Enterprise Strategy for Secure Agentic AI: From Compliance to Implementation
Imagine an AI system that doesn’t just answer questions but takes action querying your databases, updating records, triggering workflows, even processing refunds without human intervention. That’s Agentic AI and it’s here. But with great power comes great responsibility. This autonomy introduces new attack surfaces and regulatory obligations. The Model Context Protocol (MCP) Server the gateway between your AI agent and critical systems becomes your Tier-0 control point. If it fails, the blast radius is enormous. This is the story of how enterprises can secure Agentic AI, stay compliant and implement Zero Trust architectures using Azure AI Foundry. Think of it as a roadmap a journey with three milestones - Milestone 1: Securing the Foundation Our journey starts with understanding the paradigm shift. Traditional AI with RAG (Retrieval-Augmented Generation) is like a librarian: It retrieves pre-indexed data. It summarizes information. It never changes the books or places orders. Security here is simple: protect the index, validate queries, prevent data leaks. But Agentic AI? It’s a staffer with system access. It can: Execute tools and business logic autonomously. Chain operations: read → analyze → write → notify. Modify data and trigger workflows. Bottom line: RAG is a “smart librarian.” Agentic AI is a “staffer with system access.” Treat the security model accordingly. And that means new risks: unauthorized access, privilege escalation, financial impact, data corruption. So what’s the defense? Ten critical security controls your first line of protection: Here’s what a production‑grade, Zero Trust MCP gateway needs. Its intentionally simplified in the demo (e.g., no auth) to highlight where you must harden in production. (https://github.com/davisanc/ai-foundry-mcp-gateway) Authentication Demo: None Prod: Microsoft Entra ID, JWT validation, Managed Identity, automatic credential rotation Authorization & RBAC Demo: None Prod: Tool‑level RBAC via Entra; least privilege; explicit allow‑lists per agent/capability Input Validation Demo: Basic (ext whitelist, 10MB, filename sanitize) Prod: JSON Schema validation, injection guards (SQL/command), business‑rule checks Rate Limiting Demo: None Prod: Multi‑tier (per‑agent, per‑tool, global), adaptive throttling, backoff Audit Logging Demo: Console → App Service logs Prod: Structured logs w/ correlation IDs, compliance metadata, PII redaction Session Management Demo: In‑memory UUID sessions Prod: Encrypted distributed storage (Redis/Cosmos DB), tenant isolation, expirations File Upload Security Demo: Ext whitelist, size limits, memory‑only Prod: 7‑layer defense (validate, MIME, malware scanning via Defender for Storage), encryption at rest, signed URLs Network Security Demo: Public App Service + HTTPS Prod: Private Endpoints, VNet integration, NSGs, Azure Firewall no public exposure Secrets Management Demo: App Service env vars (not in code) Prod: Azure Key Vault + Managed Identity, rotation, access audit Observability & Threat Detection (5‑Layer Stack) Layer 1: Application Insights (requests, dependencies, custom security events) Layer 2: Azure AI Content Safety (harmful content, jailbreaks) Layer 3: Microsoft Defender for AI (prompt injection incl. ASCII smuggling, credential theft, anomalous tool usage) Layer 4: Microsoft Purview for AI (PII/PHI classification, DLP on outputs, lineage, policy) Layer 5: Microsoft Sentinel (SIEM correlation, custom rules, automated response) Note: Azure AI Content Safety is built into Azure AI Foundry for real‑time filtering on both prompts and completions. Picture this as an airport security model: multiple checkpoints, each catching what the previous missed. That’s defense-in-depth. Zero Trust in Practice ~ A Day in the Life of a Prompt Every agent request passes through 8 sequential checkpoints, mapped to MITRE ATLAS tactics/mitigations (e.g., AML.M0011 Input Validation, AML.M0004 Output Filtering, AML.M0015 Adversarial Input Detection). The design goal is defense‑in‑depth: multiple independent controls, different detection signals, and layered failure modes. Checkpoints 1‑7: Enforcement (deny/contain before business systems) Checkpoint 8: Monitoring (detect/respond, hunt, learn, harden) AML.M0009 – Control Access to ML Models AML.M0011 – Validate ML Model Inputs AML.M0000 – Limit ML Model Availability AML.M0014 – ML Artifact Logging AML.M0004 – Output Filtering AML.M0015 – Adversarial Input Detection If one control slips, the others still stand. Resilience is the product of layers. Milestone 2: Navigating Compliance Next stop: regulatory readiness. The EU AI Act is the world’s first comprehensive AI law. If your AI system operates in or impacts the EU market, compliance isn’t optional, it’s mandatory. Agentic AI often falls under high-risk classification. That means: Risk management systems. Technical documentation. Logging and traceability. Transparency and human oversight. Fail to comply? Fines up to €30M or 6% of global turnover. Azure helps you meet these obligations: Entra ID for identity and RBAC. Purview for data classification and DLP. Defender for AI for prompt injection detection. Content Safety for harmful content filtering. Sentinel for SIEM correlation and incident response. And this isn’t just about today. Future regulations are coming US AI Executive Orders, UK AI Roadmap, ISO/IEC 42001 standards. The trend is clear: transparency, explainability, and continuous monitoring will be universal. Milestone 3: Implementation Deep-Dive Now, the hands-on part. How do you build this strategy into reality? Step 1: Entra ID Authentication Register your MCP app in Entra ID. Configure OAuth2 and JWT validation. Enable Managed Identity for downstream resources. Step 2: Apply the 10 Controls RBAC: Tool-level access checks. Validation: JSON schema + injection prevention. Rate Limiting: Express middleware or Azure API Management. Audit Logging: Structured logs with correlation IDs. Session Mgmt: Redis with encryption. File Security: MIME checks + Defender for Storage. Network: Private Endpoints + VNet. Secrets: Azure Key Vault. Observability: App Insights + Defender for AI + Purview + Sentinel. Step 3: Secure CI/CD Pipelines Embed compliance checks in Azure DevOps: Pre-build: Secret scanning. Build: RBAC & validation tests. Deploy: Managed Identity for service connections. Post-deploy: Compliance scans via Azure Policy. Step 4: Build the 5-Layer Observability Stack App Insights → Telemetry. Content Safety → Harmful content detection. Defender for AI → Prompt injection monitoring. Purview → PII/PHI classification and lineage. Sentinel → SIEM correlation and automated response. The Destination: A Secure, Compliant Future By now, you’ve seen the full roadmap: Secure the foundation with Zero Trust and layered controls. Navigate compliance with EU AI Act and prepare for global regulations. Implement the strategy using Azure-native tools and CI/CD best practices. Because in the world of Agentic AI, security isn’t optional, compliance isn’t negotiable, and observability is your lifeline. Resources https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-azure-ai-foundry https://learn.microsoft.com/en-us/azure/defender-for-cloud/ai-threat-protection https://learn.microsoft.com/en-us/purview/ai-microsoft-purview https://atlas.mitre.org/ https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence https://techcommunity.microsoft.com/blog/microsoft-security-blog/microsoft-sentinel-mcp-server---generally-available-with-exciting-new-capabiliti/4470125Microsoft Sentinel Graph with Microsoft Security Solutions
Why I Chose Sentinel Graph Modern security operations demand speed and clarity. Attackers exploit complex relationships across identities, devices, and workloads. I needed a solution that could: Correlate signals across identity, endpoint and cloud workloads. Predict lateral movement and highlight blast radius for compromised accounts. Integrate seamlessly with Microsoft Defender, Entra ID and Purview. Sentinel Graph delivered exactly that, acting as the reasoning layer for AI-driven defense. What's new: Sentinel Graph Public Preview Sentinel Graph introduces: Graph-based threat hunting: Traverse relationships across millions of entities. Blast radius analysis: Visualize the impact of compromised accounts or assets. AI-powered reasoning: Built for integration with Security Copilot. Native integration with Microsoft Defender and Purview for unified security posture. Uncover Hidden Security Risks Sentinel Graph helps security teams: Expose lateral movement paths that attackers could exploit. Identify choke points where defenses can be strengthened. Reveal risky relationships between identities, devices, and resources that traditional tools miss. Prioritize remediation by visualizing the most critical nodes in an attack path. This capability transforms threat hunting from reactive alert triage to proactive risk discovery, enabling defenders to harden their environment before an attack occurs. How to Enable Defense at All Stages Sentinel Graph strengthens defense across: Prevention: Identify choke points and harden critical paths before attackers exploit them. Detection: Use graph traversal to uncover hidden attack paths and suspicious relationships. Investigation: Quickly pivot from alerts to full graph-based context for deeper analysis. Response: Contain threats faster by visualizing blast radius and isolating impacted entities. This end-to-end approach ensures security teams can anticipate, detect, and respond with precision. How I Implemented It Step 1: Enabling Sentinel Graph If you already have the Sentinel Data Lake, the graph is auto provisioned when you sign in to the Microsoft Defender portal. Hunting graph and blast radius experiences appear directly in Defender. New to Data Lake? Use the Sentinel Data Lake onboarding flow to enable both the data lake and graph. Step 2: Integration with Microsoft Defender Practical examples from my project: Query: Show me all entities connected to this suspicious IP address. → Revealed lateral movement attempts across multiple endpoints. Query: Map the blast radius of a compromised account. → Identified linked service principals and privileged accounts for isolation. Step 3: Integration with Microsoft Purview In Purview Insider Risk Management, follow Data Risk Graph setup instructions. In Purview Data Security Investigations, enable Data Risk Graph for sensitive data flow analysis. Example: Query: Highlight all paths where sensitive data intersects with external connectors. → Helped detect risky data exfiltration paths. Step 4: AI-Powered Insights Using Microsoft Security Copilot, I asked: Predict the next hop for this attacker based on current graph state. Identify choke points in this attack path. This reduced investigation time and improved proactive defense. If you want to experience the power of Microsoft Sentinel Graph, here’s how you can get started Enable Sentinel Graph In your Sentinel workspace, turn on the Sentinel Data Lake. The graph will be auto provisioned when you sign in to the Microsoft Defender portal. Connect Microsoft Security Solutions Use built-in connectors to integrate Microsoft Defender, Microsoft Entra ID, and Microsoft Purview. This ensures unified visibility across identities, endpoints, and data. Explore Graph Queries Start hunting with Sentinel Notebooks or take it a step further by integrating with Microsoft Security Copilot for natural language investigations. Example: “Show me the blast radius of a compromised account.” or “Find everything connected to this suspicious IP address.” You can sign up here for a free preview of Sentinel graph MCP tools, which will also roll out starting December 1, 2025.Know MCP risks before you deploy!
The Model Context Protocol (MCP) is emerging as a powerful standard for enabling AI agents to interact with tools and data. However, like any evolving technology, MCP introduces new security challenges that organizations must address before deploying it in production environments. Major MCP Vulnerabilities MCP’s flexibility comes with risks. Here are the most critical vulnerabilities: Prompt Injection Attackers embed hidden instructions in user input, manipulating the model to trigger unauthorized MCP actions and bypass safety rules. Tool Poisoning Malicious MCP servers provide misleading tool descriptions or parameters, tricking agents into leaking sensitive data or executing harmful commands. Remote Code Execution Untrusted servers can inject OS-level commands through compromised endpoints, enabling full control over the host environment. Unauthenticated Access Rogue MCP servers bypass authentication and directly call sensitive tools, extracting internal data without user consent. Confused Deputy (OAuth Proxy) A malicious server misuses OAuth tokens issued for a trusted agent, performing unauthorized actions under a legitimate identity. MCP Configuration Poisoning Attackers silently modify approved configuration files so agents execute malicious commands as if they were part of the original setup. Token or Credential Theft Plaintext MCP config files expose API keys, cloud credentials, and access tokens, making them easy targets for malware or filesystem attacks. Path Traversal Older MCP filesystem implementations allow navigation outside the intended directory, exposing sensitive project or system files. Token Passthrough Some servers blindly accept forwarded tokens, allowing compromised agents to impersonate other services without validation. Session Hijacking Session IDs appearing in URLs can be captured from logs or redirects and reused to access active sessions. Current Known Limitations While MCP is promising, it has structural limitations that organizations must plan for: Lack of Native Tool Authenticity Verification There is no built-in mechanism to verify if a tool or server is genuine. Trust relies on external validation, increasing exposure to tool poisoning attacks. Weak Context Isolation Multi-session environments risk cross-contamination, where sensitive data from one session leaks into another. Limited Built-In Encryption Enforcement MCP depends on HTTPS/TLS for secure communication but does not enforce encryption across all channels by default. Monitoring & Auditing Gaps MCP lacks native logging and auditing capabilities. Organizations must integrate with external SIEM tools like Microsoft Sentinel for visibility. Dynamic Registration Risks Current implementations allow dynamic client registration without granular controls, enabling rogue client onboarding. Scalability Constraints Large-scale deployments require manual tuning for performance and security. There is no standardized approach for load balancing or high availability. Configuration Management Challenges Credentials often stored in plaintext within MCP config files. Lack of automated secret rotation or secure vault integration makes them vulnerable. Limited Standardization Across Vendors MCP is still evolving, and interoperability between different implementations is inconsistent, creating integration complexity. Mitigation Best Practices To reduce risk and strengthen MCP deployments: Enforce OAuth 2.1 with PKCE and strong RBAC. Use HTTPS/TLS for all MCP communications. Deploy MCP servers in isolated networks with private endpoints. Validate tools before integration; avoid untrusted sources. Integrate with Microsoft Defender for Cloud and Sentinel for monitoring. Encrypt and rotate credentials; never store in plaintext. Implement policy-as-code for configuration governance. MCP opens new possibilities for AI-driven automation, but without robust security, it can become an attack vector. Organizations must start with a secure baseline, continuously monitor, and adopt best practices to operationalize MCP safely.
Unified detection rule management
Hi, I attended the webinar yesterday regarding the new unified custom detection rules in Defender XDR. I was wondering about the management of a library of rules. As with any SOC, our solution has a library of custom rules which we manage in a release cycle for a number of clients in different Tenants. To avoid having to manage rules individually we use the JSON approach, importing the library so it will update rules that we need to tune. Currently I'm not seeing an option to import unified detection rules in Defender XDR via JSON. Is that a feature that will be added? Thanks ZivNew Blog Post | How to Query HaveIBeenPwned Using a Microsoft Sentinel Playbook
How to Query HaveIBeenPwned Using a Microsoft Sentinel Playbook - Azure Cloud & AI Domain Blog (azurecloudai.blog) I’ve known Troy Hunt for a number of years and his contributions to the security and privacy industry have been hugely valuable and much appreciated by the masses. HaveIBeenPwned is a great resource developed and maintained by Troy. It provides the ability to query against its database to expose domains or user accounts that have been caught up in any of the number of reported industry data breaches. Wouldn’t it be nice, then, to have this data available for your Microsoft Sentinel investigations? Fortunately, Troy provides an API for his service. I’ve provided a Microsoft Sentinel Playbook that takes email addresses associated with an Incident and submits them through the API and returns a quick note to the Comments tab in the Incident as to whether or not the email address(es) has been compromised.
Hunting for MFA manipulations in Entra ID tenants using KQL
The following article, Hunting for MFA manipulations in Entra ID tenants using KQL proved to be an invaluable resource in my search for an automated way to notify users of MFA modifications. I've adapted the KQL query to function within Defender Advanced Hunting or Azure Entra, my objective is to establish an alert that directly E-Mails the affected user, informing them of the MFA change and advising them to contact security if they did not initiate it. While the query runs correctly under Defender Advanced Hunting, I'm currently unable to create a workable custom alert because no "ReportId" is being captured. Despite consulting with Copilot, Gemini, CDW Support, and Microsoft Support, no workable solution has been achieved. Any insight would be greatly appreciated - Thank You! //Advanced Hunting query to parse modified: //StrongAuthenticationUserDetails (SAUD) //StrongAuthenticationMethod (SAM) let SearchWindow = 1h; let AuthenticationMethods = dynamic(["TwoWayVoiceMobile","TwoWaySms","TwoWayVoiceOffice","TwoWayVoiceOtherMobile","TwoWaySmsOtherMobile","OneWaySms","PhoneAppNotification","PhoneAppOTP"]); let AuthenticationMethodChanges = CloudAppEvents | where ActionType == "Update user." and RawEventData contains "StrongAuthenticationMethod" | extend Target = tostring(RawEventData.ObjectId) | extend Actor = tostring(RawEventData.UserId) | mv-expand ModifiedProperties = parse_json(RawEventData.ModifiedProperties) | where ModifiedProperties.Name == "StrongAuthenticationMethod" | project Timestamp,Actor,Target,ModifiedProperties,RawEventData,ReportId; let OldValues = AuthenticationMethodChanges | extend OldValue = parse_json(tostring(ModifiedProperties.OldValue)) | mv-apply OldValue on (extend Old_MethodType=tostring(OldValue.MethodType),Old_Default=tostring(OldValue.Default) | sort by Old_MethodType); let NewValues = AuthenticationMethodChanges | extend NewValue = parse_json(tostring(ModifiedProperties.NewValue)) | mv-apply NewValue on (extend New_MethodType=tostring(NewValue.MethodType),New_Default=tostring(NewValue.Default) | sort by New_MethodType); let RemovedMethods = AuthenticationMethodChanges | join kind=inner OldValues on ReportId | join kind=leftouter NewValues on ReportId,$left.Old_MethodType==$right.New_MethodType | where Old_MethodType != New_MethodType | extend Action = strcat("Removed (" , AuthenticationMethods[toint(Old_MethodType)], ") from Authentication Methods.") | extend ChangedValue = "Method Removed"; let AddedMethods = AuthenticationMethodChanges | join kind=inner NewValues on ReportId | join kind=leftouter OldValues on ReportId,$left.New_MethodType==$right.Old_MethodType | where Old_MethodType != New_MethodType | extend Action = strcat("Added (" , AuthenticationMethods[toint(New_MethodType)], ") as Authentication Method.") | extend ChangedValue = "Method Added"; let DefaultMethodChanges = AuthenticationMethodChanges | join kind=inner OldValues on ReportId | join kind=inner NewValues on ReportId | where Old_Default != New_Default and Old_MethodType == New_MethodType and New_Default == "true" | join kind=inner OldValues on ReportId | where Old_Default1 == "true" and Old_MethodType1 != New_MethodType | extend Old_MethodType = Old_MethodType1 | extend Action = strcat("Default Authentication Method was changed to (" , AuthenticationMethods[toint(New_MethodType)], ").") | extend ChangedValue = "Default Method"; let AuthenticationMethodReport = union RemovedMethods,AddedMethods,DefaultMethodChanges | project Timestamp,Action,Actor,Target,ChangedValue,OldValue=case(isempty(Old_MethodType), "",strcat(Old_MethodType,": ", AuthenticationMethods[toint(Old_MethodType)])),NewValue=case(isempty( New_MethodType),"", strcat(New_MethodType,": ", AuthenticationMethods[toint(New_MethodType)])); let AuthenticationDetailsChanges = CloudAppEvents | where ActionType == "Update user." and RawEventData contains "StrongAuthenticationUserDetails" | extend Target = tostring(RawEventData.ObjectId) | extend Actor = tostring(RawEventData.UserId) | extend ReportId= tostring(RawEventData.ReportId) | mvexpand ModifiedProperties = parse_json(RawEventData.ModifiedProperties) | where ModifiedProperties.Name == "StrongAuthenticationUserDetails" | extend NewValue = parse_json(replace_string(replace_string(tostring(ModifiedProperties.NewValue),"[",""),"]","")) | extend OldValue = parse_json(replace_string(replace_string(tostring(ModifiedProperties.OldValue),"[",""),"]","")) | mv-expand NewValue | mv-expand OldValue | where (tostring( bag_keys(OldValue)) == tostring(bag_keys(NewValue))) or (isempty(OldValue) and tostring(NewValue) !contains ":null") or (isempty(NewValue) and tostring(OldValue) !contains ":null") | extend ChangedValue = tostring(bag_keys(NewValue)[0]) | extend OldValue = tostring(parse_json(OldValue)[ChangedValue]) | extend NewValue = tostring(parse_json(NewValue)[ChangedValue]) | extend OldValue = case(ChangedValue == "PhoneNumber" or ChangedValue == "AlternativePhoneNumber", replace_strings(OldValue,dynamic([' ','(',')']), dynamic(['','',''])), OldValue ) | extend NewValue = case(ChangedValue == "PhoneNumber" or ChangedValue == "AlternativePhoneNumber", replace_strings(NewValue,dynamic([' ','(',')']), dynamic(['','',''])), NewValue ) | where tostring(OldValue) != tostring(NewValue) | extend Action = case(isempty(OldValue), strcat("Added new ",ChangedValue, " to Strong Authentication."),isempty(NewValue),strcat("Removed existing ",ChangedValue, " from Strong Authentication."),strcat("Changed ",ChangedValue," in Strong Authentication.")); union AuthenticationMethodReport, AuthenticationDetailsChanges | extend AccountUpn = Target | where Timestamp > ago(SearchWindow) //| summarize count() by Timestamp, Action, Actor, Target, ChangedValue, OldValue, NewValue, ReportId, AccountDisplayName, AccountId, AccountUpn | summarize arg_max(Timestamp, *) by Action | project Timestamp, Action, Actor, Target, ChangedValue, OldValue, NewValue, ReportId, AccountDisplayName, AccountId, AccountUpn | sort by Timestamp descMeet Your New Cybersecurity Sidekick - Microsoft Security Copilot Agents
Imagine if your security team had a super-smart assistant that never sleeps, learns from every task, and helps stop cyber threats before they become disasters. That’s exactly what Microsoft’s new Security Copilot Agents are designed to do. Why Do We Need Them? Cyberattacks are getting sneakier and faster many now use AI to trick people or break into systems. In fact, 67% of phishing attacks in 2024 used AI. Meanwhile, security teams are drowning in alerts 66 per day on average and 73% of experts admit they’ve missed important ones. That’s where Security Copilot comes in. It’s like having an AI-powered teammate that helps you investigate threats, fix issues, and stay ahead of attackers. What Are Security Copilot Agents? Think of these agents as mini digital coworkers. They’re not just chatbots they’re smart, adaptable tools that: Learn from your feedback Work with your existing Microsoft security tools Help you make faster, better decisions Keep you in control while they handle the heavy lifting They’re built to be flexible and smart unlike traditional automation that breaks when things change. Real-World Examples of What They Do Here are a few of the agents already available: Phishing Triage Agent: Automatically checks if a suspicious email is a real threat or just spam. It explains its reasoning in plain language and learns from your feedback. Alert Triage Agents (in Microsoft Purview): Helps prioritize which security alerts matter most, so your team can focus on the big stuff first. Conditional Access Optimization Agent (in Microsoft Entra): Keeps an eye on who has access to what and flags any gaps in your security policies. Vulnerability Remediation Agent (in Microsoft Intune): Spots the most urgent software vulnerabilities and tells you what to fix first. Threat Intelligence Briefing Agent: Gives you a quick, customized report on the latest threats that could affect your organization. Even More Help from Partners Microsoft is also teaming up with other companies to build even more agents. For example: OneTrust helps with privacy breach responses. Tanium helps analysts make faster decisions on alerts. Fletch helps reduce alert fatigue by showing what’s most important. Aviatrix helps diagnose network issues like VPN or gateway failures. BlueVoyant: helps to assess your SOC and recommends improvements. Why It Matters These agents don’t just save time they help your team stay ahead of threats, reduce stress, and focus on what really matters. They’re like having a team of AI-powered interns who never get tired and are always learning. Learn More 📢 Microsoft Security Blog: Security Copilot Agents Launch 🎥 https://aka.ms/SecurityCopilotAgentsVideo
How to create Playbook and automation rules for M365 Defender for Identity, Endpoint, Cloud Apps, an
How to create Playbook and automation rules for M365 Defender for Identity, Endpoint, Cloud Apps, and Data as we wanted to do some automation around it to let SOAR work on the alerts which are on "Low", "Medium" severity alerts? For example: if we have many alerts those should be verified by that respective automation rule and take the appropriate actions like close those alerts or mark as no action needed.
New Blog | How to use Log Analytics log data exported to Storage Accounts
By Simone Oor Introduction Exporting your logs from Sentinel or Log Analytics to Azure storage account blobs gives you low-cost long-term retention, as well as benefits such as immutability for legal hold, and geographical redundancy. But in the event of an incident, or perhaps a legal case, you may need the data archived away in those storage account blobs to help the investigation. Team during investigation How do you go about retrieving and analyzing that data? This blog will answer exactly that question. Hint, it does involve an Azure Data Explorer cluster. I will also briefly explain how data ends up in those blobs in the first place. Read the full post here: How to use Log Analytics log data exported to Storage Accounts
