ai-agent-posture

name	ai-agent-posture
description	Use this skill when asked to audit, assess, or report on AI agent security posture across Copilot Studio, Microsoft 365 Copilot, Microsoft Foundry, and third-party agents. Triggers on keywords like "AI agent posture", "agent security audit", "Copilot Studio agents", "agent inventory", "agent access", "broadly accessible agents", "agent tools", "MCP tools on agents", "agent knowledge sources", "XPIA risk", "agent sprawl", "AI agent risk", "agent governance", or when investigating AI agent configurations, access posture, tool permissions, or credential exposure. This skill queries the AgentsInfo table in Advanced Hunting to produce a comprehensive security posture assessment covering agent inventory, access posture, broadly-accessible agent exposure, MCP tool proliferation, knowledge source exposure, XPIA email exfiltration risk, hard-coded credential detection, external endpoint risks, creator governance, and agent sprawl analysis. Supports inline chat and markdown file output.
threat_pulse_domains	["admin","cloud"]
drill_down_prompt	Run AI agent security audit — agent inventory, authentication gaps, tool permissions

AI Agent Security Posture — Instructions

Purpose

This skill audits the security posture of AI agents (Copilot Studio, Microsoft 365 Copilot / Agent Builder, Microsoft Foundry, and third-party platforms) across your organization using the AgentsInfo table in Microsoft Defender XDR Advanced Hunting.

🔄 Table migration (AIAgentsInfo → AgentsInfo): This skill was migrated from the deprecated AIAgentsInfo table to the unified multi-platform AgentsInfo table. AIAgentsInfo remains queryable until July 1, 2026, but it is Copilot Studio-only and uses a different schema. All queries in this skill target AgentsInfo. The new table is a different data model, not a rename — see Table Schema Reference and Known Pitfalls for the differences that shaped these queries.

AI agents are autonomous or semi-autonomous applications that can access organizational data, send emails, call external APIs, and use MCP tools. Misconfigured agents — missing authentication, overly broad access, AI-controlled email sending, hard-coded credentials — represent a growing attack surface. This skill systematically evaluates that surface.

What this skill covers:

Domain	Key Questions Answered
🔍 Agent Inventory	How many agents exist? What's their status, platform, environment?
🔐 Access Posture	Which agents are broadly accessible (allowForAllUsers)? How are agents shared (appType: lob/shared)?
🛠️ Tools & MCP	Which agents have MCP tools? What operations can they perform?
📚 Knowledge Sources	What data sources are agents connected to?
📧 XPIA Email Risk	Which agents can send email (data exfil precondition)?
🔑 Credential Exposure	Are credentials hard-coded in agent instructions or connector metadata?
🌐 External Endpoint Risk	What external hosts do agent connectors reach? Any insecure schemes or non-standard ports?
👥 Creator Governance	Who creates agents? Is there naming hygiene? Abandoned agents?

Data source: AgentsInfo table (Advanced Hunting) — currently in Preview.

References:

• Microsoft Docs — AgentsInfo table
• From runtime risk to real-time defense: Securing AI agents — Microsoft Defender Security Research blog detailing three attack scenarios this skill detects
• Microsoft Agent 365: The control plane for AI agents — Enterprise governance platform for agent lifecycle management (Registry, Access Control, Visualization, Interoperability, Security)
• Securing Copilot Studio agents with Microsoft Defender
• Real-time agent protection during runtime (Preview)

🔴 URL Registry — Canonical Links for Report Generation

MANDATORY: When generating reports, copy URLs verbatim from this registry. NEVER construct, guess, or paraphrase a URL. If a URL is not in this registry, omit the hyperlink entirely and use plain text.

Label	Canonical URL
BLOG_RUNTIME_RISK	https://www.microsoft.com/en-us/security/blog/2026/01/23/runtime-risk-realtime-defense-securing-ai-agents/
BLOG_AGENT_365	https://www.microsoft.com/en-us/microsoft-365/blog/2025/11/18/microsoft-agent-365-the-control-plane-for-ai-agents/
DOCS_AGENTSINFO	https://learn.microsoft.com/en-us/defender-xdr/advanced-hunting-agentsinfo-table
DOCS_AGENT_PROTECTION	https://learn.microsoft.com/en-us/defender-cloud-apps/ai-agent-protection
DOCS_RUNTIME_PROTECTION	https://learn.microsoft.com/en-us/defender-cloud-apps/real-time-agent-protection-during-runtime

Usage in reports: When referencing attack scenarios, link to BLOG_RUNTIME_RISK. When referencing Agent 365 governance, link to BLOG_AGENT_365. When referencing runtime protection, link to DOCS_RUNTIME_PROTECTION.

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Threat Landscape: Why AI Agent Posture Matters

Microsoft Defender Security Research has identified that AI agents represent a fundamentally new attack surface where the agent's capabilities are effectively equivalent to code execution. When a tool is invoked, it can read/write data, send emails, update records, or trigger workflows — and an attacker who can influence the agent's plan can indirectly cause the execution of unintended operations within the agent's capability sandbox.

The core risk: the agent's orchestrator depends on natural language input to determine which tools to use and how to use them. This creates exposure to prompt injection and reprogramming failures, where malicious prompts, embedded instructions, or crafted documents can manipulate the decision-making process.

This skill's queries map directly to three attack scenarios documented by Microsoft:

Attack Scenario 1: Malicious Instruction Injection via Event-Triggered Workflow

Element	Detail
Vector	Crafted email sent to an agent-monitored mailbox (event trigger)
Mechanism	Email contains hidden instructions telling the agent to search knowledge base for sensitive data and exfiltrate via email to attacker
Preconditions	Agent can send email (email connector) + has an event/email trigger + a knowledge source
Detection	Q5 (XPIA Email Risk) detects email-capable agents via connector operations; Q7 (Knowledge Sources) identifies data exposure
Skill Signal	Agents with an email-send operation (e.g., Office 365 Outlook Send an email (V2)) + knowledge sources, especially if broadly accessible (allowForAllUsers == "true") = highest risk

Attack Scenario 2: Prompt Injection via Shared Document → Email Exfiltration (XPIA)

Element	Detail
Vector	Malicious insider edits a SharePoint document with crafted instructions
Mechanism	Agent processing the document is tricked into reading a sensitive file on a different SharePoint site (that the agent has access to but the attacker doesn't) and emailing contents to attacker-controlled domain
Preconditions	Agent has a knowledge/data source + an email-send connector operation
Detection	Q5 (XPIA) + Q7 (Knowledge Sources) identifies the attack surface
Skill Signal	A declared data source + an email-send operation (e.g., Send an email (V2)) on the same agent = classic XPIA vector

Attack Scenario 3: Capability Reconnaissance on Unauthenticated Agent

Element	Detail
Vector	Attacker interacts with publicly accessible chatbot (no authentication required)
Mechanism	Series of crafted prompts to probe and enumerate the agent's tools and knowledge sources, then exploit them to extract sensitive data
Preconditions	Agent is broadly accessible (allowForAllUsers == "true", e.g., shared tenant-wide or website embed)
Detection	Q4 (Broadly-Accessible Agents) identifies exposed agents; cross-reference with Q7 (knowledge sources with customer data)
Skill Signal	allowForAllUsers == "true" + knowledge sources containing sensitive data = reconnaissance target

⚠️ Authentication-type telemetry gap: The deprecated AIAgentsInfo table exposed UserAuthenticationType (None/Integrated/Custom), which let this skill directly flag unauthenticated agents. The new AgentsInfo table has no populated authentication-type column in current telemetry (ToolsAuthenticationType is empty). The closest available exposure signal is RawAgentInfo.allowForAllUsers == "true" (broadly accessible to all tenant users). This is a proxy, not an equivalent — it measures broad reach, not absence of authentication. Treat broadly-accessible agents as the highest-exposure cohort and recommend Entra-based access policies (Agent 365) to close the gap.

Mitigation: Defender Runtime Protection

Microsoft Defender provides webhook-based runtime inspection for Copilot Studio agents. Before every tool, topic, or knowledge action is executed, the generative orchestrator sends a webhook to Defender containing the planned invocation context. Defender analyzes intent and destination in real time and can allow or block the action before execution.

This is the primary runtime defense against all three scenarios above. When reviewing posture findings from this skill, always recommend enabling Defender Runtime Protection for agents flagged as high-risk. See Real-time agent protection during runtime.

Governance Framework: Microsoft Agent 365

Microsoft Agent 365 is the enterprise control plane for AI agents — the platform-level answer to the governance gaps this skill detects. It provides five capabilities that directly map to this skill's risk dimensions:

Agent 365 Capability	What It Does	Skill Dimensions Addressed
1. Registry	Single source of truth for all agents (Entra agent ID). IT can quarantine unsanctioned agents and detect shadow agents. Agent Store for governed discovery.	Agent Inventory (Q1), Creator Governance (Q10), Agent Sprawl (Q11)
2. Access Control	Unique agent IDs via Entra. Agent Policy Templates enforce security from day one. Adaptive, risk-based access policies. Least-privilege enforcement.	Broadly-Accessible Agents (Q4), Access Posture (Q3)
3. Visualization	Unified dashboard mapping agents ↔ users ↔ resources. Role-based reporting. Compliance logging, e-discovery, and audit trail.	MCP Tool Exposure (Q6), Knowledge Sources (Q7), Creator Governance (Q10)
4. Interoperability	Agents access Work IQ (org data, relationships, context). Works across Copilot Studio, Microsoft Foundry, Agent Framework, Agent 365 SDK, and partner platforms.	Knowledge Source Risk (Q7), Tools Inventory (Q12)
5. Security	Defense-in-depth via Microsoft Defender (posture + threat detection + runtime protection), Entra (real-time blocking), and Purview (data exposure risk, sensitive data leak prevention, compliance).	XPIA Email Risk (Q5), Credential Hygiene (Q8), External Endpoint Risk (Q9)

How to reference Agent 365 in reports: When this skill identifies governance gaps (sprawl, missing authentication, uncontrolled tool access), recommend Agent 365 as the strategic platform to address them. Specific mappings:

• Agent sprawl / no naming conventions → Agent 365 Registry + quarantine for unsanctioned agents
• Missing access controls / broadly-accessible agents → Agent 365 Access Control + Entra agent IDs + Policy Templates
• No visibility into agent-resource connections → Agent 365 Visualization dashboard
• Uncontrolled MCP/tool proliferation → Agent 365 Security + Defender posture management
• XPIA / data exfiltration risk → Agent 365 Security + Purview for real-time data leak prevention

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📑 TABLE OF CONTENTS

1. Critical Workflow Rules — Mandatory rules
2. Table Schema Reference — AgentsInfo columns and data types
3. Agent Security Score Formula — Composite risk scoring
4. Execution Workflow — Phase-by-phase query plan
5. Sample KQL Queries — All queries (Q1–Q12)
6. Output Modes — Inline vs Markdown report
7. Inline Report Template — Chat-rendered format
8. Markdown File Report Template — Disk-saved format
9. Known Pitfalls — Schema quirks and edge cases
10. Quality Checklist — Pre-delivery validation
11. SVG Dashboard Generation — Visual dashboard from report

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⚠️ CRITICAL WORKFLOW RULES - READ FIRST ⚠️

1. ALWAYS use RunAdvancedHuntingQuery — The AgentsInfo table is an Advanced Hunting table. It is NOT available in Sentinel Data Lake (query_lake). All queries in this skill MUST use RunAdvancedHuntingQuery.

2. ALWAYS deduplicate agents with arg_max — The table contains multiple records per agent (state snapshots over time). Every query that analyzes current agent state MUST use | summarize arg_max(Timestamp, *) by AgentId to get the latest record per agent. Note AgentId is a guid.

3. ALWAYS exclude deleted agents (unless specifically auditing deletions) — Add | where LifecycleStatus != "Deleted" after deduplication. LifecycleStatus is blank for active agents and only set to Deleted for removed ones, so this filter keeps active agents.

4. ASK the user for output format before generating the report:

   • Inline chat summary (quick review in chat)
   • Markdown file report (detailed, archived to reports/ai-agent-posture/)
   • Both (markdown + inline summary)

5. ⛔ MANDATORY: Evidence-based analysis only — Report ONLY what query results show. Use the explicit absence pattern (✅ No [finding] detected) when queries return 0 results. Never guess or assume.

6. 🔴 The rich agent detail lives in RawAgentInfo (dynamic), not in flat columns — Governance signals (creatorId, allowForAllUsers, appType, scope) and deep tool/connector detail (declarativeCopilotMetadata) are nested inside the RawAgentInfo dynamic column. The normalized columns (DeclaredTools, McpServers, DeclaredDataSources) are sparse and flat. Parse RawAgentInfo with mv-expand/dot-notation — never assume a flat column holds the value. See Known Pitfalls.

7. Run queries in parallel batches where possible — Phase 1 queries (Q1–Q3) are independent and can run in parallel. Phase 2 queries (Q4–Q9) are independent and can run in parallel. Phase 3 (Q10–Q12) can run in parallel.

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Table Schema Reference

The AgentsInfo table (Preview) contains configuration snapshots of AI agents across Copilot Studio, Microsoft 365 Copilot (Agent Builder), Microsoft Foundry, and third-party platforms. The schema below reflects the live table (which differs from the published docs in several places — column casing, types, and which columns are actually populated).

Top-level columns

Column	Type	Description
Timestamp	datetime	Last recorded date/time for this agent snapshot
AgentId	guid	Unique agent identifier (dedup key)
Name	string	Display name of the agent
Description	string	Agent description
Platform	string	Copilot Studio, Agent Builder in Microsoft 365 Copilot, Microsoft Foundry, Other, SharePoint, Amazon Bedrock, LocalAgents
Version	string	Agent version
PublishedStatus	string	Published, Draft
LifecycleStatus	string	Blank for active agents; Deleted for removed agents
CreatedDateTime	datetime	When the agent was created
LastUpdatedDateTime	datetime	When last updated
LastPublishedDateTime	datetime	When last published
Owners	dynamic	Owner identities (sparse)
SharedWith	dynamic	Sharing targets (sparse)
InstanceCount	int	Blueprint instance count
Instructions	string	System prompt / agent instructions (well populated)
Model	string	Backing LLM model (sparse)
Capabilities	dynamic	Declared capabilities (sparse)

⚠️ Columns that are EMPTY / unreliable in current telemetry

These columns exist but are not populated in observed data — do NOT build detections on them without first confirming population:

ToolsAuthenticationType (auth-type gap — see below), Availability, Endpoints, Triggers, Permissions, Model (mostly), and most of Owners/SharedWith.

🔴 Authentication-type gap: The deprecated AIAgentsInfo.UserAuthenticationType (None/Integrated/Custom) has no populated equivalent in AgentsInfo. There is no reliable way to flag "unauthenticated" agents from this table. Use RawAgentInfo.allowForAllUsers == "true" as a broad-exposure proxy (Q4) and document the gap.

RawAgentInfo nested keys (the rich data)

For Copilot Studio agents, RawAgentInfo is a marketplace/governance manifest. Key fields the queries below rely on:

Path	Meaning
RawAgentInfo.creatorId	Creator GUID (resolve to UPN via IdentityInfo join). Replaces CreatorAccountUpn. Sparse
RawAgentInfo.allowForAllUsers	"true" = broadly accessible to all tenant users (exposure signal). Replaces AccessControlPolicy == "Any"
RawAgentInfo.appType	lob (line-of-business, owner-scoped), shared, thirdParty, firstParty
RawAgentInfo.scope	Sharing scope (e.g., tenant)
RawAgentInfo.declarativeCopilotMetadata	Deep connector/tool detail (DCM). Present only for the connector-sourced subset (~10% of Copilot Studio agents)

DCM nesting (recovers deep tool, operation, and endpoint detail):

RawAgentInfo.declarativeCopilotMetadata[]
  .actions[]
    .apis[]            // .type = OpenApi | RemoteMCPServer | api_action
      .serverUrls[]    // populated for OpenApi + RemoteMCPServer (external hosts)
      .operations[]
        .operationId   // e.g., "Office 365 Outlook Send an email (V2)"

DCM siblings also carry instructions, llmModels (model), and sourceIds (incl. EnvironmentId, SourceAgentId).

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Agent Security Score Formula

The Agent Security Score is a composite risk indicator that summarizes the security posture of an organization's AI agent fleet. Higher scores indicate greater risk.

Scoring Dimensions

$$ \text{AgentSecurityScore} = \sum_{i} \text{DimensionScore}_i $$

Each dimension contributes 0–20 points to a maximum of 100:

Dimension	Max	🟢 Low (0–5)	🟡 Medium (6–12)	🔴 High (13–20)
Broadly-Accessible Agents	20	0 agents with allowForAllUsers == "true"	1–2 broadly-accessible agents	≥3 broadly-accessible agents, especially if Published with knowledge sources or email capability
XPIA Email Risk	20	0 email-capable agents	1–2 email-capable agents (scoped access)	≥1 email-capable agent that is also broadly accessible or has knowledge sources
Tool & Endpoint Exposure	20	0–2 MCP agents, known creators, no external endpoints	3–10 MCP agents, external endpoints all HTTPS/standard-port	>10 MCP agents, OR MCP/endpoint agents that are broadly accessible, OR any insecure-scheme / non-standard-port external endpoint (Q9 escalators)
Knowledge Source Risk	20	0 agents with data sources + broad access	1–3 agents with data sources + scoped access	Agents with data sources + allowForAllUsers == "true". Compounding rule: When agents have data sources + an email-send operation + broad access (the full XPIA chain from Q5 + Q7), score at maximum (20) for this dimension AND score XPIA Email Risk at maximum (20) — the combination is the documented attack pattern
Credential Hygiene	20	0 credential patterns detected	Patterns found but agent is Draft (unpublished)	Patterns found in Published agents

Interpretation Scale

Score	Rating	Action
0–20	✅ Healthy	Normal posture, no immediate concerns
21–45	🟡 Elevated	Review — minor misconfigurations detected
46–70	🟠 Concerning	Investigate — multiple risk signals present
71–100	🔴 Critical	Immediate remediation — significant agent security risk

The Tool & Endpoint Exposure dimension folds external-endpoint risk (Q9) into the MCP exposure signal: an insecure scheme, a non-standard port, or an external endpoint on a broadly-accessible agent each escalates this dimension to its High tier regardless of MCP count.

Supplementary Indicators (not summed into the /100 score)

Two indicators are reported alongside the composite score for added context. They are intentionally not added to the /100 total — they enrich interpretation and feed the dimensions above as evidence.

Indicator	Source	What it tells you
Capability Privilege Index	Q13	Count of agents holding ≥1 sensitive operation (mail-send, directory-write, data-write, messaging). Split by broad access. A high count of broadly-accessible + sensitive-op agents is the strongest privilege-abuse signal and should justify maxing the Broad Access and/or XPIA dimensions.
Deep-Manifest Coverage	Q14	Percentage of the fleet carrying declarativeCopilotMetadata (DCM). Because the XPIA, endpoint, and capability queries depend on DCM, this is the fraction of the estate that was fully inspectable. Every report MUST surface this so the analyst knows what was not inspected.

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Execution Workflow

Phase 0: Prerequisites

1. Confirm RunAdvancedHuntingQuery is available (AgentsInfo is AH-only)
2. Ask user for output format (inline / markdown / both)

Phase 1: Inventory & Overview (Q1–Q3)

Run in parallel — no dependencies between queries.

Query	Purpose
Q1	Global inventory summary (counts, date range, platforms, creators)
Q2	Status and platform breakdown
Q3	Access posture distribution (appType / allowForAllUsers)

Phase 2: Security Risk Analysis (Q4–Q9)

Run in parallel — no dependencies between queries.

Query	Purpose
Q4	Broadly-accessible agents (allowForAllUsers == "true" detail)
Q5	XPIA email exfiltration risk (email-send connector operations)
Q6	MCP tool inventory across agents
Q7	Knowledge / data source audit
Q8	Hard-coded credential scan
Q9	External endpoint & HTTP risk (connector serverUrls)

Phase 3: Governance & Trends (Q10–Q12)

Run in parallel — no dependencies between queries.

Query	Purpose
Q10	Top creators and naming hygiene
Q11	Agent creation trend over time
Q12	Capability / tools inventory (all operation types)
Q13	Operation-level privilege mapping (sensitive-operation matrix → Capability Privilege Index)
Q14	Deep-manifest coverage (% of fleet with DCM → report coverage banner)

Phase 4: Score Computation & Report Generation

1. Compute per-dimension scores from Phase 1–3 data
2. Sum dimension scores for composite Agent Security Score
3. Generate report in requested output mode
4. Report total elapsed time

Phase 5: Runtime Correlation (Optional)

AgentsInfo describes how agents are configured; it does not show whether they are actually used or what they do at runtime. To close that gap, correlate the flagged configuration set against the CopilotActivity table (all-surface AI activity log, available in Advanced Hunting).

When to run: After Phase 4, when the user wants to know which flagged agents are actually active, which are dormant, or whether a high-risk agent shows runtime behavior.

🔴 Use a SCOPED lookup, never a fleet-wide join. A leftouter/inner join of the full AgentsInfo fleet (~15k agents, heavy RawAgentInfo dynamic) against CopilotActivity (100k+ rows) times out the Advanced Hunting endpoint. Instead:

1. Phase 4 produces a small flagged-agent NAME list (broadly-accessible from Q4 + sensitive-op agents from Q13 — typically <50 names).
2. Filter CopilotActivity to that name set with where AgentName in (FlaggedNames) — light, no join. See Query 15.

Join-key pitfall: CopilotActivity.AgentId is a composite/prefixed string (e.g., T_<tenant>.<guid>, CopilotStudio.Declarative.T_….gpt.<guid>, or literals like AgentBuilder) — it does not equal the clean AgentsInfo.AgentId GUID, so ID-based joins return 0 matches. AgentName is the reliable correlation key. Also note most CopilotActivity rows have an empty AgentId/AgentName (general M365 Copilot usage, not declarative-agent-attributed), so runtime attribution is inherently low-coverage — absence from CopilotActivity does NOT prove an agent is dormant.

Two high-value correlations:

• Active-and-dangerous — a flagged agent (broadly accessible / XPIA-exposed / sensitive ops) that ALSO appears in CopilotActivity with real interactions → highest remediation priority (Query 15).
• Configured-but-dormant — a flagged agent absent from CopilotActivity over the window → lower urgency, candidate for decommissioning (caveat: attribution gaps above).

For deeper runtime reconstruction (data accessed, tools invoked, jailbreak detections), hand off to the dedicated query library queries/cloud/copilot_activity_investigation.md rather than duplicating queries here.

Keep this phase thin and scoped: the posture skill owns configuration assessment; copilot_activity_investigation.md owns runtime reconstruction. Reference, don't duplicate.

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Sample KQL Queries

All queries below are validated against the live AgentsInfo table. Use them exactly as written, substituting only where noted. Because the rich agent detail lives in the RawAgentInfo dynamic column, several queries parse RawAgentInfo.declarativeCopilotMetadata (DCM). DCM is present only for the connector-sourced subset of agents — queries that depend on it carry a coverage caveat.

Query 1: Global Inventory Summary

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| extend CreatorId = tostring(RawAgentInfo.creatorId)
| summarize
    UniqueAgents = dcount(AgentId),
    EarliestRecord = min(Timestamp),
    LatestRecord = max(Timestamp),
    Published = countif(PublishedStatus == "Published"),
    Draft = countif(PublishedStatus == "Draft"),
    Deleted = countif(LifecycleStatus == "Deleted"),
    UniquePlatforms = dcount(Platform),
    UniqueCreators = dcount(CreatorId)

Note: UniqueCreators counts only agents with a populated RawAgentInfo.creatorId (the connector-sourced subset). It under-counts true creators; treat it as a lower bound.

Query 2: Status & Platform Breakdown

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| summarize AgentCount = count() by Platform, PublishedStatus
| order by AgentCount desc

⚠️ Authentication-type gap: The deprecated AIAgentsInfo table broke this down by UserAuthenticationType. AgentsInfo has no populated authentication-type column, so this query reports status by platform instead. For exposure, use Q3 (access posture) and Q4 (broadly-accessible agents).

Query 3: Access Posture Distribution

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| extend AppType = tostring(RawAgentInfo.appType),
         AllowAllUsers = tostring(RawAgentInfo.allowForAllUsers)
| summarize AgentCount = count() by Platform, AppType, AllowAllUsers
| order by AgentCount desc

Interpretation: appType == "lob" (line-of-business) agents are owner-scoped; appType == "shared" are shared more widely. allowForAllUsers == "true" (any platform) is the broad-exposure signal — these reach every tenant user. This replaces the old AccessControlPolicy distribution.

Query 4: Broadly-Accessible Agents

🔴 Security-critical query — agents with allowForAllUsers == "true" are accessible to all tenant users. This is the closest available proxy for the old "unauthenticated / Any access" exposure signal (see the authentication-type gap).

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| extend AllowAllUsers = tostring(RawAgentInfo.allowForAllUsers),
         AppType = tostring(RawAgentInfo.appType),
         CreatorId = tostring(RawAgentInfo.creatorId)
| where AllowAllUsers == "true"
| project Name, Platform, PublishedStatus, AppType, CreatorId, AgentId, CreatedDateTime, Description
| order by PublishedStatus asc, CreatedDateTime desc

Post-processing: For each broadly-accessible agent, note:

• Is it Published (active) or Draft?
• Cross-reference with Q5 (email-capable) and Q7 (knowledge sources) for compounding XPIA / reconnaissance risk.

🔴 Capability Reconnaissance Risk (Attack Scenario 3): Broadly-accessible agents are prime targets for adversarial probing. Published agents with knowledge sources containing customer/internal data are the highest-priority findings.

Query 5: XPIA Email Exfiltration Risk (Email-Capable Agents)

🔴 Security-critical query — agents that can send email via a connector operation. A successful prompt-injection (XPIA) attack could direct the agent to exfiltrate data to arbitrary recipients.

Coverage caveat: Detects email-send operations declared in RawAgentInfo.declarativeCopilotMetadata (DCM). DCM is present only for the connector-sourced agent subset. The old IsGenerativeOrchestrationEnabled flag and action-level inputs (AI-controlled vs hardcoded recipient) are not available in AgentsInfo — this query identifies capability, not orchestration mode.

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| where isnotempty(tostring(RawAgentInfo.declarativeCopilotMetadata))
| mv-expand DCM = RawAgentInfo.declarativeCopilotMetadata
| mv-expand Action = DCM.actions
| mv-expand Api = Action.apis
| mv-expand Op = Api.operations
| extend OperationId = tostring(Op.operationId)
| where OperationId has "Send an email" or OperationId has "SendEmail"
| extend AllowAllUsers = tostring(RawAgentInfo.allowForAllUsers),
         CreatorId = tostring(RawAgentInfo.creatorId)
| summarize EmailOperations = make_set(OperationId)
    by AgentId, Name, Platform, PublishedStatus, AllowAllUsers, CreatorId
| order by AllowAllUsers desc, PublishedStatus asc

Post-processing:

• AllowAllUsers == "true" → email-capable and broadly accessible = highest XPIA risk (any tenant user can trigger the chain).
• Cross-reference with Q7: an email-capable agent that also has knowledge/data sources is the documented XPIA exfiltration pattern (Attack Scenario 2). Prioritize these for Defender Runtime Protection.

🔴 Attack Scenario Mapping: This query detects the agent-configuration precondition (email-send capability) for two documented scenarios — Malicious Instruction Injection via Event Trigger and Prompt Injection via Shared Document. Broadly-accessible email-capable agents (no access restriction + email) are the most dangerous.

Query 6: MCP Tool Inventory Across Agents

🟠 Governance query — MCP servers give agents access to external systems, Graph API, Sentinel data, and more. Uncontrolled MCP proliferation increases the attack surface. AgentsInfo exposes a dedicated McpServers column (cleaner than the old tool-detail parse).

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| where array_length(McpServers) > 0
| mv-expand Mcp = McpServers
| extend McpName = tostring(Mcp.name)
| extend CreatorId = tostring(RawAgentInfo.creatorId),
         AllowAllUsers = tostring(RawAgentInfo.allowForAllUsers)
| summarize McpServerList = make_set(McpName), McpToolCount = dcount(McpName)
    by AgentId, Name, Platform, CreatorId, AllowAllUsers
| order by McpToolCount desc

MCP server distribution (which servers appear on the most agents):

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| where array_length(McpServers) > 0
| mv-expand Mcp = McpServers
| summarize AgentCount = dcount(AgentId) by McpServer = tostring(Mcp.name)
| order by AgentCount desc

Note: McpServers is flat ({name, description} only) — no server URLs or credential config. For external MCP endpoint detail (host/scheme/port), use Q9, which parses RemoteMCPServer serverUrls from DCM.

Query 7: Knowledge / Data Source Audit

🟡 Data exposure query — identifies what data sources agents declare. In AgentsInfo, declared sources appear in the DeclaredDataSources column as an array of source/filename strings.

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| where array_length(DeclaredDataSources) > 0
| mv-expand DS = DeclaredDataSources
| extend DataSource = tostring(DS)
| extend AllowAllUsers = tostring(RawAgentInfo.allowForAllUsers),
         CreatorId = tostring(RawAgentInfo.creatorId)
| summarize DataSources = make_set(DataSource), SourceCount = dcount(DataSource)
    by AgentId, Name, Platform, AllowAllUsers, CreatorId
| order by SourceCount desc

Post-processing — flag high-risk combinations:

• Data sources + allowForAllUsers == "true" → internal data potentially exposed broadly.
• Any data source on an agent that is also email-capable (Q5) → XPIA exfiltration chain.

Coverage caveat: DeclaredDataSources is sparse and stores source names/filenames (e.g., Priority-Banking-Policy.docx), not the richer $kind/site structure the old KnowledgeDetails column held. Source type classification (SharePoint vs public site vs federated) is not reliably available — report the declared source names and flag broadly-accessible agents that carry any.

For actual (runtime) SharePoint sites accessed by agents — not just declared config — see copilot_activity_investigation.md Queries 14–16: CopilotActivity.AccessedResources.SiteUrl gives the agent-attributed site list, and CloudAppEvents (filtered to the validated Power Virtual Agents / Enterprise Copilot Platform client-app fingerprint) gives the full SharePoint audit trail (ClientIP, exact file, FileDownloaded granularity) for the same OBO access.

🔴 Document Injection Risk (Attack Scenario 2): Data sources are the primary vector for indirect prompt injection (XPIA). Cross-reference with Q5: agents that combine declared data sources with an email-send operation are the textbook XPIA exfiltration pattern — flag these as highest priority in the Knowledge Source Risk dimension.

Query 8: Hard-Coded Credential Scan

🔴 Security-critical query — scans agent Instructions and the connector metadata in RawAgentInfo for patterns matching API keys, JWTs, Basic auth headers, and embedded credentials.

let suspicious_patterns = @"(AKIA[0-9A-Z]{16})|(AIza[0-9A-Za-z_\-]{35})|(xox[baprs]-[0-9a-zA-Z]{10,48})|(ghp_[A-Za-z0-9]{36,59})|(sk_(live|test)_[A-Za-z0-9]{24})|(SG\.[A-Za-z0-9]{22}\.[A-Za-z0-9]{43})|(eyJ[A-Za-z0-9_\-]+\.[A-Za-z0-9_\-]+\.[A-Za-z0-9_\-]+)|(Authorization\s*:\s*Basic\s+[A-Za-z0-9=:+]+)|([A-Za-z]+:\/\/[^\/\s]+:[^\/\s]+@[^\/\s]+)";
AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| extend Haystack = strcat(tostring(Instructions), " ", tostring(RawAgentInfo.declarativeCopilotMetadata))
| where Haystack matches regex suspicious_patterns
| project Name, Platform, PublishedStatus,
          CreatorId = tostring(RawAgentInfo.creatorId), AgentId

Post-processing:

• Published agents with credential matches = immediate remediation required.
• Recommend Azure Key Vault + environment variables instead of hard-coded secrets.
• The JWT (eyJ...) and url://user:pass@host patterns can false-positive on example payloads — manually review each match.

Query 9: External Endpoint & HTTP Risk

🟠 Network risk query — inventories the external hosts that agent connectors reach, and flags insecure schemes or non-standard ports. External endpoints are declared in DCM apis[].serverUrls for OpenApi and RemoteMCPServer connector types (these are populated; api_action Power Platform connectors abstract the URL and are not covered).

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| where isnotempty(tostring(RawAgentInfo.declarativeCopilotMetadata))
| mv-expand DCM = RawAgentInfo.declarativeCopilotMetadata
| mv-expand Action = DCM.actions
| mv-expand Api = Action.apis
| extend ApiType = tostring(Api.type)
| where ApiType in ("OpenApi", "RemoteMCPServer")
| mv-expand Url = Api.serverUrls
| extend Url = tostring(Url)
| where isnotempty(Url)
| extend Host = tostring(parse_url(Url).Host),
         Port = tostring(parse_url(Url).Port),
         Scheme = tostring(parse_url(Url).Scheme)
| extend NonStandardPort = isnotempty(Port) and Port !in ("443", "80", ""),
         InsecureScheme = Scheme != "https"
| project Name, Platform, ApiType, Scheme, Host, Port, Url,
          NonStandardPort, InsecureScheme,
          AllowAllUsers = tostring(RawAgentInfo.allowForAllUsers)
| order by NonStandardPort desc, InsecureScheme desc, Host asc

Post-processing:

• InsecureScheme == true (non-HTTPS) or NonStandardPort == true → review the connector; data may transit insecurely.
• Unfamiliar external hosts on broadly-accessible agents (AllowAllUsers == "true") → highest priority.

Coverage caveat: Only OpenApi + RemoteMCPServer connectors declare serverUrls. Power Platform api_action connectors (the majority) do not expose a URL here, so their destinations are not inventoried by this query. The old topic-level HttpRequestAction parsing is not applicable to AgentsInfo.

Query 10: Top Creators & Naming Hygiene

👥 Governance query — identifies prolific agent creators and names lacking descriptiveness. Creator is a GUID in RawAgentInfo.creatorId; resolve to UPN via an IdentityInfo join.

let IdMap = materialize(IdentityInfo
    | where isnotempty(AccountObjectId) and isnotempty(AccountUpn)
    | distinct AccountObjectId, AccountUpn);
AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| extend CreatorId = tostring(RawAgentInfo.creatorId)
| where isnotempty(CreatorId)
| join kind=leftouter IdMap on $left.CreatorId == $right.AccountObjectId
| extend CreatorUpn = coalesce(AccountUpn, CreatorId)
| summarize
    AgentCount = count(),
    PublishedCount = countif(PublishedStatus == "Published"),
    GenericNameCount = countif(Name in~ ("Agent", "agent", "Test", "test", "New Agent")),
    NoDescriptionCount = countif(isempty(Description)),
    AgentNames = make_set(Name, 10)
    by CreatorUpn
| order by AgentCount desc
| take 20

Coverage caveat: Only agents with a populated RawAgentInfo.creatorId are attributed. Creators whose GUID does not resolve in IdentityInfo fall back to the raw GUID. A single creator with a very high AgentCount is a sprawl signal worth investigating.

Query 11: Agent Creation Trend

📈 Trend query — shows agent creation velocity over time to detect sprawl acceleration.

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| where isnotempty(CreatedDateTime)
| summarize AgentsCreated = count() by bin(CreatedDateTime, 7d)
| order by CreatedDateTime asc

Query 12: Full Capability / Tools Inventory

🛠️ Tools governance query — catalogs the operations agents can invoke across all connector types, to understand the full capability surface. Parses DCM operations (operationId + API type).

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| where isnotempty(tostring(RawAgentInfo.declarativeCopilotMetadata))
| mv-expand DCM = RawAgentInfo.declarativeCopilotMetadata
| mv-expand Action = DCM.actions
| mv-expand Api = Action.apis
| mv-expand Op = Api.operations
| extend OperationId = tostring(Op.operationId), ApiType = tostring(Api.type)
| where isnotempty(OperationId)
| summarize AgentCount = dcount(AgentId), Agents = make_set(Name, 5) by OperationId, ApiType
| order by AgentCount desc

Alternative for non-DCM agents — the flat DeclaredTools column ({type, name}) covers agents without DCM:

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| where array_length(DeclaredTools) > 0
| mv-expand Tool = DeclaredTools
| summarize AgentCount = dcount(AgentId)
    by ToolType = tostring(Tool.type), ToolName = tostring(Tool.name)
| order by AgentCount desc

Coverage caveat: The DCM query yields deep operation-level detail but only for the connector-sourced subset. The DeclaredTools fallback is broader but flatter (tool name/type only, no operation IDs). Run both for the fullest picture.

Query 13: Operation-Level Privilege Mapping

🔐 Privilege query — buckets every declared operation into a sensitivity category (mail-send, directory-write, data-write, messaging, security-tooling, read/other) to surface where write/exfiltration capability concentrates. Feeds the Capability Privilege Index supplementary indicator.

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| where isnotempty(tostring(RawAgentInfo.declarativeCopilotMetadata))
| mv-expand DCM = RawAgentInfo.declarativeCopilotMetadata
| mv-expand Action = DCM.actions
| mv-expand Api = Action.apis
| mv-expand Op = Api.operations
| extend OperationId = tostring(Op.operationId)
| where isnotempty(OperationId)
| extend PrivilegeCategory = case(
    OperationId has_any ("Send an email", "SendEmail", "Send email"), "Mail-Send",
    OperationId has_any ("AddUserToGroup", "RemoveMember", "UpdatePerson", "UpdateOrganisation", "Create user", "Delete user", "Update user", "Assign"), "Directory-Write",
    OperationId has_any ("unbound action", "Create a row", "Update a row", "Delete a row", "Create record", "Update record"), "Data-Write",
    OperationId has_any ("Post message", "Post a message", "Send message", "Create chat", "post in a chat"), "Messaging",
    OperationId has_any ("Security Copilot", "Sentinel"), "Security-Tooling",
    "Other/Read")
| summarize AgentCount = dcount(AgentId) by PrivilegeCategory
| order by AgentCount desc

Capability Privilege Index — distinct agents holding ≥1 sensitive (write/send) operation, split by broad access:

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| where isnotempty(tostring(RawAgentInfo.declarativeCopilotMetadata))
| extend AllowAllUsers = tostring(RawAgentInfo.allowForAllUsers)
| mv-expand DCM = RawAgentInfo.declarativeCopilotMetadata
| mv-expand Action = DCM.actions
| mv-expand Api = Action.apis
| mv-expand Op = Api.operations
| extend OperationId = tostring(Op.operationId)
| where OperationId has_any ("Send an email", "SendEmail", "AddUserToGroup", "RemoveMember", "UpdatePerson", "UpdateOrganisation", "unbound action", "Create a row", "Update a row", "Delete a row", "Post message", "post in a chat")
| summarize SensitiveAgents = dcount(AgentId),
            BroadAndSensitive = dcountif(AgentId, AllowAllUsers == "true")

Interpretation: BroadAndSensitive > 0 is a direct privilege-abuse signal — a broadly-accessible agent that can write to the directory, write data, or send mail. These agents justify maxing the Broad Access and/or XPIA dimensions. Tune the operation keyword lists to your tenant's connector set.

Query 14: Deep-Manifest Coverage (Report Banner)

📊 Coverage query — reports what fraction of the fleet carries the deep declarativeCopilotMetadata (DCM) that the XPIA, endpoint, and capability queries depend on. Run this every report and surface the result as a banner so the analyst knows what was not fully inspected.

AgentsInfo
| summarize arg_max(Timestamp, *) by AgentId
| where LifecycleStatus != "Deleted"
| summarize Total = count(),
            WithDCM = countif(isnotempty(tostring(RawAgentInfo.declarativeCopilotMetadata))),
            WithInstructions = countif(isnotempty(Instructions)),
            WithObservabilityID = countif(isnotempty(ObservabilityID)),
            WithEntraAgentID = countif(isnotempty(EntraAgentID))
| extend DcmCoveragePct = round(100.0 * WithDCM / Total, 1),
         InstrCoveragePct = round(100.0 * WithInstructions / Total, 1),
         ObsIdPct = round(100.0 * WithObservabilityID / Total, 1),
         EntraIdPct = round(100.0 * WithEntraAgentID / Total, 1)

Why both ID columns: ObservabilityID is near-universally populated (~100%) and is the natural runtime-correlation handle; EntraAgentID is sparse (only agents provisioned with an Entra Agent ID). Report both so the analyst knows which runtime/identity correlations are feasible.

Query 15: Runtime Correlation — Active-and-Dangerous (Scoped)

🎯 Runtime query (Phase 5) — confirms which flagged agents are actually active. Scoped by name list — no fleet-wide join (see Phase 5 for why a full join times out). Populate FlaggedNames from the Q4 broadly-accessible and Q13 sensitive-op results.

let FlaggedNames = dynamic(["<broadly-accessible or sensitive-op agent names from Q4/Q13>"]);
CopilotActivity
| where TimeGenerated > ago(7d)
| where AgentName in (FlaggedNames)
| summarize Interactions = count(),
            DistinctUsers = dcount(ActorUserId),
            LastSeen = max(TimeGenerated),
            SrcIPs = dcount(SrcIpAddr) by AgentName
| order by Interactions desc

Interpretation: A flagged agent appearing here with real Interactions is active-and-dangerous — prioritize for remediation over dormant flagged agents. Join key is AgentName (CopilotActivity.AgentId is a composite prefixed string that does NOT equal AgentsInfo.AgentId). Absence here does not prove dormancy — most CopilotActivity rows are unattributed (empty AgentName). AIModelName is sparse in this table; do not rely on it for model inventory.

---

Output Modes

Mode 1: Inline Chat Summary

Render the full analysis directly in the chat response. Best for quick review.

Mode 2: Markdown File Report

Save a comprehensive report to disk at:

reports/ai-agent-posture/AI_Agent_Posture_Report_YYYYMMDD_HHMMSS.md

Mode 3: Both

Generate the markdown file AND provide an inline summary in chat.

Always ask the user which mode before generating output.

---

Inline Report Template

Render the following sections in order. Omit sections only if explicitly noted as conditional.

🔴 URL Rule: All hyperlinks in the report MUST be copied verbatim from the URL Registry above. Do NOT generate, recall from memory, or paraphrase any URL. If a needed URL is not in the registry, use plain text (no hyperlink).

# 🤖 AI Agent Security Posture Report

**Generated:** YYYY-MM-DD HH:MM UTC
**Data Source:** AgentsInfo (Advanced Hunting)
**Analysis Period:** <EarliestRecord> → <LatestRecord>
**Platforms:** <list discovered Platform values>

---

> 📊 **Deep-Manifest Coverage (Q14):** `<WithDCM>/<Total>` agents (**<DcmCoveragePct>%**) carry `declarativeCopilotMetadata` — the XPIA, external-endpoint, and capability findings below cover **only this subset**. Instructions present on **<InstrCoveragePct>%**, ObservabilityID on **<ObsIdPct>%** (runtime-correlation handle), EntraAgentID on **<EntraIdPct>%**. The remaining `<Total - WithDCM>` agents were inventoried but not deeply inspected.

---

## Executive Summary

<2-3 sentences: total agents, key risk findings, overall score>

**Overall Risk Rating:** 🔴/🟠/🟡/✅ <RATING> (<Score>/100)

---

## Key Metrics

| Metric | Value |
|--------|-------|
| Total Agents (non-deleted) |  |
| Published Agents |  |
| Draft Agents |  |
| Platforms Represented |  |
| Resolved Creators (lower bound) |  |
| Broadly-Accessible Agents (allowForAllUsers) |  |
| Agents with MCP Servers |  |
| Agents with Declared Data Sources |  |
| Email-Capable Agents (XPIA Risk) |  |



---






| App Type | Allow-All-Users | Count |
|----------|-----------------|-------|
|  |  |  |




| Agent Name | Platform | App Type | Published | Created |
|------------|----------|----------|-----------|---------|
|  |  |  |  |  |


✅ No broadly-accessible agents () detected.

---




| Agent Name | Platform | Email Operation | Broadly Accessible |
|------------|----------|-----------------|--------------------|
|  |  |  | 🔴 Yes / 🟢 No |


 🔴 Email-capable agents can be exploited via XPIA to exfiltrate data, especially when combined with declared data sources (Q7).
 ⚠️ Recommendation: Review recipient controls; apply Power Platform DLP and Defender Runtime Protection.




✅ No email-capable agents detected in the DCM-bearing subset.

---




| Agent Name | Platform | MCP Servers | Broadly Accessible |
|------------|----------|-------------|--------------------|
|  |  |  |  |


| MCP Server | Agent Count |
|------------|-------------|
|  |  |


✅ No agents with MCP servers detected.





---




| Agent Name | Platform | Data Sources | Broadly Accessible |
|------------|----------|--------------|--------------------|
|  |  |  |  |





✅ No declared data sources found on any agents.



---




🔴 
| Agent Name | Platform | Status | Creator |
|------------|----------|--------|---------|
|  |  |  |  |

⚠️  Move secrets to Azure Key Vault; use environment variables at runtime.


✅ No hard-coded credential patterns detected in agent instructions or connector metadata.

---




| Agent | API Type | Scheme | Host | Port | Insecure | Non-Standard Port |
|-------|----------|--------|------|------|----------|-------------------|
|  |  |  |  |  | 🔴/🟢 | 🔴/🟢 |


✅ No external endpoints with insecure schemes or non-standard ports detected.

---

Markdown File Report Template

When outputting to markdown file, use the same structure as the Inline Report Template above, saved to:

reports/ai-agent-posture/AI_Agent_Posture_Report_YYYYMMDD_HHMMSS.md

Include the following additional sections in the file report that are omitted from inline:

1. Full agent detail table (all non-deleted agents with key fields)
2. Per-platform breakdown (agent counts and creators by Platform)
3. Complete data source listing (every declared source name, not just examples)
4. Complete MCP agent listing (every MCP agent with full server list)
5. Raw query references — note that full query definitions are in this SKILL.md file

File Report Header

# AI Agent Security Posture Report

**Generated:** YYYY-MM-DD HH:MM UTC
**Data Source:** AgentsInfo (Advanced Hunting)
**Analysis Period:** <EarliestRecord> → <LatestRecord> (<N> days)
**Platforms:** <list discovered Platform values>
**Total Agents:** <N> (Published: <N>, Draft: <N>)

---

> 📊 **Deep-Manifest Coverage (Q14):** `<WithDCM>/<Total>` agents (**<DcmCoveragePct>%**) carry `declarativeCopilotMetadata`; XPIA/endpoint/capability findings cover only this subset. ObservabilityID **<ObsIdPct>%**, EntraAgentID **<EntraIdPct>%**.

---

Include the Capability Privilege Index and (if Phase 5 ran) Runtime Correlation sections from the inline template in the file report as well.

---

Known Pitfalls

1. AgentsInfo Is Advanced Hunting Only

Problem: The AgentsInfo table does NOT exist in Sentinel Data Lake. Querying via mcp_sentinel-data_query_lake returns SemanticError: Failed to resolve table.

Solution: Always use RunAdvancedHuntingQuery. The table has 30-day retention in AH.

2. Multiple Records Per Agent (State Snapshots)

Problem: The table logs configuration snapshots over time. Querying without deduplication returns inflated counts and duplicate agent entries.

Solution: Always use | summarize arg_max(Timestamp, *) by AgentId to get the latest state per agent before any analysis. Note AgentId is a guid and the column is Name/Description (not AgentName/AgentDescription as some docs state).

3. RawAgentInfo Is the Real Detail Source

Problem: The normalized columns (DeclaredTools, McpServers, DeclaredDataSources, Owners, Capabilities) are sparsely populated and flat. The rich governance/configuration detail lives in the RawAgentInfo dynamic column (populated for ~all agents) and, for the connector-sourced subset, in RawAgentInfo.declarativeCopilotMetadata (DCM).

Solution: For creator (RawAgentInfo.creatorId), broad access (RawAgentInfo.allowForAllUsers), app type (RawAgentInfo.appType), and deep capability/endpoint detail, parse RawAgentInfo. RawAgentInfo is dynamic — no double-parse needed; access nested keys directly with tostring(RawAgentInfo.key).

4. declarativeCopilotMetadata (DCM) Covers Only a Subset

Problem: Deep capability queries (Q5 email, Q9 endpoints, Q12 operations) depend on RawAgentInfo.declarativeCopilotMetadata, which is present for only ~10% of Copilot Studio agents (the connector-sourced subset). The majority have only a shallow manifest.

Solution: Always state the coverage caveat in reports. DCM path: declarativeCopilotMetadata[].actions[].apis[] with .type (OpenApi/RemoteMCPServer/api_action), .serverUrls[], and .operations[].operationId. Results from these queries are a floor, not a complete inventory.

5. Authentication-Type Detection Has No Equivalent

Problem: The old UserAuthenticationType (None/Microsoft/Custom) is gone. The ToolsAuthenticationType column exists in schema but is effectively empty (~100% blank). There is no way to classify agents as "unauthenticated" the way the old skill did.

Solution: Use RawAgentInfo.allowForAllUsers == "true" as a broad-exposure proxy (documented as a proxy, NOT an authentication state). Never claim an agent is "unauthenticated" — say "broadly accessible".

6. Many Schema Columns Are Empty in Practice

Problem: ToolsAuthenticationType, Availability, Endpoints, Triggers, Permissions, and Model are present in the schema but empty/null in practice. Queries built on them silently return 0 rows.

Solution: Do not build core logic on these columns. Validate population with a quick summarize countif(isnotempty(<col>)) before relying on a column. LifecycleStatus is blank for active agents (only Deleted is populated) — LifecycleStatus != "Deleted" correctly passes blanks.

7. creatorId Is a GUID — Join IdentityInfo for UPN

Problem: RawAgentInfo.creatorId is an Entra object GUID, not a UPN. There is no CreatorAccountUpn, LastModifiedByUpn, or LastPublishedByUpn equivalent.

Solution: Resolve via leftouter join to IdentityInfo on AccountObjectId, then coalesce(AccountUpn, CreatorId). Creator attribution is a lower bound — creatorId is sparse.

8. serverUrls Only Populated for OpenApi & RemoteMCPServer

Problem: External endpoint URLs in DCM apis[].serverUrls are populated for OpenApi and RemoteMCPServer connector types, but not for api_action (Power Platform connectors, the majority). Filtering all API types yields mostly empty URLs.

Solution: Filter ApiType in ("OpenApi", "RemoteMCPServer") before expanding serverUrls. State that api_action destinations are not inventoried.

9. McpServers Is Flat (Name/Description Only)

Problem: The dedicated McpServers column contains only {name, description} — no server URLs, credential configuration, or transport detail. Non-HTTPS MCP detection and hardcoded-cred-in-MCP detection from the old design are not possible.

Solution: Use McpServers for inventory/exposure counts only. For MCP server endpoints, fall back to the DCM RemoteMCPServer API type (Q9).

10. AH Booleans Are Textual True/False (Feb 25, 2026)

Problem: Since Feb 25, 2026, Advanced Hunting boolean results render as textual True/False, not 1/0. Governance flags from RawAgentInfo (e.g., allowForAllUsers) are JSON strings ("true"/"false").

Solution: Compare against the string form: tostring(RawAgentInfo.allowForAllUsers) == "true". Avoid == 1 / == true numeric/bool comparisons on parsed JSON values.

11. CopilotActivity Correlation — Composite AgentId & Fleet-Join Timeouts

Problem: Phase 5 runtime correlation against CopilotActivity has three traps: (1) CopilotActivity.AgentId is a composite/prefixed string (e.g., T_<tenant>.<guid>, CopilotStudio.Declarative.T_….gpt.<guid>, or literals like AgentBuilder) that does not equal the clean AgentsInfo.AgentId GUID — ID joins return 0 matches. (2) A fleet-wide AgentsInfo ↔ CopilotActivity join (~15k agents × 100k+ rows, heavy RawAgentInfo) times out the AH endpoint. (3) Most CopilotActivity rows have an empty AgentName/AgentId (general M365 Copilot usage), so runtime attribution is low-coverage.

Solution: Use a scoped name-based lookup (Query 15): build a small flagged-name list from Q4/Q13, then CopilotActivity | where AgentName in (FlaggedNames) — no join. AgentName is the reliable cross-table key. Never join the full fleet. Treat absence from CopilotActivity as unconfirmed, not proof of dormancy. AIModelName is sparse here — do not use it for model inventory.

---

Quality Checklist

Before delivering the report, verify:

• All queries used arg_max(Timestamp, *) by AgentId for deduplication
• All queries filtered LifecycleStatus != "Deleted" (unless auditing deletions)
• All queries ran via RunAdvancedHuntingQuery (not Data Lake)
• Zero-result queries are reported with explicit absence confirmation (✅ pattern)
• The Agent Security Score calculation is transparent with per-dimension evidence
• Broadly-accessible agents are described as a proxy (NOT "unauthenticated"); the auth-type gap is stated
• DCM-dependent sections (XPIA email, external endpoints, capability inventory) include the coverage caveat
• Deep-Manifest Coverage banner (Q14) is present at the top of the report (DCM %, ObservabilityID %, EntraAgentID %)
• Capability Privilege Index (Q13) is reported as a supplementary indicator, explicitly noted as NOT summed into the /100 score
• If Phase 5 ran, runtime correlation is SCOPED by AgentName (Query 15) — never a fleet-wide join; absence is described as unconfirmed, not dormant
• Score card uses the Tool & Endpoint Exposure dimension label (not "MCP Server Expo") and shows the two supplementary indicators
• MCP server inventory includes server names, not just counts
• Declared data sources note that source-type classification is unavailable
• Creator governance resolves creatorId GUIDs via IdentityInfo and notes the lower-bound caveat
• Recommendations are prioritized and evidence-based
• All hyperlinks in the report are copied verbatim from the URL Registry — no fabricated or recalled-from-memory URLs
• No PII from live environments in the SKILL.md file itself

---

SVG Dashboard Generation

📊 Optional post-report step. After an AI Agent Security Posture report is generated, the user can request a visual SVG dashboard.

Trigger phrases: "generate SVG dashboard", "create a visual dashboard", "visualize this report", "SVG from the report"

How to Request a Dashboard

• Same chat: "Generate an SVG dashboard from the report" — data is already in context.
• New chat: Attach or reference the report file, e.g. #file:reports/ai-agent-posture/AI_Agent_Posture_Report_<org>_<date>.md
• Customization: Edit svg-widgets.yaml before requesting — the renderer reads it at generation time.

Execution

Step 1:  Read svg-widgets.yaml (this skill's widget manifest)
Step 2:  Read .github/skills/svg-dashboard/SKILL.md (rendering rules — Manifest Mode)
Step 3:  Read the completed report file (data source)
Step 4:  Render SVG → save to reports/ai-agent-posture/{report_name}_dashboard.svg

The YAML manifest is the single source of truth for layout, widgets, field mappings, colors, and data source documentation. All customization happens there.