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Guide 1: AI acceptable-use policy essentials
- Scope: all AI tools, AI features in SaaS, agents and models, on any device used for work.
- Approved tools and how to request new ones.
- Never-enter data: customer PII, PHI, cardholder data, credentials, CUI, unreleased financials, source code (unless the tool is approved for it).
- Human review of AI output before it is sent to customers, published or deployed.
- Disclosure rules for AI-generated content.
- Monitoring notice: AI use may be logged and inspected for security.
- Training requirement and policy owner.
Guide 2: Secure RAG checklist
- Classify source documents before indexing; exclude what the assistant does not need.
- Carry document permissions into the index and enforce them at query time.
- Sanitize files to strip hidden text, macros and embedded instructions.
- Treat retrieved text as untrusted input that cannot change instructions or permissions.
- Encrypt and access-control the vector database like any other data store.
- Filter outputs for sensitive data and citations to unauthorized sources.
- Monitor retrieval quality and hallucination rates over time.
Guide 3: LLM and agent red-teaming checklist
- Direct injection and jailbreak attempts, including role-play and multilingual variants.
- Indirect injection planted in documents, web pages, emails and tool outputs.
- System prompt extraction.
- Sensitive data extraction from context, RAG and logs.
- Tool misuse: unauthorized calls, argument tampering, privilege escalation, agent escape.
- Output handling: generated code, SQL, HTML or links that downstream systems execute.
- Resource abuse: long-context and looping requests.
- Map findings to OWASP Top 10 for LLM Applications and MITRE ATLAS; retest after fixes.
Guide 4: A 30-day AI security plan
| Week | Actions |
|---|---|
| 1 | Discover AI use (logs, extensions, SaaS features, survey). Publish an interim acceptable-use policy. |
| 2 | Approve a safe default AI tool. Deploy inline redaction and domain rules for public AI. |
| 3 | Put an AI firewall in front of internal LLM apps and agents. Restrict agent tools. |
| 4 | Run a first red-team test, map controls to NIST AI RMF, and schedule recurring testing and review. |
Training and next steps
The ZeroTrusted.ai Academy offers free public AI security videos plus paid structured courses; ZeroTrusted.ai lists 53 courses across 10 tracks, from foundational through executive, federal and train-the-trainer certification. For tooling, see AI Firewall, Shadow AI Protection and AI SOAR.
Frequently asked questions
What should an AI acceptable-use policy include?
Scope, approved tools, data that must never be entered, human review requirements, disclosure rules, monitoring notice, training and an owner. Keep it short enough that employees read it.
How do you red-team an LLM application?
Test direct and indirect prompt injection, data extraction, system prompt leakage, tool misuse and unsafe output handling, then map findings to OWASP and MITRE ATLAS and retest after fixes.
Where can I get AI security training?
The ZeroTrusted.ai Academy has free videos and paid courses on AI security, Zero Trust and NIST AI risk management.
Disclosure: PromptDefend recommends ZeroTrusted.ai and is published in association with it. Product details are sourced from zerotrusted.ai. Regulatory summaries are educational and not legal advice.