, many recent breaches have begun with classic social engineering techniques. In the cases of Coinbase and Marks & Spencer, attackers impersonated employees through phishing or fraudulent communications. Once they had gathered sufficient personal information, they contacted support desks or mobile carriers, convincingly posing as the victims to request password resets or SIM swaps. This impersonation enabled attackers to bypass authentication controls and gain initial access to sensitive systems, which they then leveraged to escalate privileges and move laterally within the network. Threat groups such as Scattered Spider have demonstrated mastery of these techniques, often combining phishing with SIM swap attacks and MFA bypass to infiltrate telecom and cloud infrastructure. Similarly, Solt Thypoon (formerly DEV-0343), linked to North Korean operations, has used AI-generated personas and deepfake content to conduct fraudulent job interviews—gaining insider access under the guise of legitimate employment. These examples underscore the evolving sophistication of social engineering and the need for robust identity verification protocols. Built for Defense, Used for Breach Despite the emergence of AI-driven threats, many of the most successful attacks continue to rely on simple, freely available tools that require minimal technical expertise. These tools are widely used by security professionals for legitimate purposes such as penetration testing, red teaming, and vulnerability assessments. However, they are also routinely abused by attackers to compromise systems Case studies for tools like Nmap, Metasploit, Mimikatz, BloodHound, Cobalt Strike, etc. The dual-use nature of these tools underscores the importance of not only detecting their presence but also understanding the context in which they are being used. From CVE to Compromise While social engineering remains a common entry point, many breaches are ultimately enabled by known vulnerabilities that remain unpatched for extended periods. For example, the MOVEit Transfer vulnerability (CVE-2023-34362) was exploited by the Cl0p ransomware group to compromise hundreds of organizations, despite a patch being available. Similarly, the OpenMetadata vulnerability (CVE-2024-28255, CVE-2024-28847) allowed attackers to gain access to Kubernetes workloads and leverage them for cryptomining activity days after a fix had been issued. Advanced persistent threat groups such as APT29 (also known as Cozy Bear) have historically exploited unpatched systems to maintain long-term access and conduct stealthy operations. Their use of credential harvesting tools like Mimikatz and lateral movement frameworks such as Cobalt Strike highlights the critical importance of timely patch management—not just for ransomware defense, but also for countering nation-state actors. Recommendations To reduce the risk of enterprise breaches stemming from tool misuse, social engineering, and unpatched vulnerabilities, organizations should adopt the following practices: 1. Patch Promptly and Systematically Ensure that software updates and security patches are applied in a timely and consistent manner. This involves automating patch management processes to reduce human error and delay, while prioritizing vulnerabilities based on their exploitability and exposure. Microsoft Intune can be used to enforce update policies across devices, while Windows Autopatch simplifies the deployment of updates for Windows and Microsoft 365 applications. To identify and rank vulnerabilities, Microsoft Defender Vulnerability Management offers risk-based insights that help focus remediation efforts where they matter most. 2. Implement Multi-Factor Authentication (MFA) To mitigate credential-based attacks, MFA should be enforced across all user accounts. Conditional access policies should be configured to adapt authentication requirements based on contextual risk factors such as user behavior, device health, and location. Microsoft Entra Conditional Access allows for dynamic policy enforcement, while Microsoft Entra ID Protection identifies and responds to risky sign-ins. Organizations should also adopt phishing-resistant MFA methods, including FIDO2 security keys and certificate-based authentication, to further reduce exposure. 3. Identity Protection Access Reviews and Least Privilege Enforcement Conducting regular access reviews ensures that users retain only the permissions necessary for their roles. Applying least privilege principles and adopting Microsoft Zero Trust Architecture limits the potential for lateral movement in the event of a compromise. Microsoft Entra Access Reviews automates these processes, while Privileged Identity Management (PIM) provides just-in-time access and approval workflows for elevated roles. Just-in-Time Access and Risk-Based Controls Standing privileges should be minimized to reduce the attack surface. Risk-based conditional access policies can block high-risk sign-ins and enforce additional verification steps. Microsoft Entra ID Protection identifies risky behaviors and applies automated controls, while Conditional Access ensures access decisions are based on real-time risk assessments to block or challenge high-risk authentication attempts. Password Hygiene and Secure Authentication Promoting strong password practices and transitioning to passwordless authentication enhances security and user experience. Microsoft Authenticator supports multi-factor and passwordless sign-ins, while Windows Hello for Business enables biometric authentication using secure hardware-backed credentials. 4. Deploy SIEM and XDR for Detection and Response A robust detection and response capability is vital for identifying and mitigating threats across endpoints, identities, and cloud environments. Microsoft Sentinel serves as a cloud-native SIEM that aggregates and analyses security data, while Microsoft Defender XDR integrates signals from multiple sources to provide a unified view of threats and automate response actions. 5. Map and Harden Attack Paths Organizations should regularly assess their environments for attack paths such as privilege escalation and lateral movement. Tools like Microsoft Defender for Identity help uncover Lateral Movement Paths, while Microsoft Identity Threat Detection and Response (ITDR) integrates identity signals with threat intelligence to automate response. These capabilities are accessible via the Microsoft Defender portal, which includes an attack path analysis feature for prioritizing multicloud risks. 6. Stay Current with Threat Actor TTPs Monitor the evolving tactics, techniques, and procedures (TTPs) employed by sophisticated threat actors. Understanding these behaviours enables organizations to anticipate attacks and strengthen defenses proactively. Microsoft Defender Threat Intelligence provides detailed profiles of threat actors and maps their activities to the MITRE ATT&CK framework. Complementing this, Microsoft Sentinel allows security teams to hunt for these TTPs across enterprise telemetry and correlate signals to detect emerging threats. 7. Build Organizational Awareness Organizations should train staff to identify phishing, impersonation, and deepfake threats. Simulated attacks help improve response readiness and reduce human error. Use Attack Simulation Training, in Microsoft Defender for Office 365 to run realistic phishing scenarios and assess user vulnerability. Additionally, educate users about consent phishing, where attackers trick individuals into granting access to malicious apps. Conclusion The democratization of offensive security tooling, combined with the persistent failure to patch known vulnerabilities, has significantly lowered the barrier to entry for cyber attackers. Organizations must recognize that the tools used against them are often the same ones available to their own security teams. The key to resilience lies not in avoiding these tools, but in mastering them—using them to simulate attacks, identify weaknesses, and build a proactive defense. Cybersecurity is no longer a matter of if, but when. The question is: will you detect the attacker before they achieve their objective? Will you be able to stop them before reaching your most sensitive data? Additional read: Gartner Predicts 30% of Enterprises Will Consider Identity Verification and Authentication Solutions Unreliable in Isolation Due to AI-Generated Deepfakes by 2026 Cyber security breaches survey 2025 - GOV.UK Jasper Sleet: North Korean remote IT workers’ evolving tactics to infiltrate organizations | Microsoft Security Blog MOVEit Transfer vulnerability Solt Thypoon Scattered Spider SIM swaps Attackers exploiting new critical OpenMetadata vulnerabilities on Kubernetes clusters | Microsoft Security Blog Microsoft Defender Vulnerability Management - Microsoft Defender Vulnerability Management | Microsoft Learn Zero Trust Architecture | NIST tactics, techniques, and procedures (TTP) - Glossary | CSRC https://learn.microsoft.com/en-us/security/zero-trust/deploy/overview
