Every executive I talk to asks the same question: “Can we use AI without breaking compliance—or the business?” The honest answer is yes, but only if you treat AI like any other high-impact system: give it guardrails, measure it, and make someone accountable.

In this article, I’ll show you a practical approach to governing AI inside Microsoft 365 that aligns with SOC 2, HIPAA, and ISO 27001. No theater, just the controls, and the flow.

What Changed—and Why It Matters

AI didn’t invent new risk categories. It concentrated old ones: data leakage, identity abuse, shadow IT, and third-party exposure. The difference now is speed and scale. One poorly configured connector can exfiltrate a year’s worth of customer data in a single prompt.

• Data gravity: Foundation models and assistants pull sensitive data toward them. You need classification and least privilege at the edge.
• Opaque behavior: Models can infer more than you intended. Logging and human review matter.
• Vendor chain: “One AI tool” is usually five sub-processors. Your due diligence must go deeper than marketing pages.

Map AI Governance to Familiar Frameworks

Executives don’t need a brand-new compliance program. You need to extend what you already do:

• SOC 2: Expand CC6.x (access controls), CC7.x (change/operations), and A1.x (availability) to cover AI services, prompts as input data, and generated content as system output.
• HIPAA: Treat prompts and outputs containing PHI like any ePHI system: risk analysis, access controls, audit, transmission security, and BAAs with AI vendors.
• ISO 27001: Extend Annex A controls to AI data flows (A.5 policies, A.8 asset management, A.9 access control, A.12 operations security, A.15 supplier relationships, A.18 compliance). Use a data flow diagram to anchor the Statement of Applicability.
• NIST AI RMF (high level): Use the Map → Measure → Manage loop to identify use cases, define harm scenarios, and set guardrails. Keep it lightweight for SMBs.

The Minimum Viable AI Policy (MVAP)

A concise policy beats a 20-page PDF nobody reads. Your MVAP should answer:

1. Approved Uses: What business processes can leverage AI? (e.g., drafting emails, summarizing internal docs, code assistance—with restrictions.)
2. Prohibited Content: No export of customer secrets, regulated data, credentials, or unreleased financials into public or unvetted AI systems.
3. Data Handling: Sensitivity labels, DLP coverage, and retention rules apply to prompts and outputs.
4. Human in the Loop: Who reviews AI-generated content for external use, and when?
5. Vendor Requirements: Security attestations (SOC 2/ISO 27001), data residency, encryption, sub-processor transparency, incident SLAs, and BAA (if PHI).
6. Accountability: Product owner per use case; quarterly review of metrics and risks.

Microsoft 365 Controls That Do the Heavy Lifting

Governance works when the policy is enforced by defaults, not reminders. Here’s how to wire it up:

• Identity (Entra ID): Conditional Access baselines (MFA, device compliance), step-up for admin and high-risk sessions, Privileged Identity Management (PIM) for just-in-time elevations, and app consent policies to block unverified AI connectors.
• Data (Purview): Unified sensitivity labels with mandatory labeling in Office apps; DLP policies that monitor prompts and AI-generated files in SharePoint, OneDrive, and Teams; trainable classifiers for customer data and PHI.
• Apps (Defender for Cloud Apps): Shadow AI discovery, sanctioned/unsanctioned tagging, session controls (monitor, block, redact) for risky web AI tools.
• Devices (Intune): App protection policies to keep corporate data in managed contexts; restrict copy/paste and save-as from AI web apps on mobile; baseline hardening for Windows/Mac.
• Threat (Defender): Alerting on anomalous data exfiltration and OAuth consent grants; investigate risky sign-ins tied to AI connectors.
• Audit (M365 Audit/Log Analytics): Centralize prompt and output file activity. Retain logs per your regulatory obligations.

Common Pitfalls (and How to Avoid Them)

• Policy without plumbing: If DLP and labeling aren’t enforced, your policy is a suggestion. Bake controls into defaults.
• Over-blocking: If you block everything, users will route around you. Provide a sanctioned path with clear guardrails.
• Ignoring outputs: AI-generated files are still sensitive. Label on creation and apply retention.
• Vendor blind spots: Ask about training data use, retention, sub-processors, private tenancy options, and incident support SLAs.
• No owner: Every AI use case needs a business owner who signs off on risk and value.

AI can be safe, compliant, and fast—if you wire identity, data, and app controls into how people work. Start small, automate the guardrails, and measure what matters. That’s how you turn AI governance from a blocker into a competitive advantage.