The new privacy perimeter (why this matters now)

Generative AI tools sit between your people and your data. Every prompt may contain confidential information — pricing, source code, contracts, SIEM/EDR logs, incident notes. Without guardrails, that information can persist in vendor logs, be used for model training, be retrieved via prompt-injection, or leak through extensions and third-party plugins.

Business impact: loss of trade secrets, NDAs/DPAs violations, regulatory exposure (HIPAA/PCI/GLBA), reputational damage, and contract risk when customers audit your AI posture.

How AI data leaks actually happen (7 scenarios)

Governance stack: policy without the slowdown

Acceptable Use (template)

• Allowed: public data; anonymized drafts; synthetic test data.
• Restricted (gateway only): customer data, SIEM/EDR logs, keys/secrets, financials, HR/PII, legal docs.
• Prohibited: regulated data to non-compliant tools; training opt-in; unknown extensions.

Vendor diligence

Lock down residency, retention, log access, training opt-out, sub-processors, DPA/SCC/DPF status, audit logs, and SSO/SCIM.

Technical controls that work

Gateway enforcement

• Prompt redaction: mask emails, names, ticket IDs, IPs.
• Secrets scanning: auto-block keys/tokens (AWS/Azure/GCP, Git, OAuth, JWT).
• Policy routing: restricted data → private model; block everything else.
• Allowlist: only approved vendors/models; rate/token limits.
• Audit log: prompts/responses (redacted) with user IDs.

DLP regex starters

# Email (conservative)
\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b

# IPv4
\b(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.(?:25[0-4]\d|[01]?\d\d?)\.(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\b

# AWS Access Key ID
AKIA[0-9A-Z]{16}

# JWT (broad)
\b(eyJ[a-zA-Z0-9_-]+\.[a-zA-Z0-9_-]+\.[a-zA-Z0-9_-]+)\b

# Internal hostnames
\b([a-z0-9-]+\.)+(corp|internal|local)\b

# Ticket IDs
\b(?:INC|PRB)\d{7}\b

Logging & monitoring: protect business secrets and SIEM data

Log to SIEM: user ID, timestamp, app/tool, model/vendor, prompt hash + redacted prompt, response hash + redacted response, decision (allow/block/mask), DLP rule hits, region, cost, latency.

Microsoft Sentinel (KQL)

// AIGateway table example
AIGateway
| where DlpHitCount > 0 or Action in ("block","mask")
| summarize hits = sum(DlpHitCount), blocks = countif(Action == "block") by User, bin(TimeGenerated, 1h)
| order by TimeGenerated desc

// Secrets in prompts
AIGateway
| where PromptRedacted == false and Prompt matches regex @"AKIA[0-9A-Z]{16}"
| project TimeGenerated, User, Vendor, Endpoint, PromptHash

Graylog (search & pipeline)

// Search (Lucene-like)
index:aigateway (dlp_hits:>0 OR action:block)

// Fields to display
| table timestamp, user, action, dlp_hits, vendor, endpoint

rule "Detect AWS Key in Prompt"
when
  has_field("prompt") && regex("AKIA[0-9A-Z]{16}", to_string($message.prompt)).matches == true
then
  set_field("dlp_hit", "aws_access_key");
  set_field("action", "block");
  route_to_stream("ai-gateway-incidents");
end

30-day rollout plan

Week 1 — Discover & decide

• Inventory shadow AI via proxy/DNS + survey; define Restricted classes.
• Select gateway/DLP; draft acceptable-use policy.

Week 2 — Configure & contract

• Add redaction, secrets scanning, vendor allowlist, routing.
• DPAs signed; retention minimums; training opt-out; region pinning.
• Enable SIEM logging; test KQL/Graylog; set exception workflow.

Week 3 — Pilot

• Two teams (Support + Sales); coach “safe prompts;” tune false positives.

Week 4 — Scale

• Org-wide rollout; dashboards for execs (usage, blocks, cost, latency).
• Quarterly prompt-injection red team.

Incident playbook (when leakage happens)

1. Contain: disable user/token/extension; snapshot logs.
2. Scope: what data (business secrets, SIEM logs, PII), who, vendor/region, duration.
3. Legal/contract: DPA/NDA; breach vs. incident; customer comms as needed.
4. Eradicate: rotate keys; revoke tokens; purge vendor logs per contract.
5. Improve: update policy; tighten DLP; retrain; run red-team tests.

Implementation appendix

Policy snippet (acceptable use)

Restricted data (customer data, secrets/keys, SIEM/EDR logs, financials, HR/PII)
must only be used via the approved AI gateway with DLP and logging enabled. Do not
paste Restricted data into unapproved AI tools. Outputs with sensitive content must
undergo human review before external sharing.

Vendor questionnaire (top 10)

• Training (off by default?), retention minimums, region pinning.
• Log access, sub-processors, SOC2/ISO 27001, SSO/SCIM, audit logs.

FAQ

Can we just block AI tools?
You’ll drive shadow AI. Provide a safe, approved path instead.

Is a private LLM enough?
It improves isolation, but you still need DLP, logging, vendor controls.

What about code copilots?
Treat them as AI vendors: scope repos, disable telemetry, add secrets scanning, log usage.

How do we handle SIEM logs?
Classify as Restricted. Route via the gateway with masking; never paste raw logs in public tools.