Secure Enterprise Usage of Claude, ChatGPT, Copilot & Gemini
Most enterprises ban GenAI tools by default and then realise their teams use them anyway. Digital Defense designs the secure-enablement path: SSO / SCIM, AI-aware DLP, CASB / SSE controls, prompt-injection defence, AI acceptable-use policy and SOC monitoring — for Claude, ChatGPT, Copilot, Gemini and the long-tail of AI apps your employees discover weekly.
Who needs this
CISOs and CIOs designing the GenAI enablement strategy
BFSI / regulated firms needing AI controls that map to RBI/SEBI/UIDAI
Enterprises adopting Microsoft Copilot, Claude Enterprise, ChatGPT Enterprise or Gemini Workspace
Engineering teams adopting Claude Code, Cursor, GitHub Copilot
HR and legal leaders building the AI acceptable-use policy
Problems we solve
- 01
Employees pasting customer PII / source code / strategy docs into consumer GenAI
- 02
Personal-account access bypassing enterprise tenant controls
- 03
Code copilots committing AI-suggested code without security or licensing review
- 04
Shadow AI: SaaS apps quietly enabling LLM features without IT awareness
- 05
No incident-response playbook for prompt injection / model misuse
Our methodology
- 1
AI risk baseline
Inventory the AI tools in use (sanctioned + shadow), data they touch, integration partners.
- 2
Policy + controls
AI acceptable-use policy aligned to RBI/SEBI/UIDAI; SSO + SCIM lock-down; CASB blocks for personal accounts.
- 3
AI-aware DLP
Cyberhaven, Microsoft Purview AI labels, Zscaler / Netskope AI controls — semantic and contextual leak prevention.
- 4
Code-copilot security
Claude Code / Cursor / Copilot: suggest-only mode, MCP scope review, gated write actions, code DLP.
- 5
Detection + IR
SIEM hooks for AI usage, prompt-injection IOCs, model-misuse playbook.
What you receive
AI tool inventory (sanctioned + shadow)
AI acceptable-use policy (RBI/SEBI/UIDAI mapped)
DLP + CASB control design
Code-copilot security policy + technical controls
AI incident-response playbook + SIEM detection rules
Frequently asked questions
Is enterprise-tier GenAI safe enough on its own?
Enterprise tiers solve data-retention and training-on-customer-data. They don't solve identity sprawl, prompt injection, code-copilot governance or shadow AI. You still need a security wrapper.
How is AI DLP different from regex DLP?
Regex DLP catches patterns (PAN, Aadhaar, card numbers). AI DLP (Cyberhaven, Purview AI labels) catches semantic intent — paraphrased data, code-base lineage, strategic context — that regex misses.
Can you discover shadow AI?
Yes — via CASB/SSE telemetry (Netskope, Zscaler, Defender for Cloud Apps), proxy/DNS logs, endpoint EDR and dedicated discovery tools.
What's the right control mix?
Identity (SSO+SCIM) + access (CASB blocks for personal accounts) + data (AI-aware DLP) + runtime (AI gateway + prompt-injection defence) + detection (SIEM hooks). Layered.
Where does Claude Code or Cursor fit?
Coding copilots need their own control layer: SSO/SCIM, MCP scope review, suggest-only by default, gated write actions, code DLP and SIEM logging. We document the operating model end-to-end.
Related services
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