Efficiently process high volume logs and optimize costs with Microsoft Sentinel data lake
As organizations scale their security monitoring, a key challenge is maintaining visibility while controlling costs. High‑volume logs—such as firewall, proxy, and endpoint data—are essential for achieving full threat visibility and must be ingested to understand the complete threat environment. With Microsoft Sentinel data lake, you can ingest high‑volume logs directly into the data lake tier—significantly reducing storage costs while maintaining full visibility. After ingestion, you can extract, enrich, summarize, or normalize events to highlight what matters most for security. Only the enriched, high-value events are then promoted to the Analytics tier for correlation, detection, and investigation. This approach offers several advantages: Higher ROI : Organizations get more value from their security data by storing infrequently queried or low‑detection‑value logs in the lower‑cost data lake tier—boosting ROI while preserving complete visibility. With raw log storage dramatically cheaper in the data lake tier, teams can retain more data, uncover deeper insights, and optimize spend without compromising security. Performance optimization: Send only high‑value security data to the Analytics tier to keep performance fast and efficient Targeted insights: Post-ingestion processing allows analysts to classify and enrich logs, removing noise and enhancing relevance. Flexible retention: The data lake enables long-term retention with 6x compression rate enabling a historical analysis and deeper insights In this post, we will demonstrate how to leverage the Sentinel data lake by ingesting data into the CommonSecurityLog table. We will classify source and destination IPs as public or private, enrich the logs with IP classification, and then group and summarize repeated SourceIP-DestinationIP- RequestURL pairs to highlight outbound communication patterns. We will highlight how post-ingestion processing can reduce costs while improving the analytical value of your data. You can achieve this via KQL jobs in Microsoft Sentinel data lake. What are KQL jobs? KQL jobs in Sentinel data lake are automated one-time or scheduled jobs that run Kusto Query Language (KQL) queries directly on data lake. These jobs help security teams investigate and hunt for threats more easily by automating processes like checking logs against known threats, and enriching and grouping network logs before they are promoted to the Analytics tier. This automation reduces storage costs while producing high-value, investigation-ready datasets. Scenario Firewall and network logs typically contain a wide range of fields, such as SourceIP, DestinationIP, RequestURL, and DeviceAction. While these logs provide extensive information, much of it may not be required for analytics detections, nor does every log entry need to be available in the Analytics tier. Additionally, these logs often lack contextual details, such as traffic is internal or external, and do not inherently highlight repeated communication patterns or spikes that may warrant investigation. For high-volume logs, organizations can leverage post-ingestion enrichment and summarization within the data lake tier. This process transforms raw logs into structured, context-rich datasets, complete with IP classification and grouped connection patterns, making them ready for targeted investigation or selective promotion to the Analytics tier. This approach ensures that only the most relevant and actionable data is surfaced for analytics, optimizing both cost and operational efficiency. Step 1 - Filter and enrich logs Test and refine your query using lake explorer before automating. The following KQL query filters out empty IPs, classifies each source and destination IP as Public, Private, or Unknown, and groups repeated SourceIP-DestinationIP-RequestURL combinations to reveal meaningful traffic patterns: CommonSecurityLog | where isnotempty(SourceIP) or isnotempty(DestinationIP) | extend SrcIPType = iff(ipv4_is_private(SourceIP), "Private", iff(isempty(SourceIP), "Unknown", "Public")), DstIPType = iff(ipv4_is_private(DestinationIP), "Private", iff(isempty(DestinationIP), "Unknown", "Public")) | summarize Count = count() by SourceIP, SrcIPType, DestinationIP, DstIPType, RequestURL This classification adds context to high-volume network logs, making it easier to identify traffic between internal and external networks, as well as significantly reducing the volume of data surfaced into the Analytics tier. Step 2 - Automate post-ingestion processing using KQL jobs Once the query logic is validated in the lake explorer, you can automate it to run continuously as a KQL job: Scheduled KQL jobs can process new logs and store the results in a custom table in Analytics tier. Using Custom detection rules on prompted data, you can setup alerts for any anomalies. Using Microsoft Sentinel Workbooks you can visualize the enriched data for monitoring and analysis. Automating this workflow ensures that every log batch arriving in the data lake is enriched and included in summarized log in Analytics tier, while maintaining balance between cost and visibility. How to schedule this process using a KQL job To run post-ingestion processing query and retain results periodically, we would like to schedule this job to run every hour to summarize network log and store results in a custom table in Analytics tier. To avoid missing any logs, we recommend adding a delay of 15 minutes in the query to make sure all logs are available in lake and included in the job runs. let dt_lookBack = 1h; // Look back window duration let delay = 15m; // Delay to allow late-arriving data let endTime = now() - delay; let startTime = endTime - dt_lookBack; CommonSecurityLog | where TimeGenerated >= startTime and TimeGenerated < endTime | where isnotempty(SourceIP) or isnotempty(DestinationIP) | extend SrcIPType = iff(ipv4_is_private(SourceIP), "Private", iff(isempty(SourceIP), "Unknown", "Public")), DstIPType = iff(ipv4_is_private(DestinationIP), "Private", iff(isempty(DestinationIP), "Unknown", "Public")) | summarize Count = count() by SourceIP, SrcIPType, DestinationIP, DstIPType, RequestURL | order by Count desc KQL jobs can run ad-hoc or in a schedule (by minutes, hourly, daily, weekly or monthly). To obtain the enriched log continuously in Analytics tier, we will schedule this hourly: Results are automatically available in the Analytics tier and can be used to set up a new custom detection rule. Cost of this KQL job in Sentinel data lake The cost of running KQL jobs in Sentinel data lake depends on the volume of data scanned and how frequently the jobs run. Data lake KQL queries and jobs are priced at Analyzed data. Estimated cost analysis details of data lake: Item Calculation / Notes Cost ($) Data lake ingestion and processing (1TB/month) 1TB × ($0.05 + $0.1) per GB $153.60 Hourly KQL jobs for 1-hour lookback (1.4 GB/hour) 1.4 GB × $0.005 × 24 × 30 $5.04 Enriched data sent to Analytics (10% of 1TB) 102.4 GB × $4.3 per GB $440.32 Total data lake approach $153.6 + $440.32 + 5.04 $598.96 Note: This sample pricing model applies to East US region. This pricing model allows organizations to perform large-scale threat hunting and intelligence matching without the high expenses typically associated with traditional SIEMs. Summary of monthly costs Approach Estimated Monthly Cost ($) Raw data ingestion to Analytics tier (1TB) $4,386 Raw data ingestion to data lake tier + enriched summary to Analytics $598.96 Savings $3,787.04 For more details around Microsoft Sentinel data lake costs for KQL queries and jobs, see https://azure.microsoft.com/en-us/pricing/calculator. Summary and next steps High-volume logs are critical for security visibility, but ingesting all raw data directly into the Analytics tier can be expensive and unwieldy. By storing logs in Sentinel data lake and performing post-ingestion enrichment, organizations can classify and contextualize events, reduce costs, and maintain a lean, high-performing analytics environment. This approach demonstrates that smarter processing, is the key to maximizing both efficiency and security insight in Microsoft Sentinel. Get started with Microsoft Sentinel data lake today. Microsoft Sentinel data lake overview - Microsoft Security | Microsoft Learn KQL and the Microsoft Sentinel data lake - Microsoft Security | Microsoft Learn Microsoft Sentinel Pricing | Microsoft Security
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.
Microsoft Defender for IoT - General Release Update
Today we are excited to announce that our first General Availability (GA) release, version 22.1, is now available with additional Public Preview features via Azure portal to scale large environments and control the security components from a single pane of glass. This version follows up our December announcement for the Unified Device Inventory via Azure portal. In this release, the Defender for IoT sensor console has been re-designed to create a unified Microsoft Azure experience and enhanced and simplified workflows. Microsoft Defender for IoT's OT Sensor is a key component for deep packet inspection and OT environment analysis. The latest release emphasizes accessibility and reduces time to value by minimizing installation times for faster and more efficient deployment. Lastly, we have leveraged our detection capabilities to get broader security coverage, with an emphasis on customizing the alert engine to detect even the most minor changes in your business-critical environments. With this release we are introducing revamped unified user experience on both the sensor console as well as the Azure portal when performing detailed incident investigation and response. What's New ? OT sensor features for Defender for IoT in the Azure portal (Public Preview) Easily connect sensors to Defender for IoT by using a new, fast connectivity model that presents a secure, plug-and-play experience Customizable reports for enhanced visibility of your environment security posture Automated updating of threat intelligence to keep your sensors up-to-date with the latest threats. Microsoft Defender for Endpoint can easily be integrated with Defender for IoT, allowing you to analyze how IoT devices relate to security exposures for better results. Detecting threats using MITRE ATT&CK for ICS. Use insight into the tactics and techniques associated with your alerts to analyze and interpret them. Using these additional alert enhancements will allow you to better understand attackers' characteristics, the actions they are likely to take within the OT network, and respond accordingly (supported also via Microsoft Sentinel). OT Sensor version 22.1 User experience has been completely revamped across the entire system based on feedback from our enterprise customers collected over the last five years. Among the new features is a detailed device page with advanced information that appears on the New Device Inventory page. We've also implemented global readiness and accessibility features to comply with Microsoft standards. These updates include localization for over 15 languages. The Overview page now includes data that better highlights system deployment details, critical network monitoring health, top alerts, and important trends and statistics. Utilize the new sensor installation wizard, which verifies if traffic is being collected appropriately during installation. Alerts are now available from the new Alerts page of Defender for IoT in the Azure portal. Improve the security and operational efficiency of your IoT/OT network using alerts. View contextual information regarding each alert, for example, similar events occurring around the same time, or a map of all connected devices. Use our tailored threat detection engine with flexible custom alerts and advanced Deep Packet Inspection, (DPI) to detect specific changes in your production lines and schedule custom rules to run and detect threats outside of regular working hours Improved support for high resolution screens and themes, including high contrast and dark modes. About Microsoft 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. To learn more, visit Microsoft Defender for IoT Release Notes | Microsoft Docs Download links available at Defender for IoT Management Portal - Microsoft Azure. 'Enabling IoT/OT Threat Monitoring in Your SOC with Microsoft Sentinel
Recent ransomware attacks that shut down a US gas pipeline and global food processor have raised board-level awareness about IoT and Operational Technology (OT) risk, including safety risks and lost revenue from production downtime. To help jump-start IoT/OT detection and response in your SOC, we've created a new Microsoft Sentinel solution that leverages telemetry from Microsoft Defender for IoT — our agentless IoT/OT security monitoring technology — that provides pre-built, IoT/OT-specific analytics rules, workbooks, SOAR playbooks, and mappings to the MITRE ATT&CK for ICS (industrial control systems) framework.Security Guidance Series: CAF 4.0 Understanding Threat From Awareness to Intelligence-Led Defence
The updated CAF 4.0 raises expectations around control A2.b - Understanding Threat. Rather than focusing solely on awareness of common cyber-attacks, the framework now calls for a sector-specific, intelligence-informed understanding of the threat landscape. According to the NCSC, CAF 4.0 emphasizes the need for detailed threat analysis that reflects the tactics, techniques, and resources of capable adversaries, and requires that this understanding directly shapes security and resilience decisions. For public sector authorities, this means going beyond static risk registers to build a living threat model that evolves alongside digital transformation and service delivery. Public sector authorities need to know which systems and datasets are most exposed, from citizen records and clinical information to education systems, operational platforms, and payment gateways, and anticipate how an attacker might exploit them to disrupt essential services. To support this higher level of maturity, Microsoft’s security ecosystem helps public sector authorities turn threat intelligence into actionable understanding, directly aligning with CAF 4.0’s Achieved criteria for control A2.b. Microsoft E3 - Building Foundational Awareness Microsoft E3 provides public sector authorities with the foundational capabilities to start aligning with CAF 4.0 A2.b by enabling awareness of common threats and applying that awareness to risk decisions. At this maturity level, organizations typically reach Partially Achieved, where threat understanding is informed by incidents rather than proactive analysis. How E3 contributes to Contributing Outcome A2.b: Visibility of basic threats: Defender for Endpoint Plan 1 surfaces malware and unsafe application activity, giving organizations insight into how adversaries exploit endpoints. This telemetry helps identify initial attacker entry points and informs reactive containment measures. Identity risk reduction: Entra ID P1 enforces MFA and blocks legacy authentication, mitigating common credential-based attacks. These controls reduce the likelihood of compromise at early stages of an attacker’s path. Incident-driven learning: Alerts and Security & Compliance Centre reports allow organizations to review how attacks unfolded, supporting documentation of observed techniques and feeding lessons into risk decisions. What’s missing for Achieved: To fully meet the contributing outcomes A2.b, public sector organizations must evolve from incident-driven awareness to structured, intelligence-led threat analysis. This involves anticipating probable attack methods, developing plausible scenarios, and maintaining a current threat picture through proactive hunting and threat intelligence. These capabilities extend beyond the E3 baseline and require advanced analytics and dedicated platforms. Microsoft E5 – Advancing to Intelligence-Led Defence Where E3 establishes the foundation for identifying and documenting known threats, Microsoft E5 helps public sector organizations to progress toward the Achieved level of CAF control A2.b by delivering continuous, intelligence-driven analysis across every attack surface. How E5 aligns with Contributing Outcome A2.b: Detailed, up-to-date view of attacker paths: At the core of E5 is Defender XDR, which correlates telemetry from Defender for Endpoint Plan 2, Defender for Office 365 Plan 2, Defender for Identity, and Defender for Cloud Apps. This unified view reveals how attackers move laterally between devices, identities, and SaaS applications - directly supporting CAF’s requirement to understand probable attack methods and the steps needed to reach critical targets. Advanced hunting and scenario development: Defender for Endpoint P2 introduces advanced hunting via Kusto Query Language (KQL) and behavioural analytics. Analysts can query historical data to uncover persistence mechanisms or privilege escalation techniques, assisting organizations to anticipate attack chains and develop plausible scenarios, a key expectation under A2.b. Email and collaboration threat modelling: Defender for Office 365 P2 detects targeted phishing, business email compromise, and credential harvesting campaigns. Attack Simulation Training adds proactive testing of social engineering techniques, helping organizations maintain awareness of evolving attacker tradecraft and refine mitigations. Identity-focused threat analysis: Defender for Identity and Entra ID P2 expose lateral movement, credential abuse, and risky sign-ins. By mapping tactics and techniques against frameworks like MITRE ATT&CK, organizations can gain the attacker’s perspective on identity systems - fulfilling CAF’s call to view networks from a threat actor’s lens. Cloud application risk visibility: Defender for Cloud Apps highlights shadow IT and potential data exfiltration routes, helping organizations to document and justify controls at each step of the attack chain. Continuous threat intelligence: Microsoft Threat Intelligence enriches detections with global and sector-specific insights on active adversary groups, emerging malware, and infrastructure trends. This sustained feed helps organizations maintain a detailed understanding of current threats, informing risk decisions and prioritization. Why this meets Achieved: E5 capabilities help organizations move beyond reactive alerting to a structured, intelligence-led approach. Threat knowledge is continuously updated, scenarios are documented, and controls are justified at each stage of the attacker path, supporting CAF control A2.b’s expectation that threat understanding informs risk management and defensive prioritization. Sentinel While Microsoft E5 delivers deep visibility across endpoints, identities, and applications, Microsoft Sentinel acts as the unifying layer that helps transform these insights into a comprehensive, evidence-based threat model, a core expectation of Achieved maturity under CAF 4.0 A2.b. How Sentinel enables Achieved outcomes: Comprehensive attack-chain visibility: As a cloud-native SIEM and SOAR, Sentinel ingests telemetry from Microsoft and non-Microsoft sources, including firewalls, OT environments, legacy servers, and third-party SaaS platforms. By correlating these diverse signals into a single analytical view, Sentinel allows defenders to visualize the entire attack chain, from initial reconnaissance through lateral movement and data exfiltration. This directly supports CAF’s requirement to understand how capable, well-resourced actors could systematically target essential systems. Attacker-centric analysis and scenario building: Sentinel’s Analytics Rules and MITRE ATT&CK-aligned detections provide a structured lens on tactics and techniques. Security teams can use Kusto Query Language (KQL) and advanced hunting to identify anomalies, map adversary behaviours, and build plausible threat scenarios, addressing CAF’s expectation to anticipate probable attack methods and justify mitigations at each step. Threat intelligence integration: Sentinel enriches local telemetry with intelligence from trusted sources such as the NCSC and Microsoft’s global network. This helps organizations maintain a current, sector-specific understanding of threats, applying that knowledge to prioritize risk treatment and policy decisions, a defining characteristic of Achieved maturity. Automation and repeatable processes: Sentinel’s SOAR capabilities operationalize intelligence through automated playbooks that contain threats, isolate compromised assets, and trigger investigation workflows. These workflows create a documented, repeatable process for threat analysis and response, reinforcing CAF’s emphasis on continuous learning and refinement. This video brings CAF A2.b – Understanding Threat – to life, showing how public sector organizations can use Microsoft security tools to build a clear, intelligence-led view of attacker behaviour and meet the expectations of CAF 4.0. Why this meets Achieved: By consolidating telemetry, threat intelligence, and automated response into one platform, Sentinel elevates public sector organizations from isolated detection to an integrated, intelligence-led defence posture. Every alert, query, and playbook contributes to an evolving organization-wide threat model, supporting CAF A2.b’s requirement for detailed, proactive, and documented threat understanding. CAF 4.0 challenges every public-sector organization to think like a threat actor, to understand not just what could go wrong, but how and why. Does your organization have the visibility, intelligence, and confidence to turn that understanding into proactive defence? To illustrate how this contributing outcome can be achieved in practice, the one-slider and demo show how Microsoft’s security capabilities help organizations build the detailed, intelligence-informed threat picture expected by CAF 4.0. These examples turn A2.b’s requirements into actionable steps for organizations. In the next article, we’ll explore C2 - Threat Hunting: moving from detection to anticipation and embedding proactive resilience as a daily capability.
