Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vinayaklatthe/microsoft-security-skills --skill purview-agent-365-securitygit clone --depth 1 https://github.com/vinayaklatthe/microsoft-security-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vinayaklatthe/microsoft-security-skills/purview-agent-365-security)<a href="https://agentmods.dev/skills/vinayaklatthe/microsoft-security-skills/purview-agent-365-security"><img src="https://agentmods.dev/badge/skills/vinayaklatthe/microsoft-security-skills/purview-agent-365-security/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vinayaklatthe/microsoft-security-skills/purview-agent-365-security"><img src="https://agentmods.dev/badge/skills/vinayaklatthe/microsoft-security-skills/purview-agent-365-security.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00110 | $0.01254 |
| Opus 5 | $0.00055 | $0.00627 |
| Sonnet 5 | $0.00022 | $0.00251 |
| Haiku 4.5 | $0.00011 | $0.00125 |
Grade A, and why
purview-agent-365-security scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Securing AI Agents with Microsoft Purview
As organisations build and deploy AI agents (Microsoft 365 Copilot agents, Copilot Studio agents, Security Copilot agents), Microsoft Purview extends data security and compliance controls to the data those agents access and produce - and to the agent identities themselves.
When to use
Governing the data-security and compliance posture of AI agents before and during rollout, especially when agents are granted access to enterprise data sources or autonomous actions.
Do not use this skill for end-user Copilot oversharing alone (use purview-copilot-oversharing)
or for prompt-level AI monitoring without agents in scope (use purview-dspm-ai).
Pick the right control per agent type
| Agent type | Primary controls |
|---|---|
| M365 Copilot agent (user-grounded) | Inherits user permissions - oversharing remediation + labels + DLP for Copilot |
| Copilot Studio agent (declarative, knowledge sources) | Restrict knowledge sources + DLP + Communication Compliance |
| Copilot Studio agent (autonomous/with actions) | All above + Entra identity governance + least-privilege actions |
| Security Copilot agent | Workspace permissions + audit + scope to specific data plug-ins |
| Third-party agent in tenant | Defender for Cloud Apps + Endpoint DLP + acceptable-use policy |
Rule of thumb: the more autonomous the agent, the more it must be treated like a privileged identity - not just a piece of UX.
Approach
- Discover with DSPM for AI - Gain visibility into agent interactions and sensitive data accessed or generated by agents; action one-click protection recommendations. Verify: DSPM for AI dashboard shows agent activity volume and sensitive interaction counts.
- Control access (oversharing) - Because agents honour user permissions, remediate oversharing and apply sensitivity labels so agents can't surface content users shouldn't see. Verify: a test user via the agent cannot retrieve content they cannot reach directly.
- Apply DLP & labels - Extend DLP and sensitivity-label protection to agent grounding data and outputs where supported; configure DLP for Copilot to exclude top-tier labelled content. Verify: agent response excludes or warns on labelled-restricted content.
- Detect risky prompts - Use Communication Compliance / DLP for AI to detect sensitive data or prompt-injection patterns in agent interactions. Verify: a test prompt with PII generates a Communication Compliance alert.
- Audit & investigate - Use Purview Audit and Activity Explorer to log agent interactions for investigation and compliance; align with Insider Risk where relevant. Verify: agent interactions appear in audit search with user, agent, and prompt metadata.
- Govern identity - Treat agent identities as governed, least-privilege identities (Entra) - apply Conditional Access, periodic access reviews, and entitlement management. Verify: each non-user agent has an owner, a defined permission scope, and a renewal cadence.
- Lifecycle - Decommission unused agents on a cadence; orphaned agents are a permission sprawl risk. Verify: monthly agent inventory with owner re-attestation.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 91 lines · 110 tokens per session scan A 1b7f26fe5a5d
purview-agent-365-security is a skill published in the GitHub repository vinayaklatthe/microsoft-security-skills (173 stars, last pushed 2mo ago), licensed MIT. It adds 110 tokens to every session and 1,254 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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