Blog 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.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/overviewAnnouncing a New Microsoft Security Virtual Training Day
We’re thrilled to announce a brand-new opportunity for learning and growth: Microsoft Virtual Training Day: Strength Cloud Security with Microsoft Defender for Cloud! This free, online event is designed to empower professionals with the skills and knowledge needed to thrive in today’s digital landscape. During this training, you’ll be able to: Learn how to increase cloud security using Microsoft Defender for Cloud and how to deploy security across your DevOps workflows. Discover how to detect risks, maintain compliance, and protect hybrid and multicloud environments. Find out how to defend servers, containers, storage, and databases using built-in security. Chat with Microsoft experts—ask questions and get answers on real-world security challenges. Here’s what you can expect: Part 1 Part 2 Introduction Introduction What a comprehensive cloud-native application protection platform looks like Comprehensive workload protection (part 1) Break: 10 minutes Break: 10 minutes Starting with proactive security Comprehensive workload protection (part 2) Break: 10 minutes Automating responses Operationalizing Posture Management Closing question and answer Closing question and answer Why Attend this Virtual Training Day? Microsoft Virtual Training Days offer a host of benefits: Flexible Learning: Attend from anywhere, at your own pace. Expert Instruction: Gain insights from industry leaders and certified professionals. Certification Opportunities: Many sessions prepare you for Microsoft certifications. Networking: Connect with peers and professionals across industries. Free Resources: Access downloadable materials and follow-up learning paths. Earn a voucher: Upon completion of the event, the exam is offered at a 50% discount off the exam rate. Don't miss out on this opportunity. Go and registertoday! For more information on all things security, please visit our Security Hub.
Introducing Microsoft Sentinel data lake
Today, we announced a significant expansion of Microsoft Sentinel’s capabilities through the introduction of Sentinel data lake, now rolling out in public preview. Security teams cannot defend what they cannot see and analyze. With exploding volumes of security data, organizations are struggling to manage costs while maintaining effective threat coverage. Do-it-yourself security data architectures have perpetuated data silos, which in turn have reduced the effectiveness of AI solutions in security operations. With Sentinel data lake, we are taking a major step to address these challenges. Microsoft Sentinel data lake enables a fully managed, cloud-native, data lake that is purposefully designed for security, right inside Sentinel. Built on a modern lake architecture and powered by Azure, Sentinel data lake simplifies security data management, eliminates security data silos, and enables cost-effective long-term security data retention with the ability to run multiple forms of analytics on a single copy of that data. Security teams can now store and manage all security data. This takes the market-leading capabilities of Sentinel SIEM and supercharges it even further. Customers can leverage the data lake for retroactive TI matching and hunting over a longer time horizon, track low and slow attacks, conduct forensics analysis, build anomaly insights, and meet reporting & compliance needs. By unifying security data, Sentinel data lake provides the AI ready data foundation for AI solutions. Let’s look at some of Sentinel data lake’s core features. Simplified onboarding and enablement inside Defender Portal: Customers can easily discover and enable the new data lake from within the Defender portal, either from the banner on the home page or from settings. Setting up a modern data lake now is just a click away, empowering security teams to get started quickly without a complex setup. Simplified security data management: Sentinel data lake works seamlessly with existing Sentinel connectors. It brings together security logs from Microsoft services across M365, Defender, Azure, Entra, Purview, Intune plus third-party sources like AWS, GCP, network and firewall data from 350+ connectors and solutions. The data lake supports Sentinel’s existing table schemas while customers can also create custom connectors to bring raw data into the data lake or transform it during ingestion. In the future, we will enable additional industry-standard schemas. The data lake expands beyond just activity logs by including a native asset store. Critical asset information is added to the data lake using new Sentinel data connectors for Microsoft 365, Entra, and Azure, enabling a single place to analyze activity and asset data enriched with Threat intelligence. A new table management experience makes it easy for customers to choose where to send and store data, as well as set related retention policies to optimize their security data estate. Customers can easily send critical, high-fidelity security data to the analytics tier or choose to send high-volume, low fidelity logs to the new data lake tier. Any data brought into the analytics tier is automatically mirrored into the data lake at no additional charge, making data lake the central location for all security data. Advanced data analysis capabilities over data in the data lake: Sentinel data lake stores all security data in an open format to enable analysts to do multi-modal security analytics on a single copy of data. Through the new data lake exploration experience in the Defender portal, customers can leverage Kusto query language to analyze historical data using the full power of Kusto. Since the data lake supports the Sentinel table schema, advanced hunting queries can be run directly on the data lake. Customers can also schedule long-running jobs, either once or on a schedule, that perform complex analysis on historical data for in-depth security insights. These insights generated from the data lake can be easily elevated to analytics tier and leveraged in Sentinel for threat investigation and response. Additionally, as part of the public preview, we are also releasing a new Sentinel Visual Studio Code extension that enables security teams to easily connect to the same data lake data and use Python notebooks, as well as spark and ML libraries to deeply analyze lake data for anomalies. Since the environment is fully managed, there is no compute infrastructure to set up. Customers can just install the Visual Studio Code extension and use AI coding agents like GitHub Copilot to build a notebook and execute it in the managed environment. These notebooks can also be scheduled as jobs and the resulting insights can be elevated to analytics tier and leveraged in Sentinel for threat investigation and response. Flexible business model: Sentinel data lake enables customers to separate their data ingestion and retention needs from their security analytics needs, allowing them to ingest and store data cost effectively and then pay separately when analyzing data for their specific needs. Let’s put this all together and show an example of how a customer can operationalize and derive value from the data lake for retrospective threat intelligence matching in Microsoft Sentinel. Network logs are typically high-volume logs but can often contain key insights for detecting initial entry point of an attack, command and control connection, lateral movement or an exfiltration attempt. Customers can now send these high-volume logs to the data lake tier. Next, they can create a python notebook that can join latest threat intelligence from Microsoft Defender Threat Intelligence to scan network logs for any connections to/from a suspicious IP or domain. They can schedule this notebook to run as a scheduled job, and any insights can then be promoted to analytics tiers and leveraged to enrich ongoing investigation, hunts, response or forensics analysis. All this is possible cost-effectively without having to set up any complex infrastructure, enabling security teams to achieve deeper insights. This preview is now rolling out for customers in Defender portal in our supported regions. To learn more, check out our Mechanics video and our documentation or talk to your account teams. Get started today Join us as we redefine what’s possible in security operations: Onboard Sentinel data lake: https://aka.ms/sentineldatalakedocs Explore our pricing: https://aka.ms/sentinel/pricingblog For the supported regions, please refer to https://aka.ms/sentinel/datalake/geos Learn more about our MDTI news: http://aka.ms/mdti-convergence General Availability of Auxiliary Logs and Reduced Pricing
