Ignite your future with new security skills during Microsoft Ignite 2025
Ignite your future with new security skills during Microsoft Ignite 2025 AI and cloud technologies are reshaping every industry. Organizations need professionals who can secure AI solutions, modernize infrastructure, and drive innovation responsibly. Ignite brings together experts, learning, and credentials to help you get skilled for the future. Take on the Secure and Govern AI with Confidence Challenge Start your journey with the Azure Skilling Microsoft Challenge. These curated challenges help you practice real-world scenarios and earn recognition for your skills. One of the challenges featured is the Secure and Govern AI with Confidence challenge. This challenge helps you: Implement AI governance frameworks. Configure responsible AI guardrails in Azure AI Foundry. Apply security best practices for AI workloads. Special Offer: Be among the first 5,000 participants to complete this challenge and receive a discounted certification exam voucher—a perfect way to validate your skills and accelerate your career. Completing this challenge earns you a badge and prepares you for advanced credentials—ideal for anyone looking to lead in AI security. Join the challenge today! Validate Your Expertise with this new Microsoft Applied Skill. Applied Skills assessments are scenario-based, so you demonstrate practical expertise—not just theory. Earn the Secure AI Solutions in the Cloud credential—a job-ready validation of your ability to: Configure security for AI services using Microsoft Defender for Cloud. Implement governance and guardrails in Azure AI Foundry. Protect sensitive data and ensure compliance across AI workloads. This applied skill is designed for professionals who want to lead in AI security, accelerate career growth, and stand out in a competitive market. To learn how to prepare and take the applied skill, visit here. Your Next Steps: Security Plans Ignite isn’t just about live sessions—it’s about giving you on-demand digital content and curated learning paths so you can keep building skills long after the event ends. With 15 curated security plans that discuss topics such as controlling access with Microsoft Entra and securing your organization’s data, find what is relevant to you on Microsoft Ignite: Keep the momentum going page.
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-2025-55325Unlocking Developer Innovation with Microsoft Sentinel data lake
Introduction Microsoft Sentinel is evolving rapidly, transforming to be both an industry-leading SIEM and an AI-ready platform that empowers agentic defense across the security ecosystem. In our recent webinar: Introduction to Sentinel data lake for Developers, we explored how developers can leverage Sentinel’s unified data lake, extensible architecture, and integrated tools to build innovative security solutions. This post summarizes the key takeaways and actionable insights for developers looking to harness the full power of Sentinel. The Sentinel Platform: A Foundation for Agentic Security Unified Data and Context Sentinel centralizes security data cost-effectively, supporting massive volumes and diverse data types. This unified approach enables advanced analytics, graph-enabled context, and AI-ready data access—all essential for modern security operations. Developers can visualize relationships across assets, activities, and threats, mapping incidents and hunting scenarios with unprecedented clarity. Extensible and Open Platform Sentinel’s open architecture simplifies onboarding and data integration. Out-of-the-box connectors and codeless connector creation make it easy to bring in third-party data. Developers can quickly package and publish agents that leverage the centralized data lake and MCP server, distributing solutions through Microsoft Security Store for maximum reach. The Microsoft Security Store is a storefront for security professionals to discover, buy, and deploy vetted security SaaS solutions and AI agents from our ecosystem partners. These offerings integrate natively with Microsoft Security products—including the Sentinel platform, Defender, and Entra, to deliver end‑to‑end protection. By combining curated, deploy‑ready solutions with intelligent, AI‑assisted workflows, the Store reduces integration friction and speeds time‑to‑value for critical tasks like triage, threat hunting, and access management. Advanced Analytics and AI Integration With support for KQL, Spark, and ML tools, Sentinel separates storage and compute, enabling scalable analytics and semantic search. Jupyter Notebooks hosted in on-demand Spark environments allow for rich data engineering and machine learning directly on the data lake. Security Copilot agents, seamlessly integrated with Sentinel, deliver autonomous and adaptive automation, enhancing both security and IT operations. Developer Scenarios: Unlocking New Possibilities The webinar showcased several developer scenarios enabled by Sentinel’s platform components: Threat Investigations Over Extended Timelines: Query historical data to uncover slow-moving attacks and persistent threats. Behavioral Baselining: Model normal behavior using months of sign-in logs to detect anomalies. Alert Enrichment: Correlate alerts with firewall and NetFlow data to improve accuracy and reduce false positives. Retrospective Threat Hunting: React to new indicators of compromise by running historical queries across the data lake. ML-Powered Insights: Build machine learning models for anomaly detection, alert enrichment, and predictive analytics. These scenarios demonstrate how developers can leverage Sentinel’s data lake, graph capabilities, and integrated analytics to deliver powerful security solutions. End-to-End Developer Journey The following steps outline a potential workflow for developers to ingest and analyze their data within the Sentinel platform. Data Sources: Identify high-value data sources from your environment to integrate with Microsoft Security data. The journey begins with your unique view of the customer’s digital estate. This is data you have in your platform today. Bringing this data into Sentinel helps customers make sense of their entire security landscape at once. Data Ingestion: Import third-party data into the Sentinel data lake for secure, scalable analytics. As customer data flows from various platforms into Sentinel, it is centralized and normalized, providing a unified foundation for advanced analysis and threat detection across the customer’s digital environment. Sentinel data lake and Graph: Run Jupyter Notebook jobs for deep insights, combining contributed and first-party data. Once data resides in the Sentinel data lake, developers can leverage its graph capabilities to model relationships and uncover patterns, empowering customers with comprehensive insights into security events and trends. Agent Creation: Build Security Copilot agents that interact with Sentinel data using natural language prompts. These agents make the customer’s ingested data actionable, allowing users to ask questions or automate tasks, and helping teams quickly respond to threats or investigate incidents using their own enterprise data. Solution Packaging: Package and distribute solutions via the Microsoft Security Store, reaching customers at scale. By packaging these solutions, developers enable customers to seamlessly deploy advanced analytics and automation tools that harness their data journey— from ingestion to actionable insights—across their entire security estate. Conclusion Microsoft Sentinel’s data lake and platform capabilities open new horizons for developers. By centralizing data, enabling advanced analytics, and providing extensible tools, Sentinel empowers you to build solutions that address today’s security challenges and anticipate tomorrow’s threats. Explore the resources below, join the community, and start innovating with Sentinel today! App Assure: For assistance with developing a Sentinel Codeless Connector Framework (CCF) connector, you can contact AzureSentinelPartner@microsoft.com. Microsoft Security Community: aka.ms/communitychoice Next Steps: Resources and Links Ready to dive deeper? Explore these resources to get started: Get Educated! Sentinel data lake general availability announcement Sentinel data lake official documentation Connect Sentinel to Defender Portal Onboarding to Sentinel data lake Integration scenarios (e.g. hunt | jupyter) KQL queries Jupyter notebooks (link) as jobs (link) VS Code Extension Sentinel graph Sentinel MCP server Security Copilot agents Microsoft Security Store Take Action! Bring your data into Sentinel Build a composite solution Explore Security Copilot agents Publish to Microsoft Security Store List existing SaaS apps in Security StoreGenAI vs Cyber Threats: Why GenAI Powered Unified SecOps Wins
Cybersecurity is evolving faster than ever. Attackers are leveraging automation and AI to scale their operations, so how can defenders keep up? The answer lies in Microsoft Unified Security Operations powered by Generative AI (GenAI). This opens the Cybersecurity Paradox: Attackers only need one successful attempt, but defenders must always be vigilant, otherwise the impact can be huge. Traditional Security Operation Centers (SOCs) are hampered by siloed tools and fragmented data, which slows response and creates vulnerabilities. On average, attackers gain unauthorized access to organizational data in 72 minutes, while traditional defense tools often take on average 258 days to identify and remediate. This is over eight months to detect and resolve breaches, a significant and unsustainable gap. Notably, Microsoft Unified Security Operations, including GenAI-powered capabilities, is also available and supported in Microsoft Government Community Cloud (GCC) and GCC High/DoD environments, ensuring that organizations with the highest compliance and security requirements can benefit from these advanced protections. The Case for Unified Security Operations Unified security operations in Microsoft Defender XDR consolidates SIEM, XDR, Exposure management, and Enterprise Security Posture into a single, integrated experience. This approach allows the following: Breaks down silos by centralizing telemetry across identities, endpoints, SaaS apps, and multi-cloud environments. Infuses AI natively into workflows, enabling faster detection, investigation, and response. Microsoft Sentinel exemplifies this shift with its Data Lake architecture (see my previous post on Microsoft Sentinel’s New Data Lake: Cut Costs & Boost Threat Detection), offering schema-on-read flexibility for petabyte-scale analytics without costly data rehydration. This means defenders can query massive datasets in real time, accelerating threat hunting and forensic analysis. GenAI: A Force Multiplier for Cyber Defense Generative AI transforms security operations from reactive to proactive. Here’s how: Threat Hunting & Incident Response GenAI enables predictive analytics and anomaly detection across hybrid identities, endpoints, and workloads. It doesn’t just find threats—it anticipates them. Behavioral Analytics with UEBA Advanced User and Entity Behavior Analytics (UEBA) powered by AI correlates signals from multi-cloud environments and identity providers like Okta, delivering actionable insights for insider risk and compromised accounts. [13 -Micros...s new UEBA | Word] Automation at Scale AI-driven playbooks streamline repetitive tasks, reducing manual workload and accelerating remediation. This frees analysts to focus on strategic threat hunting. Microsoft Innovations Driving This Shift For SOC teams and cybersecurity practitioners, these innovations mean you spend less time on manual investigations and more time leveraging actionable insights, ultimately boosting productivity and allowing you to focus on higher-value security work that matters most to your organization. Plus, by making threat detection and response faster and more accurate, you can reduce stress, minimize risk, and demonstrate greater value to your stakeholders. Sentinel Data Lake: Unlocks real-time analytics at scale, enabling AI-driven threat detection without rehydration costs. Microsoft Sentinel data lake overview UEBA Enhancements: Multi-cloud and identity integrations for unified risk visibility. Sentinel UEBA’s Superpower: Actionable Insights You Can Use! Now with Okta and Multi-Cloud Logs! Security Copilot & Agentic AI: Harnesses AI and global threat intelligence to automate detection, response, and compliance across the security stack, enabling teams to scale operations and strengthen Zero Trust defenses defenders. Security Copilot Agents: The New Era of AI, Driven Cyber Defense Sector-Specific Impact All sectors are different, but I would like to focus a bit on the public sector at this time. This sector and critical infrastructure organizations face unique challenges: talent shortages, operational complexity, and nation-state threats. GenAI-centric platforms help these sectors shift from reactive defense to predictive resilience, ensuring mission-critical systems remain secure. By leveraging advanced AI-driven analytics and automation, public sector organizations can streamline incident detection, accelerate response times, and proactively uncover hidden risks before they escalate. With unified platforms that bridge data silos and integrate identity, endpoint, and cloud telemetry, these entities gain a holistic security posture that supports compliance and operational continuity. Ultimately, embracing generative AI not only helps defend against sophisticated cyber adversaries but also empowers public sector teams to confidently protect the services and infrastructure their communities rely on every day. Call to Action Artificial intelligence is driving unified cybersecurity. Solutions like Microsoft Defender XDR and Sentinel now integrate into a single dashboard, consolidating alerts, incidents, and data from multiple sources. AI swiftly correlates information, prioritizes threats, and automates investigations, helping security teams respond quickly with less manual work. This shift enables organizations to proactively manage cyber risks and strengthen their resilience against evolving challenges. Picture a single pane of glass where all your XDRs and Defenders converge, AI instantly shifts through the noise, highlighting what matters most so teams can act with clarity and speed. That may include: Assess your SOC maturity and identify silos. Use the Security Operations Self-Assessment Tool to determine your SOC’s maturity level and provide actionable recommendations for improving processes and tooling. Also see Security Maturity Model from the Well-Architected Framework Explore Microsoft Sentinel, Defender XDR, and Security Copilot for AI-powered security. Explains progressive security maturity levels and strategies for strengthening your security posture. What is Microsoft Defender XDR? - Microsoft Defender XDR and What is Microsoft Security Copilot? Design Security in Solutions from Day One! Drive embedding security from the start of solution design through secure-by-default configurations and proactive operations, aligning with Zero Trust and MCRA principles to build resilient, compliant, and scalable systems. Design Security in Solutions from Day One! Innovate boldly, Deploy Safely, and Never Regret it! Upskill your teams on GenAI tools and responsible AI practices. Guidance for securing AI apps and data, aligned with Zero Trust principles Build a strong security posture for AI About the Author: Hello Jacques "Jack” here! I am a Microsoft Technical Trainer focused on helping organizations use advanced security and AI solutions. I create and deliver training programs that combine technical expertise with practical use, enabling teams to adopt innovations like Microsoft Sentinel, Defender XDR, and Security Copilot for stronger cyber resilience. #SkilledByMTT #MicrosoftLearnBlog Series: Securing the Future: Protecting AI Workloads in the Enterprise
Post 1: The Hidden Threats in the AI Supply Chain Your AI Supply Chain Is Under Attack — And You Might Not Even Know It Imagine deploying a cutting-edge AI model that delivers flawless predictions in testing. The system performs beautifully, adoption soars, and your data science team celebrates. Then, a few weeks later, you discover the model has been quietly exfiltrating sensitive data — or worse, that a single poisoned dataset altered its decision-making from the start. This isn’t a grim sci-fi scenario. It’s a growing reality. AI systems today rely on a complex and largely opaque supply chain — one built on shared models, open-source frameworks, third-party datasets, and cloud-based APIs. Each link in that chain represents both innovation and vulnerability. And unless organizations start treating the AI supply chain as a security-critical system, they risk building intelligence on a foundation they can’t fully trust. Understanding the AI Supply Chain Much like traditional software, modern AI models rarely start from scratch. Developers and data scientists leverage a mix of external assets to accelerate innovation — pretrained models from public repositories like Hugging Face (https://huggingface.co/), data from external vendors, third-party labeling services, and open-source ML libraries. Each of these layers forms part of your AI supply chain — the ecosystem of components that power your model’s lifecycle, from data ingestion to deployment. In many cases, organizations don’t fully know: Where their datasets originated. Whether the pretrained model they fine-tuned was modified or backdoored. If the frameworks powering their pipeline contain known vulnerabilities. AI’s strength — its openness and speed of adoption — is also its greatest weakness. You can’t secure what you don’t see, and most teams have very little visibility into the origins of their AI assets. The New Threat Landscape Attackers have taken notice. As enterprises race to operationalize AI, threat actors are shifting their attention from traditional IT systems to the AI layer itself — particularly the model and data supply chain. Common attack vectors now include: Data poisoning: Injecting subtle malicious samples into training data to bias or manipulate model behavior. Model backdoors: Embedding hidden triggers in pretrained models that can be activated later. Dependency exploits: Compromising widely used ML libraries or open-source repositories. Model theft and leakage: Extracting proprietary weights or exploiting exposed inference APIs. These attacks are often invisible until after deployment, when the damage has already been done. In 2024, several research teams demonstrated how tampered open-source LLMs could leak sensitive data or respond with biased or unsafe outputs — all due to poisoned dependencies within the model’s lineage. The pattern is clear: adversaries are no longer only targeting applications; they’re targeting the intelligence that drives them. Why Traditional Security Approaches Fall Short Most organizations already have strong DevSecOps practices for traditional software — automated scanning, dependency tracking, and secure Continuous Integration/Continuous Deployment (CI/CD) pipelines. But those frameworks were never designed for the unique properties of AI systems. Here’s why: Opacity: AI models are often black boxes. Their behavior can change dramatically from minor data shifts, making tampering hard to detect. Lack of origin: Few organizations maintain a verifiable “family tree” of their models and datasets. Limited tooling: Security tools that detect code vulnerabilities don’t yet understand model weights, embeddings, or training lineage. In other words: You can’t patch what you can’t trace. The absence of traceability leaves organizations flying blind — relying on trust where verification should exist. Securing the AI Supply Chain: Emerging Best Practices The good news is that a new generation of frameworks and controls is emerging to bring security discipline to AI development. The following strategies are quickly becoming best practices in leading enterprises: Establish Model Origin and Integrity Maintain a record of where each model originated, who contributed to it, and how it’s been modified. Implement cryptographic signing for model artifacts. Use integrity checks (e.g., hash validation) before deploying any model. Incorporate continuous verification into your MLOps pipeline. This ensures that only trusted, validated models make it to production. Create a Model Bill of Materials (MBOM) Borrowing from software security, an MBOM documents every dataset, dependency, and component that went into building a model — similar to an SBOM for code. Helps identify which datasets and third-party assets were used. Enables rapid response when vulnerabilities are discovered upstream. Organizations like NIST, MITRE, and the Cloud Security Alliance are developing frameworks to make MBOMs a standard part of AI risk management. NIST AI Risk Management Framework (AI RMF) https://www.nist.gov/itl/ai-risk-management-framework NIST AI RMF Playbook https://www.nist.gov/itl/ai-risk-management-framework/nist-ai-rmf-playbook MITRE AI Risk Database (with Robust Intelligence) https://www.mitre.org/news-insights/news-release/mitre-and-robust-intelligence-tackle-ai-supply-chain-risks MITRE’s SAFE-AI Framework https://atlas.mitre.org/pdf-files/SAFEAI_Full_Report.pdf Cloud Security Alliance – AI Model Risk Management Framework https://cloudsecurityalliance.org/artifacts/ai-model-risk-management-framework Secure Your Data Supply Chain The quality and integrity of training data directly shape model behavior. Validate datasets for anomalies, duplicates, or bias. Use data versioning and lineage tracking for full transparency. Where possible, apply differential privacy or watermarking to protect sensitive data. Remember: even small amounts of corrupted data can lead to large downstream risks. Evaluate Third-Party and Open-Source Dependencies Open-source AI tools are powerful — but not always trustworthy. Regularly scan models and libraries for known vulnerabilities. Vet external model vendors and require transparency about their security practices. Treat external ML assets as untrusted code until verified. A simple rule of thumb: if you wouldn’t deploy a third-party software package without security review, don’t deploy a third-party model that way either. The Path Forward: Traceability as the Foundation of AI Trust AI’s transformative potential depends on trust — and trust depends on visibility. Securing your AI supply chain isn’t just about compliance or risk mitigation; it’s about protecting the integrity of the intelligence that drives business decisions, customer interactions, and even national infrastructure. As AI becomes the engine of enterprise innovation, we must bring the same rigor to securing its foundations that we once brought to software itself. Every model has a lineage. Every lineage is a potential attack path. In the next post, we’ll explore how to apply DevSecOps principles to MLOps pipelines — securing the entire AI lifecycle from data collection to deployment. Key Takeaway The AI supply chain is your new attack surface. The only way to defend it is through visibility, origin, and continuous validation — before, during, and after deployment. Contributors Juan José Guirola Sr. (Security GBB for Advanced Identity - Microsoft) References Hugging Face - https://huggingface.co/ Research Gate - https://www.researchgate.net/publication/381514112_Exploiting_Privacy_Vulnerabilities_in_Open_Source_LLMs_Using_Maliciously_Crafted_Prompts/fulltext/66722cb1de777205a338bbba/Exploiting-Privacy-Vulnerabilities-in-Open-Source-LLMs-Using-Maliciously-Crafted-Prompts.pdf NIST AI Risk Management Framework (AI RMF) https://www.nist.gov/itl/ai-risk-management-framework NIST AI RMF Playbook https://www.nist.gov/itl/ai-risk-management-framework/nist-ai-rmf-playbook MITRE AI Risk Database (with Robust Intelligence) https://www.mitre.org/news-insights/news-release/mitre-and-robust-intelligence-tackle-ai-supply-chain-risks MITRE’s SAFE-AI Framework https://atlas.mitre.org/pdf-files/SAFEAI_Full_Report.pdf Cloud Security Alliance – AI Model Risk Management Framework https://cloudsecurityalliance.org/artifacts/ai-model-risk-management-frameworkAzure Integrated HSM: New Chapter&Shift from Centralized Clusters to Embedded Silicon-to-Cloud Trust
Azure Integrated HSM marks a major shift in how cryptographic keys are handled—moving from centralized clusters… to local, tamper‑resistant modules embedded directly in virtual machines. This new model brings cryptographic assurance closer to the workload, reducing latency, increasing throughput, and redefining what’s possible for secure applications in the cloud. Before diving into this innovation, let’s take a step back. Microsoft’s journey with HSMs in Azure spans nearly a decade, evolving through multiple architectures, vendors, and compliance models. From shared services to dedicated clusters, from appliance‑like deployments to embedded chips, each milestone reflects a distinct response to enterprise needs and regulatory expectations. Let’s walk through that progression — not as a single path, but as a layered portfolio that continues to expand. Azure Key Vault Premium, with nCipher nShield Around 2015, Microsoft made Azure Key Vault generally available, and soon after introduced the Premium tier, which integrated nCipher nShield HSMs (previously part of Thales, later acquired by Entrust). This was the first time customers could anchor their most sensitive cryptographic material in FIPS 140‑2 Level 2 validated hardware within Azure. Azure Key Vault Premium is delivered as a fully managed PaaS service, with HSMs deployed and operated by Microsoft in the backend. The service is redundant and highly available, with cryptographic operations exposed through Azure APIs while the underlying HSM infrastructure remains abstracted and secure. This enabled two principal cornerstone scenarios. Based on the Customer Encryption Key (CEK) model, customers could generate and manage encryption keys directly in Azure, always protected by HSMs in the backend. Going further with the Bring Your Own Key (BYOK) model, organizations could generate keys in their own on‑premises HSMs and then securely import and manage them into Azure Key Vault–backed HSMs. These capabilities were rapidly adopted across Microsoft’s second-party services. For example, they underpin the master key management for Azure RMS, later rebranded as Azure Information Protection, and now part of Microsoft Purview Information Protection. These HSM-backed keys can protect the most sensitive data if customers choose to implement the BYOK model through Sensitivity Labels, applying encryption and strict usage controls to protect highly confidential information. Other services like Service Encryption with Customer Key allow customers to encrypt all their data at rest in Microsoft 365 using their own keys, via Data Encryption Policies. This applies to data stored in Exchange, SharePoint, OneDrive, Teams, Copilot, and Purview. This approach also applies to Power Platform, where customer-managed keys can encrypt data stored in Microsoft Dataverse, which underpins services like Power Apps and Power Automate. Beyond productivity services, Key Vault Premium became a building block in hybrid customer architectures: protecting SQL Server Transparent Data Encryption (TDE) keys, storing keys for Azure Storage encryption or Azure Disk Encryption (SSE, ADE, DES), securing SAP workloads running on Azure, or managing TLS certificates for large‑scale web applications. It also supports custom application development and integrations, where cryptographic operations must be anchored in certified hardware — whether for signing, encryption, decryption, or secure key lifecycle management. Around 2020, Azure Key Vault Premium benefit from a shift away from the legacy nCipher‑specific BYOK process. Initially, BYOK in Azure was tightly coupled to nCipher tooling, which limited customers to a single vendor. As the HSM market evolved and customers demanded more flexibility, Microsoft introduced a multi‑vendor BYOK model. This allowed organizations to import keys from a broader set of providers, including Entrust, Thales, and Utimaco, while still ensuring that the keys never left the protection of FIPS‑validated HSMs. This change was significant: it gave customers freedom of choice, reduced dependency on a single vendor, and aligned Azure with the diverse HSM estates that enterprises already operated on‑premises. Azure Key Vault Premium remains a cornerstone of Azure’s data protection offerings. It’s widely used for managing keys, secrets (passwords, connection strings), and certificates. Around February 2024 then with a latest firmware update in April 2025, Microsoft and Marvel has announced the modernization of the Key Vault HSM backend to meet newer standards: Azure’s HSM pool has been updated with Marvell LiquidSecurity adapters that achieved FIPS 140-3 Level 3 certification. This means Key Vault’s underpinnings are being refreshed to the latest security level, though the service interface for customers remains the same. [A tip for Tech guys: you can check the HSM backend provider by looking at the FIPS level in the "hsmPlatform" key attribute]. Key Vault Premium continues to be the go-to solution for many scenarios where a fully managed, cloud-integrated key manager with a shared HSM protection is sufficient. Azure Dedicated HSM, with SafeNet Luna In 2018, Microsoft introduced Azure Dedicated HSM, built on SafeNet Luna hardware (originally Gemalto, later part of Thales). These devices were validated to FIPS 140‑2 Level 3, offering stronger tamper resistance and compliance guarantees. This service provided physically isolated HSM appliances, deployed as single-tenant instances within a customer’s virtual network. By default, these HSMs were non-redundant, unless customers explicitly provisioned multiple units across regions. Each HSM was connected to a private subnet, and the customer retained full administrative control over provisioning, partitioning, and policy enforcement. Unlike Key Vault, using a Dedicated HSM meant the customer had to manage a lot more: HSM user management, key backup (if needed), high availability setup, and any client access configuration. Dedicated HSM was particularly attractive to regulated industries such as finance, healthcare, and government, where compliance frameworks demanded not only FIPS‑validated hardware but also the ability to define their own cryptographic domains and audit processes. Over time, however, Microsoft evolved its HSM portfolio toward more cloud‑native and scalable services. Azure Dedicated HSM is now being retired: Microsoft announced that no new customer onboardings are accepted as of August 2025, and that full support for existing customers will continue until July 31, 2028. Customers are encouraged to plan their transition, as Azure Cloud HSM will succeed Dedicated HSM. Azure Key Vault Managed HSM, with Marvell LiquidSecurity By 2020, it was evident that Azure Key Vault (with shared HSMs) and Dedicated HSM (with single-tenant appliances) represented two ends of a spectrum, and customers wanted something in between: the isolation of a dedicated HSM and the ease-of-use of a managed cloud service. In 2021, Microsoft launched Azure Key Vault Managed HSM, a fully managed, highly available service built on Marvell LiquidSecurity adapters, validated to FIPS 140‑3 Level 3. The key difference with Azure Key Vault Premium lies in the architecture and assurance model. While AKV Premium uses a shared pool of HSMs per Azure geography, organized into region-specific cryptographic domains based on nShield technology — which enforces key isolation through its Security World architecture — Managed HSM provides dedicated HSM instances per customer, ensuring stronger isolation. Also delivered as a PaaS service, it is redundant by design, with built‑in clustering and high availability across availability zones; and fully managed in terms of provisioning, configuration, patching, and maintenance. Managed HSM consists of a cluster of multiple HSM partitions, each based on a separate customer-specific security domain that cryptographically isolates every tenant. Managed HSM supports the same use cases as AKV Premium — CEK, BYOK for Azure RMS or SEwCK, database encryption keys, or any custom integrations — but with higher assurance, stronger isolation, and FIPS 140‑3 Level 3 compliance. Azure Payment HSM, with Thales payShield 10K Introduced in 2022, Azure Payment HSM is a bare-metal, single-tenant service designed specifically for regulated payment workloads. Built on Thales payShield 10K hardware, it meets stringent compliance standards including FIPS 140-2 Level 3 and PCI HSM v3. Whereas Azure Dedicated HSM was built for general-purpose cryptographic workloads (PKI, TLS, custom apps), Payment HSM is purpose-built for financial institutions and payment processors, supporting specialized operations like PIN block encryption, EMV credentialing, and 3D Secure authentication. The service offers low-latency, high-throughput cryptographic operations in a PCI-compliant cloud environment. Customers retain full administrative control and can scale performance from 60 to 2500 CPS, deploying HSMs in high-availability pairs across supported Azure regions. Azure Cloud HSM, with Marvell LiquidSecurity In 2025, Microsoft introduced Azure Cloud HSM, also based on Marvell LiquidSecurity, as a single‑tenant, cloud‑based HSM cluster. These clusters offer a private connectivity and are validated to FIPS 140‑3 Level 3, ensuring the highest level of assurance for cloud‑based HSM services. Azure Cloud HSM is now the recommended successor to Azure Dedicated HSM and gives customers direct administrative authority over their HSMs, while Microsoft handles availability, patching, and maintenance. It is particularly relevant for certificate authorities, payment processors, and organizations that need to operate their own cryptographic infrastructure in the cloud but do not want the burden of managing physical hardware. It combines sovereignty and isolation with the elasticity of cloud operations, making it easier for customers to migrate sensitive workloads without sacrificing control. A single Marvell LiquidSecurity2 adapter can manage up to 100,000 key pairs and perform over one million cryptographic operations per second, making it ideal for high-throughput workloads such as document signing, TLS offloading, and PKI operations. In contrast to Azure Dedicated HSM, Azure Cloud HSM simplifies deployment and management by offering fast provisioning, built-in redundancy, and centralized operations handled by Microsoft. Customers retain full control over their keys while benefiting from secure connectivity via private links and automatic high availability across zones — without the need to manually configure clustering or failover. Azure Integrated HSM, with Microsoft Custom Chips In 2025, Microsoft finally unveiled Azure Integrated HSM, a new paradigm, shifting from a shared cryptographic infrastructure to dedicated, hardware-backed modules integrated at the VM level: custom Microsoft‑designed HSM chips are embedded directly into the host servers of AMD v7 virtual machines. These chips are validated to FIPS 140‑3 Level 3, ensuring that even this distributed model maintains the highest compliance standards. This innovation allows cryptographic operations to be performed locally, within the VM boundary. Keys are cached securely, hardware acceleration is provided for encryption, decryption, signing, and verification, and access is controlled through an oracle‑style model that ensures keys never leave the secure boundary. The result is a dramatic reduction in latency and a significant increase in throughput, while still maintaining compliance. This model is particularly well suited for TLS termination at scale, high‑frequency trading platforms, blockchain validation nodes, and large‑scale digital signing services, where both performance and assurance are critical. Entered public preview in September 2025, Trusted Launch must be enabled to use the feature, and Linux support is expected soon. Microsoft confirmed that Integrated HSM will be deployed across all new Azure servers, making it a foundational component of future infrastructure. Azure Integrated HSM also complements Azure Confidential Computing, allowing workloads to benefit from both in-use data protection through hardware-based enclaves and key protection via local HSM modules. This combination ensures that neither sensitive data nor cryptographic keys ever leave a secure hardware boundary — ideal for high-assurance applications. A Dynamic Vendor Landscape The vendor story behind these services is almost as interesting as the technology itself. Thales acquired nCipher in 2008, only to divest it in 2019 during its acquisition of Gemalto, under pressure from competition authorities. The buyer was Entrust, which suddenly found itself owning one of the most established HSM product lines. Meanwhile, Gemalto’s SafeNet Luna became part of Thales — which would also launch the Thales payShield 10K in 2019, leading PCI-certified payment HSM — and Marvell emerged as a new force with its LiquidSecurity line, optimized for cloud-scale deployments. Microsoft has navigated these shifts pragmatically, adapting its services and partnerships to ensure continuity for customers while embracing the best available hardware. Looking back, it is almost amusing to see how vendor mergers, acquisitions, and divestitures reshaped the HSM market, while Microsoft’s offerings evolved in lockstep to give customers a consistent path forward. Comparative Perspective Looking back at the evolution of Microsoft’s HSM integrations and services, a clear trajectory emerges: from the early days of Azure Key Vault Premium backed by certified HSMs (still active), completed by Azure Key Vault Managed HSM with higher compliance levels, through the Azure Dedicated HSM offering, replaced by the more cloud‑native Azure Cloud HSM, and finally to the innovative Azure Integrated HSM embedded directly in virtual machines. Each step reflects a balance between control, management, compliance, and performance, while also adapting to the vendor landscape and regulatory expectations. Service Hardware Introduced FIPS Level Model / Isolation Current Status / Notes Azure Key Vault Premium nCipher nShield (Thales → Entrust) Then Marvell LiquidSecurity 2015 FIPS 140‑2 Level 2 > Level 3 Shared per region, PaaS, HSM-backed Active; standard service; supports CEK and BYOK; multi-vendor BYOK since ~2020 Azure Dedicated HSM SafeNet Luna (Gemalto → Thales) 2018 FIPS 140‑2 Level 3 Dedicated appliance, single-tenant, VNet Retiring; no new onboardings; support until July 31, 2028; succeeded by Azure Cloud HSM Azure Key Vault Managed HSM Marvell LiquidSecurity 2021 FIPS 140‑3 Level 3 Dedicated cluster per customer, PaaS Active; redundant, isolated, fully managed; stronger compliance than Premium Azure Payment HSM Thales payShield 10K 2022 FIPS 140-2 Level 3 Bare-metal, single-tenant, full customer control, PCI-compliant Active. Purpose-built for payment workloads. Azure Cloud HSM Marvell LiquidSecurity 2025 FIPS 140‑3 Level 3 Single-tenant cluster, customer-administered Active; successor to Dedicated HSM; fast provisioning, built-in HA, private connectivity Azure Integrated HSM Microsoft custom chips 2025 FIPS 140‑3 Level 3 Embedded in VM host, local operations Active (preview/rollout); ultra-low latency, ideal for high-performance workloads Microsoft’s strategy shows an understanding that different customers have different requirements on the spectrum of control vs convenience. So Azure didn’t take a one-size-fits-all approach; it built a portfolio: - Use Azure Key Vault Premium if you want simplicity and can tolerate multi-tenancy. - Use Azure Key Vault Managed HSM if you need sole ownership of keys but want a turnkey service. - Use Azure Payment HSM if you operate regulated payment workloads and require PCI-certified hardware. - Use Azure Cloud HSM if you need sole ownership and direct access for legacy apps. - Use Azure Integrated HSM if you need ultra-low latency and per-VM key isolation, for the highest assurance in real-time. Beyond the HSM: A Silicon-to-Cloud Security Architecture by Design Microsoft’s HSM evolution is part of a broader strategy to embed security at every layer of the cloud infrastructure — from silicon to services. This vision, often referred to as “Silicon-to-Cloud”, includes innovations like Azure Boost, Caliptra, Confidential Computing, and now Azure Integrated HSM. Azure Confidential Computing plays a critical role in this architecture. As mentioned, by combining Trusted Execution Environments (TEEs) with Integrated HSM, Azure enables workloads to be protected at every stage — at rest, in transit, and in use — with cryptographic keys and sensitive data confined to verified hardware enclaves. This layered approach reinforces zero-trust principles and supports compliance in regulated industries. With Azure Integrated HSM installed directly on every new server, Microsoft is redefining how cryptographic assurance is delivered — not as a remote service, but as a native hardware capability embedded in the compute fabric itself. This marks a shift from centralized HSM clusters to distributed, silicon-level security, enabling ultra-low latency, high throughput, and strong isolation for modern cloud workloads. Resources To go a bit further, I invite you to check out the following articles and take a look at the related documentation. Protecting Azure Infrastructure from silicon to systems | Microsoft Azure Blog by Mark Russinovich, Chief Technology Officer, Deputy Chief Information Security Officer, and Technical Fellow, Microsoft Azure, Omar Khan, Vice President, Azure Infrastructure Marketing, and Bryan Kelly, Hardware Security Architect, Microsoft Azure Microsoft Azure Introduces Azure Integrated HSM: A Key Cache for Virtual Machines | Microsoft Community Hub by Simran Parkhe Securing Azure infrastructure with silicon innovation | Microsoft Community Hub by Mark Russinovich, Chief Technology Officer, Deputy Chief Information Security Officer, and Technical Fellow, Microsoft Azure About the Author I'm Samuel Gaston-Raoul, Partner Solution Architect at Microsoft, working across the EMEA region with the diverse ecosystem of Microsoft partners—including System Integrators (SIs) and strategic advisory firms, Independent Software Vendors (ISVs) / Software Development Companies (SDCs), and Startups. I engage with our partners to build, scale, and innovate securely on Microsoft Cloud and Microsoft Security platforms. With a strong focus on cloud and cybersecurity, I help shape strategic offerings and guide the development of security practices—ensuring alignment with market needs, emerging challenges, and Microsoft’s product roadmap. I also engage closely with our product and engineering teams to foster early technical dialogue and drive innovation through collaborative design. Whether through architecture workshops, technical enablement, or public speaking engagements, I aim to evangelize Microsoft’s security vision while co-creating solutions that meet the evolving demands of the AI and cybersecurity era.Introducing Microsoft Sentinel graph (Public Preview)
Security is being reengineered for the AI era—moving beyond static, rulebound controls and after-the-fact response toward platform-led, machine-speed defense. The challenge is clear: fragmented tools, sprawling signals, and legacy architectures that can’t match the velocity and scale of modern attacks. What’s needed is an AI-ready, data-first foundation—one that turns telemetry into a security graph, standardizes access for agents, and coordinates autonomous actions while keeping humans in command of strategy and high-impact investigations. Security teams already center operations on their SIEM for end-to-end visibility, and we’re advancing that foundation by evolving Microsoft Sentinel into both the SIEM and the platform for agentic defense—connecting analytics and context across ecosystems. And today, we announced the general availability of Sentinel data lake and introduced new preview platform capabilities that are built on Sentinel data lake (Figure 1), so protection accelerates to machine speed while analysts do their best work. We are excited to announce the public preview of Microsoft Sentinel graph, a deeply connected map of your digital estate across endpoints, cloud, email, identity, SaaS apps, and enriched with our threat intelligence. Sentinel graph, a core capability of the Sentinel platform, enables Defenders and Agentic AI to connect the dots and bring deep context quickly, enabling modern defense across pre-breach and post-breach. Starting today, we are delivering new graph-based analytics and interactive visualization capabilities across Microsoft Defender and Microsoft Purview. Attackers think in graphs. For a long time, defenders have been limited to querying and analyzing data in lists forcing them to think in silos. With Sentinel graph, Defenders and AI can quickly reveal relationships, traversable digital paths to understand blast radius, privilege escalation, and anomalies across large, cloud-scale data sets, deriving deep contextual insight across their digital estate, SOC teams and their AI Agents can stay proactive and resilient. With Sentinel graph-powered experiences in Defender and Purview, defenders can now reason over assets, identities, activities, and threat intelligence to accelerate detection, hunting, investigation, and response. Incident graph in Defender. The incident graph in the Microsoft Defender portal is now enriched with ability to analyze blast radius of the active attack. During an incident investigation, the blast radius analysis quickly evaluates and visualizes the vulnerable paths an attacker could take from a compromise entity to a critical asset. This allows SOC teams to effectively prioritize and focus their attack mitigation and response saving critical time and limiting impact. Hunting graph in Defender. Threat hunting often requires connecting disparate pieces of data to uncover hidden paths that attackers exploit to reach your crown jewels. With the new hunting graph, analysts can visually traverse the complex web of relationships between users, devices, and other entities to reveal privileged access paths to critical assets. This graph-powered exploration transforms threat hunting into a proactive mission, enabling SOC teams to surface vulnerabilities and intercept attacks before they gain momentum. This approach shifts security operations from reactive alert handling to proactive threat hunting, enabling teams to identify vulnerabilities and stop attacks before they escalate. Data risk graph in Purview Insider Risk Management (IRM). Investigating data leaks and insider risks is challenging when information is scattered across multiple sources. The data risk graph in IRM offers a unified view across SharePoint and OneDrive, connecting users, assets, and activities. Investigators can see not just what data was leaked, but also the full blast radius of risky user activity. This context helps data security teams triage alerts, understand the impact of incidents, and take targeted actions to prevent future leaks. Data risk graph in Purview Data Security Investigation (DSI). To truly understand a data breach, you need to follow the trail—tracking files and their activities across every tool and source. The data risk graph does this by automatically combining unified audit logs, Entra audit logs, and threat intelligence, providing an invaluable insight. With the power of the data risk graph, data security teams can pinpoint sensitive data access and movement, map potential exfiltration paths, and visualize the users and activities linked to risky files, all in one view. Getting started Microsoft Defender If you already have the Sentinel data lake, the required graph will be auto provisioned when you login into the Defender portal; hunting graph and incident graph experience will appear in the Defender portal. New to data lake? Use the Sentinel data lake onboarding flow to provision the data lake and graph. Microsoft Purview Follow the Sentinel data lake onboarding flow to provision the data lake and graph. In Purview Insider Risk Management (IRM), follow the instructions here. In Purview Data Security Investigation (DSI), follow the instructions here. Reference links Watch Microsoft Secure Microsoft Secure news blog Data lake blog MCP server blog ISV blog Security Store blog Copilot blog Microsoft Sentinel—AI-Powered Cloud SIEM | Microsoft SecurityIntroducing Microsoft Security Store
Security is being reengineered for the AI era—moving beyond static, rulebound controls and after-the-fact response toward platform-led, machine-speed defense. We recognize that defending against modern threats requires the full strength of an ecosystem, combining our unique expertise and shared threat intelligence. But with so many options out there, it’s tough for security professionals to cut through the noise, and even tougher to navigate long procurement cycles and stitch together tools and data before seeing meaningful improvements. That’s why we built Microsoft Security Store - a storefront designed for security professionals to discover, buy, and deploy security SaaS solutions and AI agents from our ecosystem partners such as Darktrace, Illumio, and BlueVoyant. Security SaaS solutions and AI agents on Security Store integrate with Microsoft Security products, including Sentinel platform, to enhance end-to-end protection. These integrated solutions and agents collaborate intelligently, sharing insights and leveraging AI to enhance critical security tasks like triage, threat hunting, and access management. In Security Store, you can: Buy with confidence – Explore solutions and agents that are validated to integrate with Microsoft Security products, so you know they’ll work in your environment. Listings are organized to make it easy for security professionals to find what’s relevant to their needs. For example, you can filter solutions based on how they integrate with your existing Microsoft Security products. You can also browse listings based on their NIST Cybersecurity Framework functions, covering everything from network security to compliance automation — helping you quickly identify which solutions strengthen the areas that matter most to your security posture. Simplify purchasing – Buy solutions and agents with your existing Microsoft billing account without any additional payment setup. For Azure benefit-eligible offers, eligible purchases contribute to your cloud consumption commitments. You can also purchase negotiated deals through private offers. Accelerate time to value – Deploy agents and their dependencies in just a few steps and start getting value from AI in minutes. Partners offer ready-to-use AI agents that can triage alerts at scale, analyze and retrieve investigation insights in real time, and surface posture and detection gaps with actionable recommendations. A rich ecosystem of solutions and AI agents to elevate security posture In Security Store, you’ll find solutions covering every corner of cybersecurity—threat protection, data security and governance, identity and device management, and more. To give you a flavor of what is available, here are some of the exciting solutions on the store: Darktrace’s ActiveAI Security SaaS solution integrates with Microsoft Security to extend self-learning AI across a customer's entire digital estate, helping detect anomalies and stop novel attacks before they spread. The Darktrace Email Analysis Agent helps SOC teams triage and threat hunt suspicious emails by automating detection of risky attachments, links, and user behaviors using Darktrace Self-Learning AI, integrated with Microsoft Defender and Security Copilot. This unified approach highlights anomalous properties and indicators of compromise, enabling proactive threat hunting and faster, more accurate response. Illumio for Microsoft Sentinel combines Illumio Insights with Microsoft Sentinel data lake and Security Copilot to enhance detection and response to cyber threats. It fuses data from Illumio and all the other sources feeding into Sentinel to deliver a unified view of threats across millions of workloads. AI-driven breach containment from Illumio gives SOC analysts, incident responders, and threat hunters unified visibility into lateral traffic threats and attack paths across hybrid and multi-cloud environments, to reduce alert fatigue, prioritize threat investigation, and instantly isolate workloads. Netskope’s Security Service Edge (SSE) platform integrates with Microsoft M365, Defender, Sentinel, Entra and Purview for identity-driven, label-aware protection across cloud, web, and private apps. Netskope's inline controls (SWG, CASB, ZTNA) and advanced DLP, with Entra signals and Conditional Access, provide real-time, context-rich policies based on user, device, and risk. Telemetry and incidents flow into Defender and Sentinel for automated enrichment and response, ensuring unified visibility, faster investigations, and consistent Zero Trust protection for cloud, data, and AI everywhere. PERFORMANTA Email Analysis Agent automates deep investigations into email threats, analyzing metadata (headers, indicators, attachments) against threat intelligence to expose phishing attempts. Complementing this, the IAM Supervisor Agent triages identity risks by scrutinizing user activity for signs of credential theft, privilege misuse, or unusual behavior. These agents deliver unified, evidence-backed reports directly to you, providing instant clarity and slashing incident response time. Tanium Autonomous Endpoint Management (AEM) pairs realtime endpoint visibility with AI-driven automation to keep IT environments healthy and secure at scale. Tanium is integrated with the Microsoft Security suite—including Microsoft Sentinel, Defender for Endpoint, Entra ID, Intune, and Security Copilot. Tanium streams current state telemetry into Microsoft’s security and AI platforms and lets analysts pivot from investigation to remediation without tool switching. Tanium even executes remediation actions from the Sentinel console. The Tanium Security Triage Agent accelerates alert triage, enabling security teams to make swift, informed decisions using Tanium Threat Response alerts and real-time endpoint data. Walkthrough of Microsoft Security Store Now that you’ve seen the types of solutions available in Security Store, let’s walk through how to find the right one for your organization. You can get started by going to the Microsoft Security Store portal. From there, you can search and browse solutions that integrate with Microsoft Security products, including a dedicated section for AI agents—all in one place. If you are using Microsoft Security Copilot, you can also open the store from within Security Copilot to find AI agents - read more here. Solutions are grouped by how they align with industry frameworks like NIST CSF 2.0, making it easier to see which areas of security each one supports. You can also filter by integration type—e.g., Defender, Sentinel, Entra, or Purview—and by compliance certifications to narrow results to what fits your environment. To explore a solution, click into its detail page to view descriptions, screenshots, integration details, and pricing. For AI agents, you’ll also see the tasks they perform, the inputs they require, and the outputs they produce —so you know what to expect before you deploy. Every listing goes through a review process that includes partner verification, security scans on code packages stored in a secure registry to protect against malware, and validation that integrations with Microsoft Security products work as intended. Customers with the right permissions can purchase agents and SaaS solutions directly through Security Store. The process is simple: choose a partner solution or AI agent and complete the purchase in just a few clicks using your existing Microsoft billing account—no new payment setup required. Qualifying SaaS purchases also count toward your Microsoft Azure Consumption Commitment (MACC), helping accelerate budget approvals while adding the security capabilities your organization needs. Security and IT admins can deploy solutions directly from Security Store in just a few steps through a guided experience. The deployment process automatically provisions the resources each solution needs—such as Security Copilot agents and Microsoft Sentinel data lake notebook jobs—so you don’t have to do so manually. Agents are deployed into Security Copilot, which is built with security in mind, providing controls like granular agent permissions and audit trails, giving admins visibility and governance. Once deployment is complete, your agent is ready to configure and use so you can start applying AI to expand detection coverage, respond faster, and improve operational efficiency. Security and IT admins can view and manage all purchased solutions from the “My Solutions” page and easily navigate to Microsoft Cost Management tools to track spending and manage subscriptions. Partners: grow your business with Microsoft For security partners, Security Store opens a powerful new channel to reach customers, monetize differentiated solutions, and grow with Microsoft. We will showcase select solutions across relevant Microsoft Security experiences, starting with Security Copilot, so your offerings appear in the right context for the right audience. You can monetize both SaaS solutions and AI agents through built-in commerce capabilities, while tapping into Microsoft’s go-to-market incentives. For agent builders, it’s even simpler—we handle the entire commerce lifecycle, including billing and entitlement, so you don’t have to build any infrastructure. You focus on embedding your security expertise into the agent, and we take care of the rest to deliver a seamless purchase experience for customers. Security Store is built on top of Microsoft Marketplace, which means partners publish their solution or agent through the Microsoft Partner Center - the central hub for managing all marketplace offers. From there, create or update your offer with details about how your solution integrates with Microsoft Security so customers can easily discover it in Security Store. Next, upload your deployable package to the Security Store registry, which is encrypted for protection. Then define your license model, terms, and pricing so customers know exactly what to expect. Before your offer goes live, it goes through certification checks that include malware and virus scans, schema validation, and solution validation. These steps help give customers confidence that your solutions meet Microsoft’s integration standards. Get started today By creating a storefront optimized for security professionals, we are making it simple to find, buy, and deploy solutions and AI agents that work together. Microsoft Security Store helps you put the right AI‑powered tools in place so your team can focus on what matters most—defending against attackers with speed and confidence. Get started today by visiting Microsoft Security Store. If you’re a partner looking to grow your business with Microsoft, start by visiting Microsoft Security Store - Partner with Microsoft to become a partner. Partners can list their solution or agent if their solution has a qualifying integration with Microsoft Security products, such as a Sentinel connector or Security Copilot agent, or another qualifying MISA solution integration. You can learn more about qualifying integrations and the listing process in our documentation here.
