Updating SDK for Java used by Defender for Server/CSPM in AWS
Hi, I have a customer who is Defender for Cloud/CSPM in AWS. Last week, Cloud AWS Health Dashboard lit up with a recommendation around the use of AWS SDK for Java 1.x in their organization. This version will reach end of support on December 31, 2025. The recommendation is to migrate to AWS SDK for Java 2.x. The issue is present in all of AWS workload accounts. They found that a large amount of these alerts is caused by the Defender CSPM service, running remotely, and using AWS SDK for Java 1.x. Customer attaching a couple of sample events that were gathered from the CloudTrail logs. Please note that in both cases: assumed-role: DefenderForCloud-Ciem sourceIP: 20.237.136.191 (MS Azure range) userAgent: aws-sdk-java/1.12.742 Linux/6.6.112.1-2.azl3 OpenJDK_64-Bit_Server_VM/21.0.9+10-LTS java/21.0.9 kotlin/1.6.20 vendor/Microsoft cfg/retry-mode/legacy cfg/auth-source#unknown Can someone provide guidance about this? How to find out if DfC is going to leverage AWS SDK for Java 2.x after Dec 31, 2025? Thanks, Terru
Unifying AWS and Azure Security Operations with Microsoft Sentinel
The Multi-Cloud Reality Most modern enterprises operate in multi-cloud environments using Azure for core workloads and AWS for development, storage, or DevOps automation. While this approach increases agility, it also expands the attack surface. Each platform generates its own telemetry: Azure: Activity Logs, Defender for Cloud, Entra ID sign-ins, Sentinel analytics AWS: CloudTrail, GuardDuty, Config, and CloudWatch Without a unified view, security teams struggle to detect cross-cloud threats promptly. That’s where Microsoft Sentinel comes in, bridging Azure and AWS into a single, intelligent Security Operations Center (SOC). Architecture Overview Connect AWS Logs to Sentinel AWS CloudTrail via S3 Connector Enable the AWS CloudTrail connector in Sentinel. Provide your S3 bucket and IAM role ARN with read access. Sentinel will automatically normalize logs into the AWSCloudTrail table. AWS GuardDuty Connector Use the AWS GuardDuty API integration for threat detection telemetry. Detected threats, such as privilege escalation or reconnaissance, appear in Sentinel as the AWSGuardDuty table. Normalize and Enrich Data Once logs are flowing, enrich them to align with Azure activity data. Example KQL for mapping CloudTrail to Sentinel entities: AWSCloudTrail | extend AccountId = tostring(parse_json(Resources)[0].accountId) | extend User = tostring(parse_json(UserIdentity).userName) | extend IPAddress = tostring(SourceIpAddress) | project TimeGenerated, EventName, User, AccountId, IPAddress, AWSRegion Then correlate AWS and Azure activities: let AWS = AWSCloudTrail | summarize AWSActivity = count() by User, bin(TimeGenerated, 1h); let Azure = AzureActivity | summarize AzureActivity = count() by Caller, bin(TimeGenerated, 1h); AWS | join kind=inner (Azure) on $left.User == $right.Caller | where AWSActivity > 0 and AzureActivity > 0 | project TimeGenerated, User, AWSActivity, AzureActivity Automate Cross-Cloud Response Once incidents are correlated, Microsoft Sentinel Playbooks (Logic Apps) can automate your response: Example Playbook: “CrossCloud-Containment.json” Disable user in Entra ID Send a command to the AWS API via Lambda to deactivate IAM key Notify SOC in Teams Create ServiceNow ticket POST https://api.aws.amazon.com/iam/disable-access-key PATCH https://graph.microsoft.com/v1.0/users/{user-id} { "accountEnabled": false } Build a Multi-Cloud SOC Dashboard Use Sentinel Workbooks to visualize unified operations: Query 1 – CloudTrail Events by Region AWSCloudTrail | summarize Count = count() by AWSRegion | render barchart Query 2 – Unified Security Alerts union SecurityAlert, AWSGuardDuty | summarize TotalAlerts = count() by ProviderName, Severity | render piechart Scenario Incident: A compromised developer account accesses EC2 instances on AWS and then logs into Azure via the same IP. Detection Flow: CloudTrail logs → Sentinel detects unusual API calls Entra ID sign-ins → Sentinel correlates IP and user Sentinel incident triggers playbook → disables user in Entra ID, suspends AWS IAM key, notifies SOC Strengthen Governance with Defender for Cloud Enable Microsoft Defender for Cloud to: Monitor both Azure and AWS accounts from a single portal Apply CIS benchmarks for AWS resources Surface findings in Sentinel’s SecurityRecommendations table
Scam Defender pop Up… help please
Can someone please help my dad has had what looks like scam pop ups come up on his MacBook I have reported to Microsoft but don’t know how long it will be until they get back to me. I want to know if anyone can confirm they’re scams and how I can get them off his screen, it won’t let us click on the X Thank you
Defender Entity Page w/ Sentinel Events Tab
One device is displaying the Sentinel Events Tab, while the other is not. The only difference observed is that one device is Azure AD (AAD) joined and the other is Domain Joined. Could this difference account for the missing Sentinel events data? Any insight would be appreciated!
Security alerts Graph API and MFA
Hello, Does anyone know if https://learn.microsoft.com/en-us/graph/api/resources/partner-security-partnersecurityalert-api-overview?view=graph-rest-beta Security alerts works with app only method (app + cert or app + secret )? Currently i successfully login to graph using both methods Welcome to Microsoft Graph! Connected via apponly access using "ID number" Then i request to get alerts and i get error code: WARNING: Error body: {"error":{"code":"UnknownError","message":"{\"Error\":{\"Code\":\"Unauthorized_MissingMFA\",\"Message\":\"MFA is required for this request. The provided authentication token does not have MFA I found that old API worked with app+user credential only but it's not mentioned in new one about this restriction.
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)Demystifying AI Security Posture Management
Introduction In the ever-evolving paradigm shift that is Generative AI, adoption is accelerating at an unprecedented level. Organizations find it increasingly challenging to keep up with the multiple security branches of defence and attack that are complementing the adoption. With agentic and autonomous agents being the new security frontier we will be concentrating on for the next 10 years, the need to understand, secure and govern what Generative AI applications are running within an organisation becomes critical. Organizations that have a strong “security first” principle have been able to integrate AI by following appropriate methodologies such as Microsoft’s Prepare, Discover, Protect and Govern approach, and are now accelerating the adoption with strong posture management. Link: Build a strong security posture for AI | Microsoft Learn However, due to the nature of this rapid adoption, many organizations have found themselves in a “chicken and egg” situation whereby they are racing to allow employees and developers to adopt and embrace both Low Code and Pro Code solutions such as Microsoft Copilot Studio and Microsoft Foundry, but due to governance and control policies not being implemented in time, now find themselves in a Shadow AI situation, and require the ability to retroactively assess already deployed solutions. Why AI Security Posture Management? Generative AI Workloads, like any other, can only be secured and governed if the organization is aware of their existence and usage. With the advent of Generative AI we now not only have Shadow IT but also Shadow AI, so the need to be able to discover, assess, understand, and govern the Generative AI tooling that is being used in an organisation is now more important than ever. Consider the risks mentioned in the recent Microsoft Digital Defence Report and how they align to AI Usage, AI Applications and AI Platform Security. As Generative AI becomes more ingrained in the day-to-day operations of organizations, so does the potential for increased attack vectors, misuse and the need for appropriate security oversight and mitigation. Link: Microsoft Digital Defense Report 2025 – Safeguarding Trust in the AI Era A recent study by KMPG discussing Shadow AI listed the following statistics: 44% of employees have used AI in ways that contravene policies and guidelines, indicating a significant prevalence of shadow AI in organizations. 57% of employees have made mistakes due to AI, and 58 percent have relied on AI output without evaluating its accuracy. 41% of employees report that their organization has a policy guiding the use of GenAI, highlighting a huge gap in guardrails. A very informed comment by Sawmi Chandrasekaran, Principal, US and Global AI and Data Labs leader at KPMG states: “Shadow AI isn’t a fringe issue—it’s a signal that employees are moving faster than the systems designed to support them. Without trusted oversight and a coordinated architectural strategy, even a single shortcut can expose the organization to serious risk. But with the right guardrails in place, shadow AI can become a powerful force for innovation, agility, and long-term competitive advantage. The time to act is now—with clarity, trust, and bold forward-looking leadership.” Link: Shadow AI is already here: Take control, reduce risk, and unleash innovation It’s abundantly clear that organizations require integrated solutions to deal with the escalating risks and potential flashpoints. The “Best of Breed” approach is no longer sustainable considering the integration challenges both in cross-platform support and data ingestion charges that can arise, this is where the requirements for a modern CNAPP start to come to the forefront. The Next Era of Cloud Security report created by the IDC highlights Cloud Native Application Protection Platforms (CNAPPs) as a key investment area for organizations: “The IDC CNAPP Survey affirmed that 71% of respondents believe that over the next two years, it would be beneficial for their organization to invest in an integrated SecOps platform that includes technologies such as XDR/EDR, SIEM, CNAPP/cloud security, GenAI, and threat intelligence.” Link: The Next Era of Cloud Security: Cloud-Native Application Protection Platform and Beyond AI Security Posture Management vs Data Security Posture Management Data Security Posture Management (DSPM) is often discussed, having evolved prior to the conceptualization of Generative AI. However, DSPM is its own solution that is covered in the Blog Post Data Security Posture Management for AI. AI Security Posture Management (AI-SPM) focuses solely on the ability to monitor, assess and improve the security of AI systems, models, data and infrastructure in the environment. Microsoft’s Approach – Defender for Cloud Defender for Cloud is Microsoft’s modern Cloud Native Application Protection Platform (CNAPP), encompassing multiple cloud security solution services across both Proactive Security and Runtime Protection. However, for the purposes of this article, we will just be delving into AI Security Posture Management (AI-SPM) which is a sub feature of Cloud Security Posture Management (CSPM), both of which sit under Proactive Security solutions. Link: Microsoft Defender for Cloud Overview - Microsoft Defender for Cloud | Microsoft Learn Understanding AI Security Posture Management The following is going to attempt to “cut to the chase” on each of the four areas and cover an overview of the solution and the requirements. For detailed information on feature enablement and usage, each section includes a link to the full documentation on Microsoft Learn for further reading AI Security Posture Management AI Security Posture Management is a key component of the all-up Cloud Security Posture Management (CSPM) solution, and focuses on 4 key areas: o Generative AI Workload Discover o Vulnerability Assessment o Attack Path Analysis o Security Recommendations Generative AI Workload Discovery Overview Arguably, the principal role of AI Security Posture Management is to discover and identify Generative AI Workloads in the organization. Understanding what AI resources exist in the environment being the key to understanding their defence. Microsoft refers to this as the AI Bill-Of-Materials or AI-BOM. Bill-Of-Materials is a manufacturing term used to describe the components that go together to create a product (think door, handle, latch, hinges and screws). In the AI World this becomes application components such as data and artifacts. AI-SPM can discover Generative AI Applications across multiple supported services including: Azure OpenAI Service Microsoft foundry Azure Machine Learning Amazon Bedrock Google Vertex AI (Preview) Why no Microsoft Copilot Studio Integration? Microsoft Copilot Studio is not an external or custom AI agent service and is deeply integrated into Microsoft 365. Security posture for Microsoft Copilot Studio is handed over to Microsoft Defender for Cloud Apps and Microsoft Purview, with applications being marked as Sanctioned or Unsanctioned via the Defender for Cloud portal. For more information on Microsoft Defender for Cloud Apps see the link below. Link: App governance in Microsoft Defender for Cloud Apps and Microsoft Defender XDR - Microsoft Defender for Cloud Apps | Microsoft Learn Requirements An active Azure Subscription with Microsoft Defender for Cloud. Cloud Security Posture Management (CSPM) Enabled Have at least one environment with an AI supported workload. Link: Discover generative AI workloads - Microsoft Defender for Cloud | Microsoft Learn Vulnerability Assessment Once you have a clear overview of which AI resources exist in your environment, Vulnerability Assessment in AI-SPM allows you to cover two main areas of consideration. The first allows for the organization to discover vulnerabilities within containers that are running generative AI images with known vulnerabilities. The second allows vulnerability discovery within Generative AI Library Dependences such as TensorFlow, PyTorch, and LangChain. Both options will align any vulnerabilities detected to known Common Vulnerabilities and Exposures (CVE) IDs via Microsoft Threat Detection. Requirements An active Azure Subscription with Microsoft Defender for Cloud. Cloud Security Posture Management (CSPM) Enabled Have at least one Azure OpenAI resource, with at least one model deployment connected to it via Azure AI Foundry portal. Link: Explore risks to pre-deployment generative AI artifacts - Microsoft Defender for Cloud | Microsoft Learn Attack Path Analysis AI-SPM hunts for potential attack paths in a multi-cloud environment, by concentrating on real, externally driven and exploitable threats rather than generic scenarios. Using a proprietary algorithm, the attack path is mapped from outside the organization, through to critical assets. The attack path analysis is used to highlight immediately, exploitable threats to the business, which attackers would be able to exploit and breach the environment. Recommendations are given to be able to resolve the detected security issues. Discovered Attack Paths are organized by risk levels, which are determined using a context-aware risk-prioritization engine that considers the risk factors of each resource. Requirements An active Azure Subscription with Microsoft Defender for Cloud. Cloud Security Posture Management (CSPM) with Agentless Scanning Enabled. Required roles and permissions: Security Reader, Security Admin, Reader, Contributor, or Owner. To view attack paths that are related to containers: You must enable agentless container posture extension in Defender CSPM or You can enable Defender for Containers, and install the relevant agents in order to view attack paths that are related to containers. This also gives you the ability to query containers data plane workloads in security explorer. Required roles and permissions: Security Reader, Security Admin, Reader, Contributor, or Owner. Link: Identify and remediate attack paths - Microsoft Defender for Cloud | Microsoft Learn Security Recommendations Microsoft Defender for Cloud evaluates all resources discovered, including AI resources, and all workloads based on both built-in and custom security standards, which are implemented across Azure subscriptions, Amazon Web Services (AWS) accounts, and Google Cloud Platform (GCP) projects. Following these assessments, security recommendations offer actionable guidance to address issues and enhance the overall security posture. Defender for Cloud utilizes an advanced dynamic engine to systematically assess risks within your environment by considering exploitation potential and possible business impacts. This engine prioritizes security recommendations according to the risk factors associated with each resource, determined by the context of the environment, including resource configuration, network connections, and existing security measures. Requirements No specific requirements are required for Security Recommendations if you have Defender for Cloud enabled in the tenant as the feature is included by default. However, you will not be able to see Risk Prioritization unless you have the Defender for CSPM plan enabled. Link: Review Security Recommendations - Microsoft Defender for Cloud | Microsoft Learn CSPM Pricing CSPM has two billing models, Foundational CSPM (Free) Defender CSPM, which has its own additional billing model. AI-SPM is only included as part of the Defender CSPM plan. Foundational CSPM Defender CSPM Cloud Availability AI Security Posture Management - Azure, AWS, GCP (Preview) Price Free $5.11/Billable resource/month Information regarding licensing in this article is provided for guidance purposes only and doesn’t provide any contractual commitment. This list and license requirements are subject to change without any prior notice. Full details can be found on the official Microsoft documentation found here, Link: Pricing - Microsoft Defender for Cloud | Microsoft Azure. Final Thoughts AI Security Posture Management can no longer be considered an optional component to security, but rather a cornerstone to any organization’s operations. The integration of Microsoft Defender for Cloud across all areas of an organization shows the true potential of a modern a CNAPP, where AI is no longer a business objective, but rather a functional business component.The Microsoft Defender for AI Alerts
I will start this blog post by thanking my Secure AI GBB Colleague Hiten Sharma for his contributions to this Tech Blog as a peer-reviewer. Microsoft Defender for AI (part of Microsoft Defender) helps organizations threats to generative AI applications in real time and helps respond to security issues. Microsoft Defender for AI is in General Availability state and covers Azure OpenAI supported models and Azure AI Model Inference service supported models deployed on Azure Commercial Cloud and provides Activity monitoring and prompt evidence for security teams. This blog aims to help the Microsoft Defender for AI (the service) users understand the different alerts generated by the service, what they mean, how they align to the Mitre Att&ck Framework, and how to reduce the potential alert re-occurrences. The 5 Generative AI Security Threats You Need to Know About This section aims to give the reader an overview of the 5 Generative AI Security Threats every security professional needs to know about. For more details, please refer to “The 5 generative AI security threats you need to know about e-book”. Poisoning Attacks Poisoning attacks are adversarial attacks which target the training or fine-tuning data of generative AI models. In a Poisoning Attack, the adversary injects biased or malicious data during the learning process with the intention of affecting the model’s behavior, accuracy, reliability, and ethical boundaries. Evasion Attacks Evasion Attacks are adversarial attacks where the adversary crafts inputs designed to bypass the security controls and model restrictions. This kind of attacks [Evasion Attacks] exploit the generative AI system in the model’s inference stage (In the context of generative AI, this is the stage where the model generates text, images, or other outputs in response to user inputs.). In an Evasion Attack, the adversary does not modify the Generative AI model itself but rather adapts and manipulates prompts to avoid the model safety mechanisms. Functional Extraction Functional Extraction attacks are model extraction attacks where the adversary repeatedly interacts with the Generative AI system and observes the responses. In a Functional Extraction attack, the adversary attempts to reverse-engineer or recreate the generative AI system without direct access to its infrastructure or training data. Inversion Attack Inversion Attacks are adversarial attacks where the adversary repeatedly interacts with the Generative AI system to reconstruct or infer sensitive information about the model and its infrastructure. In an Inversion Attack, the adversary attempts to exploit what the Generative AI model have memorized from its training data. Prompt Injection Attacks Prompt Injection Attacks are evasion attacks where the adversary uses malicious prompts to override or bypass the AI system’s safety rules, policies, and intended behavior. In a Prompt Injection Attack, the adversary embeds malicious instructions in a prompt (or a sequence of prompts) to trick the AI system into ignoring safety filters, generate harmful or restricted contents, or reveal confidential information (i.e. the Do Anything Now (DAN) exploit, which prompts LLMs to “do anything now.” More details about AI Jail Brake attempts, including DAN exploit can be found in this Microsoft Tech Blog Article). The Microsoft Defender for AI Alerts Microsoft Defender for AI works with Azure AI Prompt Shields (more details at Microsoft Foundry Prompt Shields documentation) and utilizes Microsoft’s Threat Intelligence to identify (in real-time) the threats impacting the monitored AI Services. Below is a list of the different alerts Defender for AI generates, what they mean, how they align with the Mitre Att&ck Framework, and suggestion on how to reduce the potential of their re-occurrence. More details about these alerts can be found at Microsoft Defender for AI documentation. Detected credential theft attempts on an Azure AI model deployment Severity: Medium Mitre Tactics: Credential Access, Lateral Movement, Exfiltration Attack Type: Inversion Attack Description: As per Microsoft Documentation “The credential theft alert is designed to notify the SOC when credentials are detected within GenAI model responses to a user prompt, indicating a potential breach. This alert is crucial for detecting cases of credential leak or theft, which are unique to generative AI and can have severe consequences if successful.” How it happens: Credential Leakage in a Generative AI response typically occur because of training the model with data that contains credentials (i.e. Hardcoded secrets, API Keys, passwords, or configuration files that contain such information), this can also occur if the prompt triggers the AI System to retrieve the information from host system tools or memory. How to avoid: The re-occurrence of this alert can be reduced by adapting the following: Training Data Hygiene: Ensure that no credentials exist in the training data, this can be done by scanning for credentials and using secret-detection tools before training or fine-tuning the model(s) in use. Guardrails and Filtering: Implementing output scanning (i.e. credential detectors, filters, etc…) to block responses that contain credentials. This can be addressed using various methods including custom content filters in Azure AI Foundry. Adapt Zero Trust, including least privilege access to the run-time environment for the AI system, ensure that the AI System and its plugins has no access to secrets (more details at Microsoft’s Zero Trust site.) Prompt Injection Defense: In addition to adapting the earlier recommendations, also use Azure AI Prompt Shields to identify and potentially block prompt injection attempts. A Jailbreak attempt on an Azure AI model deployment was blocked by Azure AI Content Safety Prompt Shields Severity: Medium Mitre Tactics: Privilege Escalation, Defense Evasion Attack Type: Prompt Injection Attack Description: As per Microsoft Documentation “The Jailbreak alert, carried out using a direct prompt injection technique, is designed to notify the SOC there was an attempt to manipulate the system prompt to bypass the generative AI’s safeguards, potentially accessing sensitive data or privileged functions. It indicated that such attempts were blocked by Azure Responsible AI Content Safety (also known as Prompt Shields), ensuring the integrity of the AI resources and the data security.” How it happens: This alert indicates that Prompt Shields (more details about prompt shields at Microsoft Foundry Prompt Shields documentation) have identified an attempt by an adversary to use a specially engineered input to trick the AI System into by passing its safety rules, guardrails, or content filters. In the case of this alert, Prompt Shields have detected and blocked the attempt, preventing the AI system from acting differently than its guardrails. How to avoid: While this alert indicates that Prompt Shield has successfully blocked the Jailbreak attempt, additional measures can be taken to reduce the potential impact and re-occurrence of Jailbreak attempts: Use Azure AI Prompt Shields: Real-Time detection is not a single use but rather a continuous use security measure. Continue using it and monitor alerts, (more details at Microsoft Foundry Prompt Shields documentation). Use Retrieval Isolation: Retrieval Isolation separates user prompts from knowledge/retrieval sources (i.e. Knowledge Bases, Databases, Web search Agents, APIs, Documents), this isolation ensures that the model is not directly influencing what contents is retrieved, insures that malicious prompts cannot poison the knowledge/retrieval sources, and reduces the impact of malicious prompts that intend to coerce the system to retrieve sensitive or unsafe data. Continuous testing: Using Red Teaming tools (i.e. Microsoft AI Read Team tools) and exercises, continuously test the AI system against Jail Break patterns and models and adjust security measures according to findings. Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, use least privilege access, and always assume breach to ensure the AI system cannot directly trigger actions, API calls, or sensitive operations without proper validation (more details at Microsoft’s Zero Trust site. A Jailbreak attempt on an Azure AI model deployment was detected by Azure AI Content Safety Prompt Shields Severity: Medium Mitre Tactics: Privilege Escalation, Defense Evasion Attack Type: Prompt Injection Attack Description: As per Microsoft Documentation “The Jailbreak alert, carried out using a direct prompt injection technique, is designed to notify the SOC there was an attempt to manipulate the system prompt to bypass the generative AI’s safeguards, potentially accessing sensitive data or privileged functions. It indicated that such attempts were detected by Azure Responsible AI Content Safety (also known as Prompt Shields), but weren't blocked due to content filtering settings or due to low confidence.” How it happens: This alert indicates that Prompt Shields have identified an attempt by an adversary to use a specially engineered input to trick the AI System into by passing its safety rules, guardrails, or content filters. In the case of this alert, Prompt Shields have detected the attempt but did not block it, the event is not blocked due to either the content filter settings configuration or low confidence. How to avoid: While this alert indicates that Prompt Shield is enabled to protect the AI system and has successfully detected the Jailbreak attempt, additional measures can be taken to reduce the potential impact and re-occurrence of Jailbreak attempts: Use Azure AI Prompt Shields: Real-Time detection is not a single use but rather a continuous use security measure. Continue using it and monitor alerts, (more details at Microsoft Foundry Prompt Shields documentation). Use Retrieval Isolation: Retrieval Isolation separates user prompts from knowledge/retrieval sources (i.e. Knowledge Bases, Databases, Web search Agents, APIs, Documents), this isolation ensures that the model is not directly influencing what contents is retrieved, insures that malicious prompts cannot poison the knowledge/retrieval sources, and reduces the impact of malicious prompts that intend to coerce the system to retrieve sensitive or unsafe data. Continuous testing: Using Red Teaming tools (i.e. Microsoft AI Read Team tools) and exercises, continuously test the AI system against Jail Break patterns and models and adjust security measures according to findings. Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, use least privilege access, and always assume breach to ensure the AI system cannot directly trigger actions, API calls, or sensitive operations without proper validation (more details at Microsoft’s Zero Trust site. Corrupted AI application\model\data directed a phishing attempt at a user Severity: High Mitre Tactics: Impact (Defacement) Attack Type: Poisoning Attack Description: As per Microsoft Documentation “This alert indicates a corruption of an AI application developed by the organization, as it has actively shared a known malicious URL used for phishing with a user. The URL originated within the application itself, the AI model, or the data the application can access.” How it happens: This alert indicates the AI system, its underlying model, or its knowledge sources were corrupted with malicious data and started returning the corrupted data in the form of phishing-style responses to the users. This can occur because of training data poisoning, an earlier successful attack that modified the system knowledge sources, tampered system instructions, or unauthorized access to the AI system itself. How to avoid: This alert needs to be taken seriously and investigated accordingly, the re-occurrence of this alert can be reduced by adapting the following: Strengthen model and data integrity controls, this includes hashing model artifacts (i.e. Model Weights, Tokenizers), signing model packages, and enforcing integrity checks during runtime. Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, across developer environments, CI/CD pipelines, knowledge sources, and deployment endpoints, (more details at Microsoft’s Zero Trust site.) Implement data validation and data poisoning detection strategies on all incoming training and fine-tuning data. Use Retrieval Isolation: Retrieval Isolation separates user prompts from knowledge/retrieval sources (i.e. Knowledge Bases, Databases, Web search Agents, APIs, Documents), this isolation ensures that the model is not directly influencing what contents is retrieved, insures that malicious prompts cannot poison the knowledge/retrieval sources, and reduces the impact of malicious prompts that intend to coerce the system to retrieve sensitive or unsafe data. Continuous testing: Using Red Teaming tools (i.e. Microsoft AI Read Team tools) and exercises, continuously test the AI system against poisoning attempts, prompt injection attacks, and malicious tools invocation scenarios. Phishing URL shared in an AI application Severity: High Mitre Tactics: Impact (Defacement), Collection Attack Type: Prompt Injection Description: As per Microsoft Documentation “This alert indicates a potential corruption of an AI application, or a phishing attempt by one of the end users. The alert determines that a malicious URL used for phishing was passed during a conversation through the AI application, however the origin of the URL (user or application) is unclear.” How it happens: This alert indicates that a phishing URL was present in the interaction between the user and the AI System, this phishing URL might originate from a user prompt, as a result of malicious input in a prompt, generated by them model as a result of an earlier attack, or due to a poisoned knowledge source. How to avoid: This alert needs to be taken seriously and investigated accordingly, the re-occurrence of this alert can be reduced by adapting the following: Adapt URL scanning mechanism prior to returning any URL to users (i.e. check against Threat Intelligence, URL reputation sources) and content scanning mechanisms (This can be done using Azure Prompt Flows, or using Azure Functions). Use Retrieval Isolation: Retrieval Isolation separates user prompts from knowledge/retrieval sources (i.e. Knowledge Bases, Databases, Web search Agents, APIs, Documents), this isolation ensures that the model is not directly influencing what contents is retrieved, insures that malicious prompts cannot poison the knowledge/retrieval sources, and reduces the impact of malicious prompts that intend to coerce the system to retrieve sensitive or unsafe data. Filter and sanitize user prompts to prevent harmful or malicious URLs from being used or amplified by the AI system (This can be done using Azure Prompt Flows, or using Azure Functions). Phishing attempt detected in an AI application Severity: High Mitre Tactics: Collection Attack Type: Prompt Injection, Poisoning Attack Description: As per Microsoft Documentation “This alert indicates a URL used for phishing attack was sent by a user to an AI application. The content typically lures visitors into entering their corporate credentials or financial information into a legitimate looking website. Sending this to an AI application might be for the purpose of corrupting it, poisoning the data sources it has access to, or gaining access to employees or other customers via the application's tools.” How it happens: This alert indicates that a phishing URL was present in a prompt sent from the user to the AI System. When a user uses a phishing URL in a prompt, this can be an indicator of a user who is attempting to corrupt the AI system, corrupt its knowledge sources to compromise other users of the AI system, or a user who is trying to manipulate the AI system to use stored data, stored credentials or system tools in the phishing URL. How to avoid: This alert needs to be taken seriously and investigated accordingly, the re-occurrence of this alert can be reduced by adapting the following: Filter and sanitize user prompts to prevent harmful or malicious URLs from being used or amplified by the AI system (This can be done using Azure Prompt Flows, or using Azure Functions). Use Retrieval Isolation: Retrieval Isolation separates user prompts from knowledge/retrieval sources (i.e. Knowledge Bases, Databases, Web search Agents, APIs, Documents), this isolation ensures that the model is not directly influencing what contents is retrieved, insures that malicious prompts cannot poison the knowledge/retrieval sources, and reduces the impact of malicious prompts that intend to coerce the system to retrieve sensitive or unsafe data. Monitor anomalous behavior originating from the sources that have common connection characteristics. Suspicious user agent detected Severity: Medium Mitre Tactics: Execution, Reconnaissance, Initial access Attack Type: Multiple Description: As per Microsoft Documentation “The user agent of a request accessing one of your Azure AI resources contained anomalous values indicative of an attempt to abuse or manipulate the resource. The suspicious user agent in question has been mapped by Microsoft threat intelligence as suspected of malicious intent and hence your resources were likely compromised.” How it happens: This alert indicates that a user agent of a request that is accessing one of your Azure AI resources contains values that were mapped by Microsoft Threat Intelligence as suspected of Malicious intent. When this alert is present, it is indicative of an abuse or manipulation attempt. This does not necessarily mean that your AI System has been breached, however its an indication that an attack is being attempted and underway, or the AI system was already compromised. How to avoid: Indicators from this alert need to reviewed, including other alerts that might help formulate a full understanding of the sequence of events taking place. Impact and re-occurrence of this alert can be reduced by adapting the following: Review impacted AI systems to assess impact of the event on these systems. Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, use least privilege access, and always assume breach (more details at Microsoft’s Zero Trust site.) Applying rate limiting and bot detection measures using services like Azure Management Gateway. Apply comprehensive user agent filtering and restriction measures to protect your AI System from suspicious or malicious clients by enforcing user-agent filtering at the edge (i.e. using Azure Front door), the gateway (i.e. using Azure API Management), and identity layers to ensure only trusted, verified applications and devices can access your GenAI endpoints. Enable Network Protection Measures (i.e. WAF, Reputation Filters, Geo Restrictions) to filter out traffic from IP addresses associated with Malicious actors and their infrastructure, to avoid traffic from geographies and locations known to be associated with malicious actors, and to eliminate traffic with other highly suspicious characteristics. This can be done using services like Azure Front Door, or Azure Web Application Firewall. ASCII Smuggling prompt injection detected Severity: Medium Mitre Tactics: Execution, Reconnaissance, Initial access Attack Type: Evasion Attack, Prompt Injection Description: As per Microsoft Documentation “ASCII smuggling technique allows an attacker to send invisible instructions to an AI model. These attacks are commonly attributed to indirect prompt injections, where the malicious threat actor is passing hidden instructions to bypass the application and model guardrails. These attacks are usually applied without the user's knowledge given their lack of visibility in the text and can compromise the application tools or connected data sets.” How it happens: This alert indicates an AI system has received a request that a attempted to circumvent system guardrails by embedding harmful instructions by using ASCII characters commonly used for prompt injection attacks. This alert can be caused by multiple reasons including: a malicious user who is attempting prompt manipulation, by an innocent user who is pasting a prompt that contains malicious hidden ASCII characters or instructions, or a knowledge source connected to the AI System that is adding the malicious ASCII characters to the user prompt. How to avoid: Indicators from this alert should be reviewed, including other alerts that might help formulate a full understanding of the sequence of events taking place. Impact and re-occurrence of this alert can be reduced by adapting the following: If the user involved in the incident is known, review their access grant (in Microsoft Entra), and ensure their device and accounts are not compromised starting with reviewing incidents and evidences in Microsoft Defender. Normalize user input before sending it to the models of the AI system, this can be performed using a pre-processing ( i.e. using Azure Prompt Flows, or using Azure Functions, or using Azure API Management). Strip (or block) suspicious ASCII patterns and hidden characters using a pre-processing layer (i.e. using Azure Prompt Flows, or using Azure Functions). Use retrieval isolation to prevent smuggled ASCII from propagating to knowledge sources and tools, multiple retrieval isolation strategies can be adapted including separating user’s raw-input from system-safe input and utilizing the system-safe inputs as bases to build queries and populate fields (i.e. arguments) to invoke tools the AI System interacts with. Using Red Teaming tools (i.e. Microsoft AI Read Team tools) and exercises, continuously test the AI system against ASCII smuggling attempts. Access from a Tor IP Severity: High Mitre Tactics: Execution Attack Type: Multiple Description: As per Microsoft Documentation “An IP address from the Tor network accessed one of the AI resources. Tor is a network that allows people to access the Internet while keeping their real IP hidden. Though there are legitimate uses, it is frequently used by attackers to hide their identity when they target people's systems online.” How it happens: This alert indicates that a user attempted to access the AI System using a TOR exit node. This can be an indicator of a malicious user attempting to hide the true origin of there connection source, whether to avoid geo fencing, or to conceal their identity while carrying on an attack against the AI system. How to avoid: Impact and re-occurrence of this alert can be reduced by adapting the following: Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, use least privilege access, and always assume breach (more details at Microsoft’s Zero Trust site.) Enable Network Protection Measures (i.e. WAF, Reputation Filters, Geo Restrictions) to prevent traffic from TOR exit nodes from reaching the AI System. This can be done using services like Azure Front Door, or Azure Web Application Firewall. Access from a suspicious IP Severity: High Mitre Tactics: Execution Attack Type: Multiple Description: As per Microsoft Documentation “An IP address accessing one of your AI services was identified by Microsoft Threat Intelligence as having a high probability of being a threat. While observing malicious Internet traffic, this IP came up as involved in attacking other online targets.” How it happens: This alert indicates that a user attempted to access the AI System from an IP address that was identified by Microsoft Threat Intelligence as suspicious. This can be an indicator of a malicious user or a malicious tool carrying on an attack against the AI system. How to avoid: Impact and re-occurrence of this alert can be reduced by adapting the following: Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, use least privilege access, and always assume breach (more details at Microsoft’s Zero Trust site.) Enable Network Protection Measures (i.e. WAF, Reputation Filters, Geo Restrictions) to prevent traffic from suspicious IP addresses from reaching the AI System. This can be done using services like Azure Front Door, or Azure Web Application Firewall. Suspected wallet attack - recurring requests Severity: Medium Mitre Tactics: Impact Attack Type: Wallet Attack Description: As per Microsoft Documentation “Wallet attacks are a family of attacks common for AI resources that consist of threat actors excessively engage with an AI resource directly or through an application in hopes of causing the organization large financial damages. This detection tracks high volumes of identical requests targeting the same AI resource which may be caused due to an ongoing attack.” How it happens: Wallet attacks are a category of attacks that attempt to exploit the usage-based billing, quota limits, or token-consumption of the AI System to inflect financial or operational harm on the AI system. This alert is an indicator of the AI System receiving repeated, or high-frequency, or patterned requests that are consistent with wallet attack attempts. How to avoid: Impact and re-occurrence of this alert can be reduced by adapting the following: Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, use least privilege access, and always assume breach (more details at Microsoft’s Zero Trust site.) Enable Network Protection Measures (i.e. WAF, Reputation Filters, Geo Restrictions) to prevent traffic from known malicious actors known IP addresses and infrastructure from reaching the AI System. This can be done using services like Azure Front Door, or Azure Web Application Firewall. Apply rate-limiting and throttling to connection attempts to the AI System using Azure API Management. Enable Quotas, strict usage caps, and cost guardrails using Azure API Management, using Azure Foundry Limits and Quotas, and Azure cost management. Implement client-side security measures (i.e. tokens, signed requests) to prevent bots from imitating legitimate users. There are multiple approaches to adapt (collectively) to achieve this, for example by using Entra ID Tokens for authentication instead of using a simple API key from the front end. Suspected wallet attack - volume anomaly Severity: Medium Mitre Tactics: Impact Attack Type: Wallet Attack Description: As per Microsoft Documentation “Wallet attacks are a family of attacks common for AI resources that consist of threat actors excessively engage with an AI resource directly or through an application in hopes of causing the organization large financial damages. This detection tracks high volumes of requests and responses by the resource that are inconsistent with its historical usage patterns.” How it happens: Wallet attacks are a category of attacks that attempt to exploit the usage-based billing, quota limits, or token-consumption of the AI System to inflect financial or operational harm on the AI system. This alert is an indicator of the AI system experiencing an abnormal volume of interactions exceeding normal usage patterns, which can be caused by automated scripts, bots, or coordinated efforts that are attempting to impose financial and / or operational harm on the AI System. How to avoid: Impact and re-occurrence of this alert can be reduced by adapting the following: Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, use least privilege access, and always assume breach (more details at Microsoft’s Zero Trust site.) Enable Network Protection Measures (i.e. WAF, Reputation Filters, Geo Restrictions) to prevent traffic from known malicious actors known IP addresses and infrastructure from reaching the AI System. This can be done using services like Azure Front Door, or Azure Web Application Firewall. Apply rate-limiting and throttling to connection attempts to the AI System using Azure API Management. Enable Quotas, strict usage caps, and cost guardrails using Azure API Management, using Azure Foundry Limits and Quotas and Azure cost management. Implement client-side security measures (i.e. tokens, signed requests) to prevent bots from imitating legitimate users. There are multiple approaches to adapt (collectively) to achieve this, for example by using Entra ID Tokens for authentication instead of using a simple API key from the front end. Access anomaly in AI resource Severity: Medium Mitre Tactics: Execution, Reconnaissance, Initial access Attack Type: Multiple Description: As per Microsoft Documentation “This alert track anomalies in access patterns to an AI resource. Changes in request parameters by users or applications such as user agents, IP ranges, authentication methods. can indicate a compromised resource that is now being accessed by malicious actors. This alert may trigger when requests are valid if they represent significant changes in the pattern of previous access to a certain resource.” How it happens: This alert indicates that a shift in connection and interaction patterns was detected compared to the established baseline of connections and interactions with the AI Systems. This alert can be an indicator of probing events or can be an indicator of a compromised AI System that is now being abused by the malicious actor. How to avoid: Impact and re-occurrence of this alert can be reduced by adapting the following: Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, use least privilege access, and always assume breach (more details at Microsoft’s Zero Trust site.) If exposure is suspected, rotate API Keys and Secrets (more details on how to rotate API Keys in Azure Foundry Documentation). Enable Network Protection Measures (i.e. WAF, Reputation Filters, Geo Restrictions, Conditional Access Controls) to prevent similar traffic from reaching the AI System. Restrictions can be implemented using services like Azure Front Door, or Azure Web Application Firewall. Apply rate-limiting and anomaly detection measures to block unusual request bursts or abnormal access patterns. Rate limiting can be implemented using Azure API Management, Anomaly detection can be performed using AI Real-Time monitoring tools like Microsoft Defender for AI and Security Operations platforms like Microsoft Sentinel where rules can later be created to trigger automations and playbooks that can update the Azure WAF and APIM to block or rate limit traffic from a certain origin. Suspicious invocation of a high-risk 'Initial Access' operation by a service principal detected (AI resources) Severity: Medium Mitre Tactics: Initial access Attack Type: Identity-based Initial Access Attack Description: As per Microsoft Documentation “This alert detects a suspicious invocation of a high-risk operation in your subscription, which might indicate an attempt to access restricted resources. The identified AI-resource related operations are designed to allow administrators to efficiently access their environments. While this activity might be legitimate, a threat actor might utilize such operations to gain initial access to restricted AI resources in your environment. This can indicate that the service principal is compromised and is being used with malicious intent.” How it happens: This alert indicates that an AI System was involved in a highly privileged operation against the run-time environment of the AI System using legitimate credentials. While this might be an intended behavior (regardless of the validity of this design from a security standpoint), this can also be an indicator of an attack against the AI system where the malicious actor has successfully circumvented the AI System guardrails and influenced the AI System to operate beyond its intended behavior. When performed by a malicious actor, this event is expected to be a part of a multi-stage attack against the AI System. How to avoid: Impact and re-occurrence of this alert can be reduced by adapting the following: Upon detection, immediately rotate impacted accounts secrets and certificates. To ensure the AI system cannot directly trigger actions, API calls, or sensitive operations without proper validation, Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, use least privilege access, and always assume breach (more details at Microsoft’s Zero Trust site.). As a part of adapting Zero Trust strategy, enforce managed identities usage instead of relying on long-lived credentials, such as Entra managed IDs for Azure. Use conditional access measures (i.e. Entra conditional access) to limit where and how service principals can authenticate into the system. Enforce a training data hygiene practice to ensure that no credentials exist in the training data, this can be done by scanning for credentials and using secret-detection tools before training or fine-tuning the model(s) in use. Use retrieval isolation to prevent similar events from propagating to knowledge sources and tools, multiple retrieval isolation strategies can be adapted including separating user’s raw-input from system-safe input and utilizing the system-safe inputs as bases to build queries and populate fields (i.e. arguments) to invoke tools the AI System interacts with. Anomalous tool invocation Severity: Low Mitre Tactics: Execution Attack Type: Prompt Injection, Evasion Attack Description: As per Microsoft Documentation “This alert analyzes anomalous activity from an AI application connected to an Azure OpenAI model deployment. The application attempted to invoke a tool in a manner that deviates from expected behavior. This behavior may indicate potential misuse or an attempted attack through one of the tools available to the application.” How it happens: This alert indicates that the AI System has invoked a tool or a downstream capability in behavior pattern that deviates from its expected behavior. This event can be an indicator that a malicious user have managed to provide a prompt (or series of prompts) that have circumvented the AI System defenses and guardrails and as a result caused the AI System to call tools it should not call or caused it to use tools it has access to in an abnormal way. How to avoid: Impact and re-occurrence of this alert can be reduced by adapting the following: Adapt Zero Trust, including enforcing strong authentication and authorization measures where you verify explicitly, use least privilege access, and always assume breach (more details at Microsoft’s Zero Trust site.) In addition to Prompt Shields, use input sanitization in the AI System to block malicious prompts and sanitize ASCII smuggling attempts using a pre-processing layer (i.e. using Azure Prompt Flows, or using Azure Functions). Use retrieval isolation to prevent similar events from propagating to knowledge sources and tools, multiple retrieval isolation strategies can be adapted including separating user’s raw-input from system-safe input and utilizing the system-safe inputs as bases to build queries and populate fields (i.e. arguments) to invoke tools the AI System interacts with. Implement functional guardrails to separate model reasoning from tool-execution, multiple strategies can be adapted to implement function guardrails including retrieval isolation (discussed earlier) and separating the decision making layer to call a tool from the LLM itself. In this case, the LLM will receive the user prompt (request and context) and will then reason that it need to invoke a specific tool, then the request is sent to an orchestration layer that will validate the request and run policy and safety checks, and then initiates the tool execution. Suggested Additional Reading: Microsoft Azure Functions Documentation https://aka.ms/azureFunctionsDocs Microsoft Azure AI Content Safety https://aka.ms/aiContentSafety Microsoft Azure AI Content Safety Prompt Shields https://aka.ms/aiPromptShields Microsoft AI Red Team https://aka.ms/aiRedTeam Microsoft Azure API Management Documentation https://aka.ms/azureAPIMDocs Microsoft Azure Front Door https://aka.ms/azureFrontDoorDocs Microsoft Azure Machine Learning Prompt Flow https://aka.ms/azurePromptFlowDocs Microsoft Azure Web Application Firewall https://aka.ms/azureWAF Microsoft Defender for AI Alerts https://aka.ms/d4aiAlerts Microsoft Defender for AI Documentation Homepage https://aka.ms/d4aiDocs Microsoft Entra Conditional Access Documentation https://aka.ms/EntraConditionalAccess Microsoft Foundry Models quotas and limits https://aka.ms/FoundryQuotas Microsoft Sentinel Documentation Home page: https://aka.ms/SentinelDocs Protect and modernize your organization with a Zero Trust strategy: https://aka.ms/ZeroTrust. The 5 generative AI security threats you need to know about e-book https://aka.ms/genAItop5Threats Microsoft’s open automation framework to red team generative AI Systems https://aka.ms/PyRIT
Disable Defender for Cloud Apps alerts
Hi all, we just enabled Defender for Cloud Apps in our environment (about 500 clients). We started with setting about 300 apps to "Unsanctioned". Now we get flooded with alerts. Mainly "Connection to a custom network indicator on one endpoint" and "Multi-stage incident on multiple endpoints" when an URL is blocked on more clients. Is there a possibility to disable the alerts for this kind of blocks? I tried creating a supression rules, but didnt manage to get it working. Dont know if it is not possible or if I made a mistake. As the Defender for Cloud Apps just creates a Indicator for every app i want to block I could click every single Indicator and disable the alert there. But thats a few hundred Indicators and we plan to extend the usage. Can I centrally disable alerts for custom indicators? Thanks & Cheers
