AI Security & Agent Governance
AI will act on everything it can reach. Decide what that is — before it does.
The problem
Copilot surfaces whatever a user's permissions allow — years of oversharing become instantly searchable. AI agents act autonomously with machine identities nobody governs. Employees adopt unsanctioned AI tools with corporate data. Low-code platforms let anyone wire AI into business processes. Boards are asking for AI adoption and AI safety simultaneously, and most security programs have controls for neither.
Our approach
We treat AI security as a governance problem with technical controls — not a new tool category. Before Copilot: data exposure assessment (oversharing discovery, permission remediation, label coverage) so rollout doesn't become disclosure. For agents and automation: an identity and permission model for non-human actors, lifecycle governance (who creates agents, what they may access, how they're retired), and monitoring. Around it all: AI usage policy, shadow AI discovery, DLP extended to AI channels, and compliance mapping for emerging AI regulation — grounded in the Microsoft stack you already run.
What we do
- Copilot security readiness assessment
- Oversharing discovery & permission remediation
- M365 Copilot governance (labels, restricted content, audit)
- AI data exposure assessment
- AI usage policy development
- Shadow AI discovery
- AI data loss prevention
- AI agent risk assessment
- Agent identity & permission architecture (non-human identity governance)
- Agent lifecycle governance
- Autonomous agent monitoring design
- Prompt security governance
- Power Platform & low-code governance
- Responsible AI governance frameworks
- AI compliance & audit readiness
What you receive
- Copilot readiness report with exposure findings & remediation plan
- AI governance framework (policy, roles, approval workflow)
- Agent identity & permission model
- Shadow AI inventory & risk ranking
- AI-aware DLP policy set
- Monitoring & audit design for AI activity
- Executive briefing on AI risk posture
What changes for the business
- Copilot deployed without a data disclosure incident
- AI agents with governed identity, scope, and lifecycle
- Visibility into actual AI usage across the organization
- An AI adoption position your risk committee and customers accept
- Readiness for AI-specific regulatory and audit demands
Who this is for
Organizations rolling out (or stuck on) M365 Copilot; regulated firms with AI governance mandates from boards or regulators; companies building agents on Copilot Studio, Azure AI Foundry, or Power Platform; any enterprise that suspects — correctly — it has shadow AI.
Common questions
We paused our Copilot rollout over data concerns. Is that typical?
Extremely — permission sprawl and oversharing are the #1 blocker. The readiness assessment exists precisely to unblock this safely.
What is "agent governance" concretely?
Registration and inventory of agents, identity and least-privilege access for each, an approval workflow for creation, monitoring of actions, and decommissioning discipline — the joiner-mover-leaver process, applied to software actors.
Can you find shadow AI usage?
Yes — via Defender for Cloud Apps discovery, network signals, and endpoint telemetry, producing an inventory with risk ranking and a sanctioning workflow.
Does this address the EU AI Act and emerging AI rules?
We map technical controls to regulatory expectations and prepare audit evidence. Regulatory interpretation belongs with your counsel; we provide the control implementation.
Is this only for Microsoft AI tools?
The governance framework covers all AI usage; deep technical controls focus on the Microsoft stack — Copilot, Purview, Entra, Power Platform — where we implement hands-on.
See where you stand first.
A fixed-scope assessment gives you findings, priorities, and a roadmap — with defined deliverables, so you know exactly what you're buying.