🔍 Implications for Microsoft’s AI-First Model 1. Attack Surface Explosion Every AI connector, plugin, or telemetry pipeline adds new entry points for adversaries. 2. Supply Chain Fragility AI integrations often rely on multiple third-party APIs; one weak link can compromise entire ecosystems. 3. Telemetry & Data Exposure AI models thrive on data, but over-privileged APIs may leak sensitive information beyond intended scope. 4. Lifecycle Gaps Legacy systems that remain connected after support ends create an exploitable bridge between old and new environments. 🧩 The Security Imperative We can’t ignore the innovation that AI brings but we also can’t treat AI integration as a “feature upgrade.” It’s an attack surface transformation. Organizations need to: ✅ Map and monitor all AI-connected APIs ✅ Enforce least-privilege access and token hygiene ✅ Perform continuous red teaming against AI and API layers ✅ Demand transparency from vendors on how AI features collect, store, and process data Final Thoughts The future of operating systems isn’t about just running software — it’s about running intelligent, connected systems. But with that evolution comes accountability. Microsoft, and every enterprise adopting AI-first platforms, must recognize that every endpoint, API, and model call is now part of the cybersecurity perimeter. As we’ve seen from recent API breaches, connectivity without security is the fastest path to compromise. The next major data breach may not come from human error — it may come from the AI systems we helped train. #CyberSecurityAwarenessMonth #AI #AppSec #APISecurity #Microsoft #OWASP #DataSecurity #ThreatIntelligence #Pentesting #CyberRisk
Microsoft's AI-First Model: Security Risks and Imperatives
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🚨 Agentic AI Is Redefining Security Operations — And It’s Closer to Our World Than Ever 🚨 As someone working in Azure Monitoring & Observability, I’m excited to see how Microsoft is transforming Microsoft Sentinel into an agentic AI-driven platform. This evolution goes far beyond traditional SIEM — it introduces AI agents capable of reasoning, correlating signals, and even acting autonomously to support defenders. What stands out to me 👇 🔍 Unified Context with Sentinel Data Lake & Graph No more siloed signals — telemetry, identities, assets, and security events are brought together for true end-to-end visibility. 🤖 Model Context Protocol (MCP) & Security Copilot Integration Security teams can now build custom agents (no-code or using GitHub Copilot) to automate investigation, triage, and insights — similar to how we build observability logic with Prometheus, OTel, or Kusto. 🛡️ Security for AI: Guardrails & Trust With agents comes responsibility — Microsoft introduces controls for prompt injection, PII protection, and enforcing agent boundaries. 💭 My Take: This marks the beginning of SOC operations working hand-in-hand with autonomous AI agents — just like how we’ve seen automation reshape cloud operations. The intersection of Observability + AI + Security is becoming real, and this is where our skills must evolve. 🔗 Read the full article: https://lnkd.in/erVAgNMh --- #Azure #MicrosoftSentinel #AgenticAI #CyberSecurity #Observability #MicrosoftSecurity #AIOps #AzureMonitor #SOC #FutureOfWork
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AI Security Heatmap: Practical Controls and Accelerated Response with Microsoft. Overview As organizations scale generative AI, two motions must advance in lockstep: hardening the AI stack (“Security for AI”) and using AI to supercharge SecOps (“AI for Security”). This post is a practical map—covering assets, common attacks, scope, solutions, SKUs, and ownership—to help you ship AI safely and investigate faster. Why both motions matter, at the same time Security for AI (hereafter ‘ Secure AI’ ) guards prompts, models, apps, data, identities, keys, and networks; it adds governance and monitoring around GenAI workloads (including indirect prompt injection from retrieved documents and tools). Agents add complexity because one prompt can trigger multiple actions, increasing the blast radius if not constrained. AI for Security uses Security Copilot with Defender XDR, Microsoft Sentinel, Purview, Entra, and threat... #techcommunity #azure #microsoft https://lnkd.in/g4AWKaXb
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AI Security Ideogram: Practical Controls and Accelerated Response with Microsoft. Overview As organizations scale generative AI, two motions must advance in lockstep: hardening the AI stack (“Security for AI”) and using AI to supercharge SecOps (“AI for Security”). This post is a practical map—covering assets, common attacks, scope, solutions, SKUs, and ownership—to help you ship AI safely and investigate faster. Why both motions matter, at the same time Security for AI (hereafter ‘ Secure AI’ ) guards prompts, models, apps, data, identities, keys, and networks; it adds governance and monitoring around GenAI workloads (including indirect prompt injection from retrieved documents and tools). Agents add complexity because one prompt can trigger multiple actions, increasing the blast radius if not constrained. AI for Security uses Security Copilot with Defender XDR, Microsoft Sentinel, Purview, Entra, and threat... #techcommunity #azure #microsoft https://lnkd.in/gyB-hHRs
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An evening well spent Microsoft Secure September 30 2025 and I’m leaving with so much to think about. AI isn’t just changing how we work; it’s reshaping what “secure” really means. For years we have designed Zero Trust around people, devices and data. But now AI is part of the enterprise surface area. If we don’t secure it from the inside out, we create a new class of insider risk. Here’s what stood out: 🔹 Security for AI Agents Microsoft is embedding cross-prompt injection protection, task adherence, and PII detection into the platform, powered by Azure AI Foundry and DSPM for AI in Microsoft Purview, alongside Microsoft Entra Agent ID. Task adherence ensure AI agents stay within their assigned mission. If it drifts outside scope, you know immediately via Azure AI Foundry. PII detection watches for personal or sensitive data (names, IDs, phone numbers) and can flag, mask or block it before it’s exposed or stored. Cross-prompt injection protection defends against malicious prompts that try to override instructions and force models to leak data or act in unintended ways. Together these are the guardrails that turn AI from a black box into a controllable, auditable system. 🔹 Architecture that unifies security data and intelligence Microsoft is turning its ecosystem into a connected fabric with Microsoft Sentinel as the intelligence backbone, unifying telemetry into tabular, graph and embedding models through the MCP server. On top of that foundation sits Defender, Entra, Intune, and Purview, with Security Copilot providing reasoning and action across the stack. security data is being normalised and enriched so AI can understand context and act responsibly. I’m especially excited about: Custom Security Copilot Agents → moving from using pre-built agents to building organisation-specific copilots, low-code or pro-code. Zero Trust is no longer static. It’s becoming a living, adaptive architecture where identity, data and AI intersect. The organisations that thrive will be those who can engineer controls as fast as they adopt new AI. This is the future of cyber defence designing security as you design intelligence. #MicrosoftSecure #ZeroTrust #AI #Cybersecurity #MicrosoftSecurity #Sentinel #Defender #DSPM #CloudSecurity
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Stop building AI agents without a firewall! Your written compliance policy is officially obsolete. ➤ Do you know what this means for data governance? It means the governance 𝒊𝒔 𝒏𝒐𝒘 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒅𝒆, not just the handbook. ⇢ This is a huge step forward with the launch of the Microsoft Agent Framework and the new capabilities in Azure AI Foundry, clearly signaling the shift from a passive compliance such as having a written policy to active runtime enforcement (the technology preventing the policy violation). 1) PII Guardrails enforce data privacy by automatically detecting and redacting sensitive PII in real-time during tool calls. 2) The Task Adherence API ensures data integrity and security by acting as an agentic policy firewall, preventing agents from making unauthorized or erroneous calls outside of their defined scope. ↳ Operationalizing data governance in the age of autonomous agents starts here. I think it’s a must-read for anyone building or governing AI systems. ↳ Check out more on the link provided below shared from Microsoft's Chief Product Officer Sarah Bird's post- Introducing Microsoft Agent Framework.
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Think free AI tools are a bargain? 💰 They can actually cost you big on data security, compliance, and IT overhead. Microsoft's Copilot Chat offers a safer alternative for businesses, with built-in controls to keep data protected and teams compliant. ✅ Enterprise-grade security ✅ Local data residency ✅ Admin control over access and usage Ready to try a smarter, more secure approach to AI? #CopilotChat #DataProtection #SecureAI https://lnkd.in/gTgq6X5r
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I recently created this Microsoft security history slide to tell the story of the different eras of security as the world (and security/tech industry) woke up to the different dimensions of security: ◾ Starting with the old orange book /common criteria days of "security is a feature" ◾ …then the first big worldwide security events in the early 2000s that woke the world up to the fact that secure code mattered ◾ …and then the release of the PTH toolkit and later Mimikatz that brought secure operations to the spotlight ◾ followed by the complexity and amplification of cloud complexity and the big game ransomware that grew out of the industrialize attack complex (which drove the need for end to end Zero Trust approach) ◾ …and to today's world of generative AI (it was nice to see Microsoft was way ahead of the curve with Responsible AI and AI Red Team guidance back in 2017 or so.) thoughts? feedback? memories? p.s. these are the notes on how I picked dates: ◾ The Microsoft dates are typically the first public/mainstream milestone of when something begins. ◾ The Era/Trend dates reflect mainstream/popularity of ideas/tools that began affecting risk for many organizations, not necessarily their first conceptualization. For example, the PTH technique was well known since 1997, but didn’t impact most organizations until it was weaponized as a free tool for anyone to use. Similarly secure by design/secure and operations, etc. were figured out long before they became a mainstream philosophy.
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Microsoft Copilot AI Adopted by U.S. House of Representatives to Drive Digital Transformation in Government House members and staff will gain access to Microsoft Copilot AI features, including a chatbot synced with Outlook emails, OneDrive, and Microsoft Teams. Importantly, the system is being implemented with “heightened legal and data protections” to ensure cybersecurity and compliance in a highly sensitive environment. #Technology, #AI, #DigitalMarketing, #SoftwareEngineering, #CloudComputing, #DataScience,#Innovation https://lnkd.in/ghEH6x6G
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🚨 𝐄𝐜𝐡𝐨𝐋𝐞𝐚𝐤 (𝐂𝐕𝐄‑2025‑32711): 𝐖𝐡𝐞𝐧 𝐀𝐈 𝐓𝐨𝐨𝐥𝐬 𝐓𝐮𝐫𝐧 𝐈𝐧𝐬𝐢𝐝𝐞 𝐎𝐮𝐭 Zero‑click vulnerabilities sounded futuristic. EchoLeak makes them real. Microsoft 365 Copilot had a critical prompt‑injection flaw that required no user interaction to exfiltrate data. That’s a big shift in AI risk profiles. 🔍 𝐇𝐨𝐰 𝐄𝐜𝐡𝐨𝐋𝐞𝐚𝐤 𝐖𝐨𝐫𝐤𝐞𝐝 • A single crafted email, without any malicious link or attachment, triggers the vulnerability. • It chains several bypasses: • Evading Microsoft’s XPIA (Cross‑Prompt Injection Attempt) classifier. • Using reference‑style Markdown to bypass link redaction. • Exploiting auto‑fetched images. • Leveraging Microsoft Teams proxy, allowed by Content Security Policy (CSP). No click, no macro, no suspicious behaviour visible to the user — just AI reading internal context and leaking data. 🛡 𝐃𝐞𝐟𝐞𝐧𝐬𝐞𝐬 & 𝐖𝐡𝐚𝐭 𝐘𝐨𝐮 𝐒𝐡𝐨𝐮𝐥𝐝 𝐃𝐨 • Review and tighten prompt/response sanitization in your AI and Copilot‑like tools. • Limit internal data exposure via AI tools; apply least privilege principles. For example restrict what documents, emails, or internal knowledge the model can access. • Monitor for abnormal internal queries in AI agents. Logging and auditing are crucial. • Apply vendor patches. Microsoft has addressed this issue, so ensure your Copilot version is up to date. 💡 𝐅𝐢𝐧𝐚𝐥 𝐑𝐞𝐟𝐥𝐞𝐜𝐭𝐢𝐨𝐧 EchoLeak isn’t just a curiosity — it’s a warning. AI systems, especially those deeply integrated with enterprise data, must be treated like critical infrastructure. Attack surfaces expand when models read internal sources. 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐲𝐨𝐮: • Has your org audited AI tools for similar vulnerabilities? • What controls do you think should be mandatory for enterprise AI to reduce risk? #𝘈𝘐 #𝘊𝘝𝘌202532711 #𝘌𝘤𝘩𝘰𝘓𝘦𝘢𝘬 #𝘗𝘳𝘰𝘮𝘱𝘵𝘐𝘯𝘫𝘦𝘤𝘵𝘪𝘰𝘯 #𝘐𝘯𝘧𝘰𝘴𝘦𝘤 #𝘎𝘦𝘯𝘈𝘐 #𝘚𝘦𝘤𝘶𝘳𝘪𝘵𝘺𝘉𝘺𝘋𝘦𝘴𝘪𝘨𝘯 #𝘉𝘭𝘶𝘦𝘛𝘦𝘢𝘮
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Why Your AI Network Automation Is Still Failing—and How to Fix It AI‑driven networking sounds futuristic until you spend hours troubleshooting a “smart” policy that never applies. The pain isn’t the tech—it’s the blind spots you never saw coming. 🔹 You trusted default intents in the M365 Intune policy engine, forgetting that AI ignores non‑standard VLAN tags. 🔹 You left legacy DNS zones unmanaged, so the AI model kept routing traffic to obsolete endpoints. 🔹 You never audited the conditional access scripts; a single mis‑typed regex broke the whole automation chain. 🔹 You assumed the AI would self‑heal. It doesn’t – it repeats the last good config until you intervene. The truth: AI in networking only works when you feed it clean, complete data and enforce strict governance. Anything less is a recipe for silent failure. My stance: Stop treating AI as a set‑and‑forget solution. Treat it as a high‑precision tool that demands the same rigor you apply to manual scripts. What’s the single oversight in your AI network rollout that keeps you up at night? Drop a comment, tag a teammate who needs to hear this, and let’s get the conversation rolling. #AIinNetworking #Microsoft365 #Intune #Cybersecurity #NetworkAutomation #ITLeadership #TechOps
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