Copilot Readiness & AI Security
Prepare your data and permissions before AI can reach them.
Copilot and AI agents can surface anything the user or agent already has access to. We find the oversharing, permission gaps, and injection risks first, then help you close them in a sensible order.
AI makes existing access visible
A SharePoint site shared with everyone years ago, an unlabeled salary spreadsheet, an anyone-with-the-link file: once Copilot is turned on, each is one question away from the wrong person.
Custom AI agents add their own risks. They hold credentials, call tools, and read untrusted input, so a single prompt-injection can push them into actions nobody approved.
Who this is for
- Organizations on Microsoft 365 evaluating or starting a Copilot rollout
- IT and security teams asked to sign off on AI adoption
- Teams building AI agents, chat assistants, or LLM-powered workflows
- Businesses that want a clear-eyed view of AI risk before scaling usage
What the engagement includes
Scoped to what you need. These are the building blocks.
Microsoft 365 Copilot readiness assessment
Licensing, tenant settings, and data exposure reviewed against what Copilot would be able to retrieve for each user.
SharePoint and OneDrive oversharing review
Broadly shared sites, organization-wide and anonymous links, and inherited permissions that no longer make sense.
Purview sensitivity label and DLP readiness
Label taxonomy, labeling coverage, and DLP policies, plus the gaps that matter most before AI usage grows.
AI agent identity and permission review
What each agent's identity can reach, which tools it can call, and whether that matches the job it was built for.
Agentic workflow threat modeling
Data flows, trust boundaries, and abuse cases for the AI workflows in scope, mapped to OWASP's Top 10 for LLM applications.
Prompt-injection and data-exposure testing
Hands-on testing of direct and indirect injection, data leakage, and unauthorized tool use against agreed targets.
Deliverables
What you walk away with
- A readiness report highlighting oversharing hotspots and permission gaps
- A label and DLP gap analysis with recommended policy changes
- A threat model for each AI workflow in scope
- Test results with reproduction steps, mapped to the OWASP LLM Top 10
- A phased remediation roadmap for safer AI adoption
Process
How it runs
- 01
Discover
Confirm licensing, tenant configuration, the AI tools in use or planned, and the data that matters most.
- 02
Assess
Review permissions, sharing, labels, and policies. Inventory agents, their identities, and their tools.
- 03
Test
Threat-model the workflows in scope and run agreed injection and exposure tests in a controlled way.
- 04
Prioritize
Rank fixes by exposure and effort. Some are a setting change, others need a rollout plan.
- 05
Roadmap
Deliver a phased plan so AI adoption can move forward while the remaining gaps get closed.
Technologies
- Microsoft Purview
- SharePoint & OneDrive
- Microsoft Entra ID
- Defender for Cloud AI threat protection
- Azure AI Content Safety Prompt Shields
- Microsoft Sentinel
- OWASP LLM Top 10
- NIST AI RMF
Many Copilot and Purview capabilities depend on your Microsoft 365 licenses and tenant configuration. We only recommend what your environment can actually use.
Testing only happens against systems you own or are authorized to test, under written scope. Readiness work prepares your environment. It doesn't license or deploy Copilot for you.
Applied to a production AI system we run
- Caller input to the ClarityOS voice receptionist is screened by Prompt Shields, and detections raise Sentinel incidents
- Agent-to-agent calls are authenticated, and each agent identity is scoped to the secrets its job needs
- Findings are tracked against the OWASP LLM Top 10 and NIST AI RMF
This is work on ClarityOS, our own product, not a client engagement. About ClarityOS
Start with a conversation about your environment.
A 30-minute call to understand the problem and decide whether this is the right fit. No obligation.
Discuss AI Readiness