Elevate Your Copilot Experience: The Essential Role of AI DataFireWall™

Everyone wants Copilot’s speed. However, no one wants privileged paragraphs escaping in ways they can’t defend. The missing layer is a boundary “seatbelt” that pseudonymises before sending, enforces on-path, and rehydrates on return. This ensures that safety becomes the default.

Understanding the AI Leak Surface

The Risks of Tight Office Integration

Copilot feels native to Word, Outlook, and Teams. Yet, every useful interaction forms a prompt that an AI service interprets. This prompt often contains the exact details a law firm must protect: parties, matter IDs, dollar amounts, timelines, deposition fragments, and internal commentary. The risk lives during model interaction, not just in file storage or sharing:

• Privilege exposure when raw facts and names appear inside prompts.

• Cross-matter mixing as conversational context bleeds between unrelated work.

• Over-broad plugin/tool pulls that fetch more data than the immediate purpose requires.

• Prompt-injection and output exfil patterns that coax models to reveal or propagate unintended details.

  
  

Tight integration doesn’t erase these edges; it just makes them easy to cross.

Purview as the Baseline

What It Does Well

Microsoft Purview is a strong foundational layer for content governance inside the Microsoft estate. It brings sensitivity labels and classification, robust DLP policies across M365 apps and endpoints, and controls that influence what Copilot can access. It also offers comprehensive compliance and audit features. Keep it. Treat it as the control plane for content at rest and in motion inside your collaboration platform.

Where Purview Falls Short

AI-Boundary Gaps

Purview was not designed as a model-call firewall. In AI data-leak scenarios, these boundary-level gaps matter:

1. Pre-send transformation gap: Purview governs content and access; it does not inline-pseudonymise prompts before a model call. It doesn’t transform sensitive fields into safe, context-preserving surrogates right at inference time.

2. Automatic rehydration gap: There’s no matter-aware rehydration of model outputs to restore originals for authorised users while others see safe surrogates.

3. On-path, per-request enforcement gap: Purview isn’t built for sub-second allow/deny/detour decisions at the model call boundary, where latency budgets are tight and decisions must be made per prompt.

4. Plugin/tool minimisation gap: It does not act as a field-level minimisation broker for LLM tool/connector calls at prompt time, trimming exactly what flows into the interaction.

5. Prompt-injection/output filtering gap: It isn’t a model-mediating filter that strips adversarial instructions or blocks exfil patterns in generated outputs across mixed AI paths.

6. Matter-scoped compartmentalisation gap: Labels govern content, but they don’t provide consistent surrogate identities across a conversation to prevent cross-matter bleed while keeping reasoning quality intact.

7. Conversation-level, surrogate-aware audit gap: It lacks logs of pseudonymisation/rehydration decisions and surrogate mappings at the LLM conversation level for defensible partner/client assurance.

   
   

What AI DataFireWall (AIDF) Is

AIDF is a boundary proxy for AI requests and responses across Copilot, its plugins, and any other LLMs. It performs pseudonymise → policy-evaluate (allow/deny/detour) → rehydrate → log, with a private LLM sidecar for high-risk requests and conversation-level, SIEM-grade telemetry. Lawyers keep the same Copilot experience; the safety work happens inline, on the wire.

DLP Proxy Server Deployment

How AIDF Works in Practice

Three Anonymised Legal Workflows

1) Privileged Memo Drafting (Word + Copilot)

A lawyer drafts a memo that names parties, cites amounts, and references matter identifiers. Before any model sees the prompt, AIDF replaces sensitive elements with context-preserving surrogates. Copilot produces high-quality text because the semantics remain intact. When the response returns, authorised users automatically see the rehydrated version with real entities; others see the safe surrogate view. No extra steps. No manual redaction.

2) Diligence Summarisation (Teams/SharePoint + Assistant)

A team asks Copilot to summarise collaboration content. At the boundary, AIDF applies purpose binding and field minimisation, ensuring only the necessary elements reach the model. If the request is deemed high-risk, AIDF detours it to a private LLM sidecar in the firm’s environment. The result: fast summaries without over-shared data and zero unauthorised egress on sensitive threads.

3) Email Triage with Plugins (Outlook)

A partner uses Copilot and email plugins to extract tasks and categorise correspondence. AIDF enforces on-path policies—allow/deny/strip—so plugins can’t over-pull mailboxes or metadata. Output filters catch likely exfil patterns (for example, attempts to echo raw identifiers). Conversation-level logs show precisely what was transformed, routed, or blocked, creating a defensible trail for client and internal reviews.

Advantages That Matter

Key Benefits for Law Firms

• Protects privilege and confidentiality without manual redaction or workflow changes.

• No UX disruption: Lawyers keep Word/Outlook/Teams/Copilot; the boundary work is invisible.

• Matter-aware compartmentalisation: Consistent surrogates maintain compartments across conversations while preserving reasoning quality.

• On-path policy enforcement: Per-request allow/deny/detour decisions with tool/field minimisation tailored to the immediate purpose.

• Output safety checks: Filters for likely prompt-injection and exfil patterns before responses reach users.

• Conversation-level audit: A tamper-evident record of pseudonymisation choices, routing decisions, and rehydration events—credible for partner and client assurance.

• Shadow-AI risk reduction (without monitoring): By making safe ≈ seamless, AIDF removes the incentive to bypass approved tools.

  
  

Addressing Common Objections

“We already have Purview.” Keep it. AIDF layers at the AI boundary to add pre-send pseudonymisation, on-path detours, rehydration, and conversation-level audit that Purview doesn’t aim to provide.

“Will pseudonymisation hurt answer quality?” AIDF uses context-preserving surrogates so the model retains structure and meaning. Rehydration restores exact details for authorised users.

“Will this slow people down?” AIDF is designed for sub-second inline decisions with no UI changes. Lawyers shouldn’t notice the control, only the safety.

Measuring Success

Proof & Success Metrics

Measure what matters in a short, production-like trial:

• Sensitive-entity coverage: Target ≥95% correct pseudonymisation across names, organisations, IDs, amounts, and dates.

• Latency: Low-hundreds-ms median overhead per request.

• Policy correctness: Zero unauthorised egress on blocked matters and successful detours to the private sidecar when required.

• Audit completeness: 100% of decisions logged at the conversation level, with surrogate mappings and rehydration events.

• User acceptance: Positive lawyer feedback on unchanged workflows and drafting quality.

  
  

Call to Action: A Low-Risk, High-Signal PoV

Run a 2–4 week PoV on two live but controlled flows:

1. Word + Copilot drafting of privileged materials using AIDF’s boundary proxy and rehydration.

2. Collaboration content summarisation (Teams/SharePoint) with purpose binding, tool minimisation, and detours to the private sidecar for high-risk asks.

   
   

Evaluate sensitive-entity coverage, per-request enforcement accuracy, conversation-level audit quality, median latency, and end-user satisfaction. If the firm can prove that safe equals seamless, and that Copilot becomes defensible by default, then the case for adopting AIDF at scale writes itself.

Bottom line: Purview governs content effectively inside your collaboration platform. AIDF is the seatbelt layer at the AI boundary that turns Copilot from “useful” into useful and defensible, by default.