Part 3: Unified Security Intelligence - Orchestrating GenAI Threat Detection with Microsoft Sentinel
Why Sentinel for GenAI Security Observability? Before diving into detection rules, let's address why Microsoft Sentinel is uniquely positioned for GenAI security operations—especially compared to traditional or non-native SIEMs. Native Azure Integration: Zero ETL Overhead The problem with external SIEMs: To monitor your GenAI workloads with a third-party SIEM, you need to: Configure log forwarding from Log Analytics to external systems Set up data connectors or agents for Azure OpenAI audit logs Create custom parsers for Azure-specific log schemas Maintain authentication and network connectivity between Azure and your SIEM Pay data egress costs for logs leaving Azure The Sentinel advantage: Your logs are already in Azure. Sentinel connects directly to: Log Analytics workspace - Where your Container Insights logs already flow Azure OpenAI audit logs - Native access without configuration Azure AD sign-in logs - Instant correlation with identity events Defender for Cloud alerts - Platform-level AI threat detection included Threat intelligence feeds - Microsoft's global threat data built-in Microsoft Defender XDR - AI-driven cybersecurity that unifies threat detection and response across endpoints, email, identities, cloud apps and Sentinel There's no data movement, no ETL pipelines, and no latency from log shipping. Your GenAI security data is queryable in real-time. KQL: Built for Complex Correlation at Scale Why this matters for GenAI: Detecting sophisticated AI attacks requires correlating: Application logs (your code from Part 2) Azure OpenAI service logs (API calls, token usage, throttling) Identity signals (who authenticated, from where) Threat intelligence (known malicious IPs) Defender for Cloud alerts (platform-level anomalies) KQL's advantage: Kusto Query Language is designed for this. You can: Join across multiple data sources in a single query Parse nested JSON (like your structured logs) natively Use time-series analysis functions for anomaly detection and behavior patterns Aggregate millions of events in seconds Extract entities (users, IPs, sessions) automatically for investigation graphs Example: Correlating your app logs with Azure AD sign-ins and Defender alerts takes 10 lines of KQL. In a traditional SIEM, this might require custom scripts, data normalization, and significantly slower performance. User Security Context Flows Natively Remember the user_security_context you pass in extra_body from Part 2? That context: Automatically appears in Azure OpenAI's audit logs Flows into Defender for Cloud AI alerts Is queryable in Sentinel without custom parsing Maps to the same identity schema as Azure AD logs With external SIEMs: You'd need to: Extract user context from your application logs Separately ingest Azure OpenAI logs Write correlation logic to match them Maintain entity resolution across different data sources With Sentinel: It just works. The end_user_id, source_ip, and application_name are already normalized across Azure services. Built-In AI Threat Detection Sentinel includes pre-built detections for cloud and AI workloads: Azure OpenAI anomalous access patterns (out of the box) Unusual token consumption (built-in analytics templates) Geographic anomalies (using Azure's global IP intelligence) Impossible travel detection (cross-referencing sign-ins with AI API calls) Microsoft Defender XDR (correlation with endpoint, email, cloud app signals) These aren't generic "high volume" alerts—they're tuned for Azure AI services by Microsoft's security research team. You can use them as-is or customize them with your application-specific context. Entity Behavior Analytics (UEBA) Sentinel's UEBA automatically builds baselines for: Normal request volumes per user Typical request patterns per application Expected geographic access locations Standard model usage patterns Then it surfaces anomalies: "User_12345 normally makes 10 requests/day, suddenly made 500 in an hour" "Application_A typically uses GPT-3.5, suddenly switched to GPT-4 exclusively" "User authenticated from Seattle, made AI requests from Moscow 10 minutes later" This behavior modeling happens automatically—no custom ML model training required. Traditional SIEMs would require you to build this logic yourself. The Bottom Line For GenAI security on Azure: Sentinel reduces time-to-detection because data is already there Correlation is simpler because everything speaks the same language Investigation is faster because entities are automatically linked Cost is lower because you're not paying data egress fees Maintenance is minimal because connectors are native If your GenAI workloads are on Azure, using anything other than Sentinel means fighting against the platform instead of leveraging it. From Logs to Intelligence: The Complete Picture Your structured logs from Part 2 are flowing into Log Analytics. Here's what they look like: { "timestamp": "2025-10-21T14:32:17.234Z", "level": "INFO", "message": "LLM Request Received", "request_id": "a7c3e9f1-4b2d-4a8e-9c1f-3e5d7a9b2c4f", "session_id": "550e8400-e29b-41d4-a716-446655440000", "prompt_hash": "d3b07384d113edec49eaa6238ad5ff00", "security_check_passed": "PASS", "source_ip": "203.0.113.42", "end_user_id": "user_550e8400", "application_name": "AOAI-Customer-Support-Bot", "model_deployment": "gpt-4-turbo" } These logs are in the ContainerLogv2 table since our application “AOAI-Customer-Support-Bot” is running on Azure Kubernetes Services (AKS). Steps to Setup AKS to stream logs to Sentinel/Log Analytics From Azure portal, navigate to your AKS, then to Monitoring -> Insights Select Monitor Settings Under Container Logs Select the Sentinel-enabled Log Analytics workspace Select Logs and events Check the ‘Enable ContainerLogV2’ and ‘Enable Syslog collection’ options More details can be found at this link Kubernetes monitoring in Azure Monitor - Azure Monitor | Microsoft Learn Critical Analytics Rules: What to Detect and Why Rule 1: Prompt Injection Attack Detection Why it matters: Prompt injection is the GenAI equivalent of SQL injection. Attackers try to manipulate the model by overriding system instructions. Multiple attempts indicate intentional malicious behavior. What to detect: 3+ prompt injection attempts within 10 minutes from similar IP let timeframe = 1d; let threshold = 3; AlertEvidence | where TimeGenerated >= ago(timeframe) and EntityType == "Ip" | where DetectionSource == "Microsoft Defender for AI Services" | where Title contains "jailbreak" or Title contains "prompt injection" | summarize count() by bin (TimeGenerated, 1d), RemoteIP | where count_ >= threshold What the SOC sees: User identity attempting injection Source IP and geographic location Sample prompts for investigation Frequency indicating automation vs. manual attempts Severity: High (these are actual attempts to bypass security) Rule 2: Content Safety Filter Violations Why it matters: When Azure AI Content Safety blocks a request, it means harmful content (violence, hate speech, etc.) was detected. Multiple violations indicate intentional abuse or a compromised account. What to detect: Users with 3+ content safety violations in a 1 hour block during a 24 hour time period. let timeframe = 1d; let threshold = 3; ContainerLogV2 | where TimeGenerated >= ago(timeframe) | where isnotempty(LogMessage.end_user_id) | where LogMessage.security_check_passed == "FAIL" | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | summarize count() by bin(TimeGenerated, 1h),source_ip,end_user_id,session_id,Computer,application_name,security_check_passed | where count_ >= threshold What the SOC sees: Severity based on violation count Time span showing if it's persistent vs. isolated Prompt samples (first 80 chars) for context Session ID for conversation history review Severity: High (these are actual harmful content attempts) Rule 3: Rate Limit Abuse Why it matters: Persistent rate limit violations indicate automated attacks, credential stuffing, or attempts to overwhelm the system. Legitimate users who hit rate limits don't retry 10+ times in minutes. What to detect: Users blocked by rate limiter 5+ times in 10 minutes let timeframe = 1h; let threshold = 5; AzureDiagnostics | where ResourceProvider == "MICROSOFT.COGNITIVESERVICES" | where OperationName == "Completions" or OperationName contains "ChatCompletions" | extend tokensUsed = todouble(parse_json(properties_s).usage.total_tokens) | summarize totalTokens = sum(tokensUsed), requests = count(), rateLimitErrors = countif(httpstatuscode_s == "429") by bin(TimeGenerated, 1h) | where count_ >= threshold What the SOC sees: Whether it's a bot (immediate retries) or human (gradual retries) Duration of attack Which application is targeted Correlation with other security events from same user/IP Severity: Medium (nuisance attack, possible reconnaissance) Rule 4: Anomalous Source IP for User Why it matters: A user suddenly accessing from a new country or VPN could indicate account compromise. This is especially critical for privileged accounts or after-hours access. What to detect: User accessing from an IP never seen in the last 7 days let lookback = 7d; let recent = 1h; let baseline = IdentityLogonEvents | where Timestamp between (ago(lookback + recent) .. ago(recent)) | where isnotempty(IPAddress) | summarize knownIPs = make_set(IPAddress) by AccountUpn; ContainerLogV2 | where TimeGenerated >= ago(recent) | where isnotempty(LogMessage.source_ip) | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | extend full_prompt_sample = tostring (LogMessage.full_prompt_sample) | lookup baseline on $left.AccountUpn == $right.end_user_id | where isnull(knownIPs) or IPAddress !in (knownIPs) | project TimeGenerated, source_ip, end_user_id, session_id, Computer, application_name, security_check_passed, full_prompt_sample What the SOC sees: User identity and new IP address Geographic location change Whether suspicious prompts accompanied the new IP Timing (after-hours access is higher risk) Severity: Medium (environment compromise, reconnaissance) Rule 5: Coordinated Attack - Same Prompt from Multiple Users Why it matters: When 5+ users send identical prompts, it indicates a bot network, credential stuffing, or organized attack campaign. This is not normal user behavior. What to detect: Same prompt hash from 5+ different users within 1 hour let timeframe = 1h; let threshold = 5; ContainerLogV2 | where TimeGenerated >= ago(timeframe) | where isnotempty(LogMessage.prompt_hash) | where isnotempty(LogMessage.end_user_id) | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend prompt_hash=tostring(LogMessage.prompt_hash) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | project TimeGenerated, prompt_hash, source_ip, end_user_id, application_name, security_check_passed | summarize DistinctUsers = dcount(end_user_id), Attempts = count(), Users = make_set(end_user_id, 100), IpAddress = make_set(source_ip, 100) by prompt_hash, bin(TimeGenerated, 1h) | where DistinctUsers >= threshold What the SOC sees: Attack pattern (single attacker with stolen accounts vs. botnet) List of compromised user accounts Source IPs for blocking Prompt sample to understand attack goal Severity: High (indicates organized attack) Rule 6: Malicious model detected Why it matters: Model serialization attacks can lead to serious compromise. When Defender for Cloud Model Scanning identifies issues with a custom or opensource model that is part of Azure ML Workspace, Registry, or hosted in Foundry, that may be or may not be a user oversight. What to detect: Model scan results from Defender for Cloud and if it is being actively used. What the SOC sees: Malicious model Applications leveraging the model Source IPs and users accessed the model Severity: Medium (can be user oversight) Advanced Correlation: Connecting the Dots The power of Sentinel is correlating your application logs with other security signals. Here are the most valuable correlations: Correlation 1: Failed GenAI Requests + Failed Sign-Ins = Compromised Account Why: Account showing both authentication failures and malicious AI prompts is likely compromised within a 1 hour timeframe l let timeframe = 1h; ContainerLogV2 | where TimeGenerated >= ago(timeframe) | where isnotempty(LogMessage.source_ip) | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | extend full_prompt_sample = tostring (LogMessage.full_prompt_sample) | extend message = tostring (LogMessage.message) | where security_check_passed == "FAIL" or message contains "WARNING" | join kind=inner ( SigninLogs | where ResultType != 0 // 0 means success, non-zero indicates failure | project TimeGenerated, UserPrincipalName, ResultType, ResultDescription, IPAddress, Location, AppDisplayName ) on $left.end_user_id == $right.UserPrincipalName | project TimeGenerated, source_ip, end_user_id, application_name, full_prompt_sample, prompt_hash, message, security_check_passed Severity: High (High probability of compromise) Correlation 2: Application Logs + Defender for Cloud AI Alerts Why: Defender for Cloud AI Threat Protection detects platform-level threats (unusual API patterns, data exfiltration attempts). When both your code and the platform flag the same user, confidence is very high. let timeframe = 1h; ContainerLogV2 | where TimeGenerated >= ago(timeframe) | where isnotempty(LogMessage.source_ip) | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | extend full_prompt_sample = tostring (LogMessage.full_prompt_sample) | extend message = tostring (LogMessage.message) | where security_check_passed == "FAIL" or message contains "WARNING" | join kind=inner ( AlertEvidence | where TimeGenerated >= ago(timeframe) and AdditionalFields.Asset == "true" | where DetectionSource == "Microsoft Defender for AI Services" | project TimeGenerated, Title, CloudResource ) on $left.application_name == $right.CloudResource | project TimeGenerated, application_name, end_user_id, source_ip, Title Severity: Critical (Multi-layer detection) Correlation 3: Source IP + Threat Intelligence Feeds Why: If requests come from known malicious IPs (C2 servers, VPN exit nodes used in attacks), treat them as high priority even if behavior seems normal. //This rule correlates GenAI app activity with Microsoft Threat Intelligence feed available in Sentinel and Microsoft XDR for malicious IP IOCs let timeframe = 10m; ContainerLogV2 | where TimeGenerated >= ago(timeframe) | where isnotempty(LogMessage.source_ip) | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | extend full_prompt_sample = tostring (LogMessage.full_prompt_sample) | join kind=inner ( ThreatIntelIndicators | where IsActive == "true" | where ObservableKey startswith "ipv4-addr" or ObservableKey startswith "network-traffic" | project IndicatorIP = ObservableValue ) on $left.source_ip == $right.IndicatorIP | project TimeGenerated, source_ip, end_user_id, application_name, full_prompt_sample, security_check_passed Severity: High (Known bad actor) Workbooks: What Your SOC Needs to See Executive Dashboard: GenAI Security Health Purpose: Leadership wants to know: "Are we secure?" Answer with metrics. Key visualizations: Security Status Tiles (24 hours) Total Requests Success Rate Blocked Threats (Self detected + Content Safety + Threat Protection for AI) Rate Limit Violations Model Security Score (Red Team evaluation status of currently deployed model) ContainerLogV2 | where TimeGenerated > ago (1d) | extend security_check_passed = tostring (LogMessage.security_check_passed) | summarize SuccessCount=countif(security_check_passed == "PASS"), FailedCount=countif(security_check_passed == "FAIL") by bin(TimeGenerated, 1h) | extend TotalRequests = SuccessCount + FailedCount | extend SuccessRate = todouble(SuccessCount)/todouble(TotalRequests) * 100 | order by SuccessRate 1. Trend Chart: Pass vs. Fail Over Time Shows if attack volume is increasing Identifies attack time windows Validates that defenses are working ContainerLogV2 | where TimeGenerated > ago (14d) | extend security_check_passed = tostring (LogMessage.security_check_passed) | summarize SuccessCount=countif(security_check_passed == "PASS"), FailedCount=countif(security_check_passed == "FAIL") by bin(TimeGenerated, 1d) | render timechart 2. Top 10 Users by Security Events Bar chart of users with most failures ContainerLogV2 | where TimeGenerated > ago (1d) | where isnotempty(LogMessage.end_user_id) | extend end_user_id=tostring(LogMessage.end_user_id) | extend security_check_passed = tostring (LogMessage.security_check_passed) | where security_check_passed == "FAIL" | summarize FailureCount = count() by end_user_id | top 20 by FailureCount | render barchart Applications with most failures ContainerLogV2 | where TimeGenerated > ago (1d) | where isnotempty(LogMessage.application_name) | extend application_name=tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | where security_check_passed == "FAIL" | summarize FailureCount = count() by application_name | top 20 by FailureCount | render barchart 3. Geographic Threat Map Where are attacks originating? Useful for geo-blocking decisions ContainerLogV2 | where TimeGenerated > ago (1d) | where isnotempty(LogMessage.application_name) | extend application_name=tostring(LogMessage.application_name) | extend source_ip=tostring(LogMessage.source_ip) | extend security_check_passed = tostring (LogMessage.security_check_passed) | where security_check_passed == "FAIL" | extend GeoInfo = geo_info_from_ip_address(source_ip) | project sourceip, GeoInfo.counrty, GeoInfo.city Analyst Deep-Dive: User Behavior Analysis Purpose: SOC analyst investigating a specific user or session Key components: 1. User Activity Timeline Every request from the user in time order ContainerLogV2 | where isnotempty(LogMessage.end_user_id) | project TimeGenerated, LogMessage.source_ip, LogMessage.end_user_id, LogMessage. session_id, Computer, LogMessage.application_name, LogMessage.request_id, LogMessage.message, LogMessage.full_prompt_sample | order by tostring(LogMessage_end_user_id), TimeGenerated Color-coded by security status AlertInfo | where DetectionSource == "Microsoft Defender for AI Services" | project TimeGenerated, AlertId, Title, Category, Severity, SeverityColor = case( Severity == "High", "🔴 High", Severity == "Medium", "🟠 Medium", Severity == "Low", "🟢 Low", "⚪ Unknown" ) 2. Session Analysis Table All sessions for the user ContainerLogV2 | where TimeGenerated > ago (1d) | where isnotempty(LogMessage.end_user_id) | extend end_user_id=tostring(LogMessage.end_user_id) | where end_user_id == "<username>" // Replace with actual username | extend application_name=tostring(LogMessage.application_name) | extend source_ip=tostring(LogMessage.source_ip) | extend session_id=tostri1ng(LogMessage.session_id) | extend security_check_passed = tostring (LogMessage.security_check_passed) | project TimeGenerated, session_id, end_user_id, application_name, security_check_passed Failed requests per session ContainerLogV2 | where TimeGenerated > ago (1d) | extend security_check_passed = tostring (LogMessage.security_check_passed) | where security_check_passed == "FAIL" | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend security_check_passed = tostring (LogMessage.security_check_passed) | summarize Failed_Sessions = count() by end_user_id, session_id | order by Failed_Sessions Session duration ContainerLogV2 | where TimeGenerated > ago (1d) | where isnotempty(LogMessage.session_id) | extend security_check_passed = tostring (LogMessage.security_check_passed) | where security_check_passed == "PASS" | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name=tostring(LogMessage.application_name) | extend source_ip=tostring(LogMessage.source_ip) | summarize Start=min(TimeGenerated), End=max(TimeGenerated), count() by end_user_id, session_id, source_ip, application_name | extend DurationSeconds = datetime_diff("second", End, Start) 3. Prompt Pattern Detection Unique prompts by hash Frequency of each pattern Detect if user is fuzzing/testing boundaries Sample query for user investigation: ContainerLogV2 | where TimeGenerated > ago (14d) | where isnotempty(LogMessage.prompt_hash) | where isnotempty(LogMessage.full_prompt_sample) | extend prompt_hash=tostring(LogMessage.prompt_hash) | extend full_prompt_sample=tostring(LogMessage.full_prompt_sample) | extend application_name=tostring(LogMessage.application_name) | summarize count() by prompt_hash, full_prompt_sample | order by count_ Threat Hunting Dashboard: Proactive Detection Purpose: Find threats before they trigger alerts Key queries: 1. Suspicious Keywords in Prompts (e.g. Ignore, Disregard, system prompt, instructions, DAN, jailbreak, pretend, roleplay) let suspicious_prompts = externaldata (content_policy:int, content_policy_name:string, q_id:int, question:string) [ @"https://raw.githubusercontent.com/verazuo/jailbreak_llms/refs/heads/main/data/forbidden_question/forbidden_question_set.csv"] with (format="csv", has_header_row=true, ignoreFirstRecord=true); ContainerLogV2 | where TimeGenerated > ago (14d) | where isnotempty(LogMessage.full_prompt_sample) | extend full_prompt_sample=tostring(LogMessage.full_prompt_sample) | where full_prompt_sample in (suspicious_prompts) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name=tostring(LogMessage.application_name) | extend source_ip=tostring(LogMessage.source_ip) | project TimeGenerated, session_id, end_user_id, source_ip, application_name, full_prompt_sample 2. High-Volume Anomalies User sending too many requests by a IP or User. Assuming that Foundry Projects are configured to use Azure AD and not API Keys. //50+ requests in 1 hour let timeframe = 1h; let threshold = 50; AzureDiagnostics | where ResourceProvider == "MICROSOFT.COGNITIVESERVICES" | where OperationName == "Completions" or OperationName contains "ChatCompletions" | extend tokensUsed = todouble(parse_json(properties_s).usage.total_tokens) | summarize totalTokens = sum(tokensUsed), requests = count() by bin(TimeGenerated, 1h),CallerIPAddress | where count_ >= threshold 3. Rare Failures (Novel Attack Detection) Rare failures might indicate zero-day prompts or new attack techniques //10 or more failures in 24 hours ContainerLogV2 | where TimeGenerated >= ago (24h) | where isnotempty(LogMessage.security_check_passed) | extend security_check_passed=tostring(LogMessage.security_check_passed) | where security_check_passed == "FAIL" | extend application_name=tostring(LogMessage.application_name) | extend end_user_id=tostring(LogMessage.end_user_id) | extend source_ip=tostring(LogMessage.source_ip) | summarize FailedAttempts = count(), FirstAttempt=min(TimeGenerated), LastAttempt=max(TimeGenerated) by application_name | extend DurationHours = datetime_diff('hour', LastAttempt, FirstAttempt) | where DurationHours >= 24 and FailedAttempts >=10 | project application_name, FirstAttempt, LastAttempt, DurationHours, FailedAttempts Measuring Success: Security Operations Metrics Key Performance Indicators Mean Time to Detect (MTTD): let AppLog = ContainerLogV2 | extend application_name=tostring(LogMessage.application_name) | extend security_check_passed=tostring (LogMessage.security_check_passed) | extend session_id=tostring(LogMessage.session_id) | extend end_user_id=tostring(LogMessage.end_user_id) | extend source_ip=tostring(LogMessage.source_ip) | where security_check_passed=="FAIL" | summarize FirstLogTime=min(TimeGenerated) by application_name, session_id, end_user_id, source_ip; let Alert = AlertEvidence | where DetectionSource == "Microsoft Defender for AI Services" | extend end_user_id = tostring(AdditionalFields.AadUserId) | extend source_ip=RemoteIP | extend application_name=CloudResource | summarize FirstAlertTime=min(TimeGenerated) by AlertId, Title, application_name, end_user_id, source_ip; AppLog | join kind=inner (Alert) on application_name, end_user_id, source_ip | extend DetectionDelayMinutes=datetime_diff('minute', FirstAlertTime, FirstLogTime) | summarize MTTD_Minutes=round(avg (DetectionDelayMinutes),2) by AlertId, Title Target: <= 15 minutes from first malicious activity to alert Mean Time to Respond (MTTR): SecurityIncident | where Status in ("New", "Active") | where CreatedTime >= ago(14d) | extend ResponseDelay = datetime_diff('minute', LastActivityTime, FirstActivityTime) | summarize MTTR_Minutes = round (avg (ResponseDelay),2) by CreatedTime, IncidentNumber | order by CreatedTime, IncidentNumber asc Target: < 4 hours from alert to remediation Threat Detection Rate: ContainerLogV2 | where TimeGenerated > ago (1d) | extend security_check_passed = tostring (LogMessage.security_check_passed) | summarize SuccessCount=countif(security_check_passed == "PASS"), FailedCount=countif(security_check_passed == "FAIL") by bin(TimeGenerated, 1h) | extend TotalRequests = SuccessCount + FailedCount | extend SuccessRate = todouble(SuccessCount)/todouble(TotalRequests) * 100 | order by SuccessRate Context: 1-3% is typical for production systems (most traffic is legitimate) What You've Built By implementing the logging from Part 2 and the analytics rules in this post, your SOC now has: ✅ Real-time threat detection - Alerts fire within minutes of malicious activity ✅ User attribution - Every incident has identity, IP, and application context ✅ Pattern recognition - Detect both volume-based and behavior-based attacks ✅ Correlation across layers - Application logs + platform alerts + identity signals ✅ Proactive hunting - Dashboards for finding threats before they trigger rules ✅ Executive visibility - Metrics showing program effectiveness Key Takeaways GenAI threats need GenAI-specific analytics - Generic rules miss context like prompt injection, content safety violations, and session-based attacks Correlation is critical - The most sophisticated attacks span multiple signals. Correlating app logs with identity and platform alerts catches what individual rules miss. User context from Part 2 pays off - end_user_id, source_ip, and session_id enable investigation and response at scale Prompt hashing enables pattern detection - Detect repeated attacks without storing sensitive prompt content Workbooks serve different audiences - Executives want metrics; analysts want investigation tools; hunters want anomaly detection Start with high-fidelity rules - Content Safety violations and rate limit abuse have very low false positive rates. Add behavioral rules after establishing baselines. What's Next: Closing the Loop You've now built detection and visibility. In Part 4, we'll close the security operations loop with: Part 4: Platform Integration and Automated Response Building SOAR playbooks for automated incident response Implementing automated key rotation with Azure Key Vault Blocking identities in Entra Creating feedback loops from incidents to code improvements The journey from blind spot to full security operations capability is almost complete. Previous: Part 1: Securing GenAI Workloads in Azure: A Complete Guide to Monitoring and Threat Protection - AIO11Y | Microsoft Community Hub Part 2: Part 2: Building Security Observability Into Your Code - Defensive Programming for Azure OpenAI | Microsoft Community Hub Next: Part 4: Platform Integration and Automated Response (Coming soon)Announcing AI Entity Analyzer in Microsoft Sentinel MCP Server - Public Preview
What is the Entity Analyzer? Assessing the risk of entities is a core task for SOC teams - whether triaging incidents, investigating threats, or automating response workflows. Traditionally, this has required building complex playbooks or custom logic to gather and analyze fragmented security data from multiple sources. With Entity Analyzer, this complexity starts to fade away. The tool leverages your organization’s security data in Sentinel to deliver comprehensive, reasoned risk assessments for any entity you encounter - starting with users and urls. By providing this unified, out-of-the-box solution for entity analysis, Entity Analyzer also enables the AI agents you build to make smarter decisions and automate more tasks - without the need to manually engineer risk evaluation logic for each entity type. And for those building SOAR workflows, Entity Analyzer is natively integrated with Logic Apps, making it easy to enrich incidents and automate verdicts within your playbooks. *Entity Analyzer is rolling out in Public Preview to Sentinel MCP server and within Logic Apps starting today. Learn more here. Deep Dive: How the User Analyzer is already solving problems for security teams Problem: Drowning in identity alerts Security operations centers (SOCs) are inundated with identity-based threats and alert noise. Triaging these alerts requires analyzing numerous data sources across sign-in logs, cloud app events, identity info, behavior analytics, threat intel, and more, all in tandem with each other to reach a verdict - something very challenging to do without a human in the loop today. So, we introduced the User Analyzer, a specialized analyzer that unifies, correlates, and analyzes user activity across all these security data sources. Government of Nunavut: solving identity alert overload with User Analyzer Hear the below from Arshad Sheikh, Security Expert at Government of Nunavut, on how they're using the User Analyzer today: How it's making a difference "Before the User Analyzer, when we received identity alerts we had to check a large amount of data related to users’ activity (user agents, anomalies, IP reputation, etc.). We had to write queries, wait for them to run, and then manually reason over the results. We attempted to automate some of this, but maintaining and updating that retrieval, parsing, and reasoning automation was difficult and we didn’t have the resources to support it. With the User Analyzer, we now have a plug-and-play solution that represents a step toward the AI-driven automation of the future. It gathers all the context such as what the anomalies are and presents it to our analysts so they can make quick, confident decisions, eliminating the time previously spent manually gathering this data from portals." Solving a real problem "For example, every 24 hours we create a low severity incident of our users who successfully sign-in to our network non interactively from outside of our GEO fence. This type of activity is not high-enough fidelity to auto-disable, requiring us to manually analyze the flagged users each time. But with User Analyzer, this analysis is performed automatically. The User Analyzer has also significantly reduced the time required to determine whether identity-based incidents like these are false positives or true positives. Instead of spending around 20 minutes investigating each incident, our analysts can now reach a conclusion in about 5 minutes using the automatically generated summary." Looking ahead "Looking ahead, we see even more potential. In the future, the User Analyzer could be integrated directly with Microsoft Sentinel playbooks to take automated, definitive action such as blocking user or device access based on the analyzer’s results. This would further streamline our incident response and move us closer to fully automated security operations." Want similar benefits in your SOC? Get started with our Entity Analyzer Logic Apps template here. User Analyzer architecture: how does it work? Let’s take a look at how the User Analyzer works. The User Analyzer aggregates and correlates signals from multiple data sources to deliver a comprehensive analysis, enabling informed actions based on user activity. The diagram below gives an overview of this architecture: Step 1: Retrieve Data The analyzer starts by retrieving relevant data from the following sources: Sign-In Logs (Interactive & Non-Interactive): Tracks authentication and login activity. Security Alerts: Alerts from Microsoft Defender solutions. Behavior Analytics: Surfaces behavioral anomalies through advanced analytics. Cloud App Events: Captures activity from Microsoft Defender for Cloud Apps. Identity Information: Enriches user context with identity records. Microsoft Threat Intelligence: Enriches IP addresses with Microsoft Threat Intelligence. Steps 2: Correlate signals Signals are correlated using identifiers such as user IDs, IP addresses, and threat intelligence. Rather than treating each alert or behavior in isolation, the User Analyzer fuses signals to build a holistic risk profile. Step 3: AI-based reasoning In the User Analyzer, multiple AI-powered agents collaborate to evaluate the evidence and reach consensus. This architecture not only improves accuracy and reduces bias in verdicts, but also provides transparent, justifiable decisions. Leveraging AI within the User Analyzer introduces a new dimension of intelligence to threat detection. Instead of relying on static signatures or rigid regex rules, AI-based reasoning can uncover subtle anomalies that traditional detection methods and automation playbooks often miss. For example, an attacker might try to evade detection by slightly altering a user-agent string or by targeting and exfiltrating only a few files of specific types. While these changes could bypass conventional pattern matching, an AI-powered analyzer understands the semantic context and behavioral patterns behind these artifacts, allowing it to flag suspicious deviations even when the syntax looks benign. Step 4: Verdict & analysis Each user is given a verdict. The analyzer outputs any of the following verdicts based on the analysis: Compromised Suspicious activity found No evidence of compromise Based on the verdict, a corresponding recommendation is given. This helps teams make an informed decision whether action should be taken against the user. *AI-generated content from the User Analyzer may be incorrect - check it for accuracy. User Analyzer Example Output See the following example output from the user analyzer within an incident comment: *IP addresses have been redacted for this blog* &CK techniques, a list of malicious IP addresses the user signed in from (redacted for this blog), and a few suspicious user agents the user's activity originated from. Conclusion Entity Analyzer in Microsoft Sentinel MCP server represents a leap forward in alert triage & analysis. By correlating signals and harnessing AI-based reasoning, it empowers SOC teams to act on investigations with greater speed, precision, and confidence.
Latest Threat Intelligence (November 2025)
Microsoft Defender for IoT has released the November 2025 Threat Intelligence package. The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file). Threat Intelligence updates reflect the combined impact of proprietary research and threat intelligence carried out by Microsoft security teams. Each package contains the latest CVEs (Common Vulnerabilities and Exposures), IOCs (Indicators of Compromise), and other indicators applicable to IoT/ICS/OT networks (published during the past month) researched and implemented by Microsoft Threat Intelligence Research - CPS. The CVE scores are aligned with the National Vulnerability Database (NVD). Starting with the August 2023 threat intelligence updates, CVSSv3 scores are shown if they are relevant; otherwise the CVSSv2 scores are shown. Guidance Customers are recommended to update their systems with the latest TI package in order to detect potential exposure risks and vulnerabilities in their networks and on their devices. Threat Intelligence packages are updated every month with the most up-to-date security information available, ensuring that Microsoft Defender for IoT can identify malicious actors and behaviors on devices. Update your system with the latest TI package The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file), for more information, please review Update threat intelligence data | Microsoft Docs. MD5 Hash: 0ed5b864101c471d987b332fc8619551 For cloud connected sensors, Microsoft Defender for IoT can automatically update new threat intelligence packages following their release, click here for more information.Latest Threat Intelligence (February 2025)
Microsoft Defender for IoT has released the February 2025 Threat Intelligence package. The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file). Threat Intelligence updates reflect the combined impact of proprietary research and threat intelligence carried out by Microsoft security teams. Each package contains the latest CVEs (Common Vulnerabilities and Exposures), IOCs (Indicators of Compromise), and other indicators applicable to IoT/ICS/OT networks (published during the past month) researched and implemented by Microsoft Threat Intelligence Research - CPS. The CVE scores are aligned with the National Vulnerability Database (NVD). Starting with the August 2023 threat intelligence updates, CVSSv3 scores are shown if they are relevant; otherwise the CVSSv2 scores are shown. Guidance Customers are recommended to update their systems with the latest TI package in order to detect potential exposure risks and vulnerabilities in their networks and on their devices. Threat Intelligence packages are updated every month with the most up-to-date security information available, ensuring that Microsoft Defender for IoT can identify malicious actors and behaviors on devices. Update your system with the latest TI package The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file), for more information, please review Update threat intelligence data | Microsoft Docs. MD5 Hash: 5b052ee069d62916b55fc0aa24e47114 For cloud connected sensors, Microsoft Defender for IoT can automatically update new threat intelligence packages following their release, click here for more information.Latest Threat Intelligence (August 2024)
Microsoft Defender for IoT has released the August 2024 Threat Intelligence package. The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file). Threat Intelligence updates reflect the combined impact of proprietary research and threat intelligence carried out by Microsoft security teams. Each package contains the latest CVEs (Common Vulnerabilities and Exposures), IOCs (Indicators of Compromise), and other indicators applicable to IoT/ICS/OT networks (published during the past month) researched and implemented by Microsoft Threat Intelligence Research - CPS. The CVE scores are aligned with the National Vulnerability Database (NVD). Starting with the August 2023 threat intelligence updates, CVSSv3 scores are shown if they are relevant; otherwise the CVSSv2 scores are shown. Guidance Customers are recommended to update their systems with the latest TI package in order to detect potential exposure risks and vulnerabilities in their networks and on their devices. Threat Intelligence packages are updated every month with the most up-to-date security information available, ensuring that Microsoft Defender for IoT can identify malicious actors and behaviors on devices. Update your system with the latest TI package The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file), for more information, please review Update threat intelligence data | Microsoft Docs. MD5 Hash: 1b0b3742c28c70692504dcf88d63d54c For cloud connected sensors, Microsoft Defender for IoT can automatically update new threat intelligence packages following their release, click here for more information.Latest Threat Intelligence (June 2024)
Microsoft Defender for IoT has released the June 2024 Threat Intelligence package. The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file). Threat Intelligence updates reflect the combined impact of proprietary research and threat intelligence carried out by Microsoft security teams. Each package contains the latest CVEs (Common Vulnerabilities and Exposures), IOCs (Indicators of Compromise), and other indicators applicable to IoT/ICS/OT networks (published during the past month) researched and implemented by Microsoft Threat Intelligence Research - CPS. The CVE scores are aligned with the National Vulnerability Database (NVD). Starting with the August 2023 threat intelligence updates, CVSSv3 scores are shown if they are relevant; otherwise the CVSSv2 scores are shown. Guidance Customers are recommended to update their systems with the latest TI package in order to detect potential exposure risks and vulnerabilities in their networks and on their devices. Threat Intelligence packages are updated every month with the most up-to-date security information available, ensuring that Microsoft Defender for IoT can identify malicious actors and behaviors on devices. Update your system with the latest TI package The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file), for more information, please review Update threat intelligence data | Microsoft Docs. MD5 Hash: dcdd8a2d48f81aa4df4af4c9a14652d0 For cloud connected sensors, Microsoft Defender for IoT can automatically update new threat intelligence packages following their release, click here for more information.Latest Threat Intelligence (April 2024)
Microsoft Defender for IoT has released the April 2024 Threat Intelligence package. The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file). Threat Intelligence updates reflect the combined impact of proprietary research and threat intelligence carried out by Microsoft security teams. Each package contains the latest CVEs (Common Vulnerabilities and Exposures), IOCs (Indicators of Compromise), and other indicators applicable to IoT/ICS/OT networks (published during the past month) researched and implemented by Microsoft Threat Intelligence Research - CPS. The CVE scores are aligned with the National Vulnerability Database (NVD). Starting with the August 2023 threat intelligence updates, CVSSv3 scores are shown if they are relevant; otherwise the CVSSv2 scores are shown. Guidance Customers are recommended to update their systems with the latest TI package in order to detect potential exposure risks and vulnerabilities in their networks and on their devices. Threat Intelligence packages are updated every month with the most up-to-date security information available, ensuring that Microsoft Defender for IoT can identify malicious actors and behaviors on devices. Update your system with the latest TI package The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file), for more information, please review Update threat intelligence data | Microsoft Docs. MD5 Hash: 58e727f2e7c5d004b27613bac3a49314 For cloud connected sensors, Microsoft Defender for IoT can automatically update new threat intelligence packages following their release, click here for more information.Latest Threat Intelligence (August 2023)
Microsoft Defender for IoT has released the August 2023 Threat Intelligence package. The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file). Threat Intelligence updates reflect the combined impact of proprietary research and threat intelligence carried out by Microsoft security teams. Each package contains the latest CVEs (Common Vulnerabilities and Exposures), IOCs (Indicators of Compromise), and other indicators applicable to IoT/ICS/OT networks (published during the past month) researched and implemented by Microsoft Threat Intelligence Research - CPS. The CVE scores are aligned with the National Vulnerability Database (NVD). Starting with the August threat intelligence updates, CVSSv3 scores are shown if they are relevant; otherwise the CVSSv2 scores are shown. Guidance Customers are recommended to update their systems with the latest TI package in order to detect potential exposure risks and vulnerabilities in their networks and on their devices. Threat Intelligence packages are updated every month with the most up-to-date security information available, ensuring that Microsoft Defender for IoT can identify malicious actors and behaviors on devices. Update your system with the latest TI package The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file), for more information, please review Update threat intelligence data | Microsoft Docs. MD5 Hash: e04c8c5c4837f99dfd4b41d448bf5e92 For cloud connected sensors, Microsoft Defender for IoT can automatically update new threat intelligence packages following their release, click here for more information.
Microsoft Defender for IoT - New Release (OT v22.3.4)
Microsoft Defender for IoT is excited to announce a new major release of OT sensor version (22.3.4). To learn more, visit Defender for IoT Release Notes | Microsoft Docs Download links available at Defender for IoT Management Portal - Microsoft Azure. What's New? Service area Updates OT networks Version 22.3.4: Azure connectivity status shown on OT sensors MD5 Hash - f781734c1b8e2baf94f7a1fd6508df79 About Defender for IoT Microsoft Defender for IoT provides agentless, network-layer security, provides security for diverse industrial equipment, and interoperates with Microsoft Sentinel and other SOC tools. Continuous asset discovery, vulnerability management, and threat detection for Internet of Things (IoT) devices, operational technology (OT) and Industrial Control Systems (ICS) can be deployed on-premises or in Azure-connected environments.Latest Threat Intelligence (October 2022)
Microsoft Defender for IoT has released the October 2022 Threat Intelligence package. The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file). Threat Intelligence updates reflect the combined impact of proprietary research and threat intelligence carried out by Microsoft security teams. Each package contains the latest CVEs (Common Vulnerabilities and Exposures), IOCs (Indicators of Compromise), and other indicators applicable to IoT/ICS/OT networks (published during the past month) researched and implemented by Microsoft Defender for IoT’s security research team, Section 52. This Threat Intelligence update contains CVEs released during September. CVEs provide a reference method for publicly known information security vulnerabilities and exposures. Updated CVEs published over the last month are available for reference on the MITRE site, in the National Vulnerability Database site (NVD) as well as IoT/OT specific ICS-CERT. Along with the release of this TI package, provided below further guidance for affected Schneider Electric and Siemens devices which are commonly used in industrial networks. Package Updates With this release, Microsoft Defender for IoT has expanded vulnerability detection capabilities for Siemens industrial equipment including: Siemens RUGGEDCOM RST2288P Siemens RUGGECOM RST2288 Siemens SCALANCE XM-400 Siemens SIMATIC IPC3000 Smart v3 The October Threat Intelligence package contains high-severity CVEs, including CVE-2022-37300. This vulnerability could allow unauthorized users access through weak recovery mechanisms for forgotten passwords in Schneider Electric EcoStruxureTM Control Expert, EcoStruxureTM Process Expert, and Modicon M580 and M340 controller read and write modes when communicating over Modbus data protocols. CVE-2022-37300 Analysis Modbus is a standard communication protocol, which transmits signals from a wide range of devices and controls to controllers and often used to connect to a remote terminal unit (RTU) in supervisory control and data acquisition (SCADA) systems. An attacker abusing this vulnerability can easily recover the password of the controller granting them permissions to change settings, change software and ladder logic installed on the controller which affect device behavior, and delete files. This vulnerability can also allow an attacker to install custom firmware on the controller, conferring device control even if the password is later updated by network operators. Guidance Microsoft Defender for IoT researchers encourage you to review the implementation of the Siemens and Schneider Electric industrial devices included in the October Threat Intelligence package and to patch devices when relevant in order to reduce your attack surface. With the publication of this vulnerability Schneider Electric has issued updates to affected devices and recommended mitigations for customers who cannot update their devices. Given the nature of the vulnerability, Microsoft Defender for IoT strongly recommends immediately patching affected devices and following Schneider Electric’s published mitigations: Use strong passwords and refrain from using default credentials. Segment networks and configure firewalls to block unauthorized access to TCP port 502. Configure devices, access lists and communications according to guidelines issued by Schneider Electric. Use virtual private networks between devices. Secure files and their transfer with encryption and secure communication protocols. Only access files from trusted sources. Customers are recommended to update their systems with the latest TI package in order to detect potential exposure risks and vulnerabilities in their networks and on their devices. Threat Intelligence packages are updated every month with the most up-to-date security information available, ensuring that Microsoft Defender for IoT can identify malicious actors and behaviors on devices. If you would like more information about these CVEs or have concerns about your security posture, please do not hesitate to reach out. Update your system with the latest TI package The package is available for download from the Microsoft Defender for IoT portal (click Updates, then Download file), for more information, please review Update threat intelligence data | Microsoft Docs. MD5 Hash - 4126b21d3a5f2e79a350207ee40e5dca For cloud connected sensors, Microsoft Defender for IoT can automatically update new threat intelligence packages following their release, click here for more information.
