AI DLP Consulting
Traditional DLP catches PANs and Aadhaar numbers via regex. AI DLP catches what regex can't — paraphrased customer data, strategic documents pasted as 'summarise this', code-base lineage in a prompt. Digital Defense designs and deploys AI DLP across Cyberhaven, Microsoft Purview AI labels, Netskope AI Control and Zscaler AI Security.
Who needs this
Customers who've discovered employees pasting PII / source / strategy into ChatGPT or Claude
BFSI / regulated firms with strict data-residency rules
Engineering-heavy teams adopting Copilot, Claude Code, Cursor
Customers with regex DLP that's missing AI leakage paths
Healthcare / pharma protecting PHI / IP under GenAI workflows
Problems we solve
- 01
Regex DLP missing paraphrased / contextual data leakage
- 02
Browser-based GenAI usage bypassing endpoint DLP
- 03
Code-base leakage via copilots without code DLP controls
- 04
No labelling strategy — controls fire on everything or nothing
- 05
Audit-evidence gap for AI DLP events
Our methodology
- 1
Data classification + labelling
Sensitivity labels (Confidential, Restricted, Public) mapped to controls; AI-specific label policies.
- 2
Tool selection
Cyberhaven vs Purview AI labels vs Netskope vs Zscaler — fit-for-stack scoring.
- 3
Deployment
Endpoint agent / browser extension / SSE roll-out; tenant onboarding; SSO/SCIM.
- 4
Policy tuning
Detection policies per label, action per severity (warn / block / quarantine), user-coaching mode for the first 2-4 weeks.
- 5
SOC integration
AI DLP events → SIEM; investigation playbooks; user-coaching workflow for the first 90 days.
What you receive
Data classification + labelling strategy
AI DLP vendor selection report
AI DLP deployment runbook
Tuned policy set + user-coaching playbook
SIEM integration + investigation playbooks
Frequently asked questions
Cyberhaven vs Microsoft Purview AI labels — which one?
Microsoft-heavy shops with M365 + Purview should start with AI labels. Engineering-heavy or multi-cloud shops typically get more value from Cyberhaven's lineage-based approach. We help you pick after a short POC.
Where does Netskope / Zscaler fit?
CASB/SSE controls give you access control + telemetry across all GenAI apps (sanctioned + shadow). Pair with endpoint/browser AI DLP for full coverage.
How long to deploy AI DLP?
Cyberhaven / Purview AI labels: 6-10 weeks (deployment + labelling + tuning). Netskope / Zscaler AI controls: 4-6 weeks.
Do we need a user-coaching phase?
Yes — we recommend 2-4 weeks of warn-only mode with coaching messages so employees understand what's restricted before we move to block. Reduces friction and false-positives.
Will AI DLP block legitimate AI usage?
Tuned correctly, no. We tune policies to the label + context (e.g., block 'paste source code to consumer ChatGPT' but allow 'summarise meeting notes in Copilot'). The labelling + policy work is the lift.
Related services
Secure Enterprise Usage of Claude, ChatGPT, Copilot & Gemini
/services/ai-security-governance/secure-genai-usage
cyberhaven deployment
/services/ai-security-governance/cyberhaven-deployment
microsoft purview integration
/services/ai-security-governance/microsoft-purview-integration
netskope ai control
/services/ai-security-governance/netskope-ai-control
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