What it is
AI systems introduce new attack surfaces and new kinds of failure. Prompt injection can turn a helpful assistant against its user; over-permissioned agents can take harmful actions; sensitive data can leak through prompts, logs or outputs; and biased or unexplainable decisions create legal and reputational risk.
AI governance is the organizational side: an inventory of AI use, risk assessments, clear accountability, documentation, human oversight and monitoring. Regulations and standards increasingly expect it, and customers and boards ask for it.
How it works
Inventory
Catalog every AI system, model, data source and vendor in use.
Assess risk
Classify use cases by impact and regulatory exposure; threat-model each system.
Control
Apply least privilege, input and output guardrails, PII protection and human approval.
Test
Red-team for prompt injection, jailbreaks, data leakage and harmful output.
Document
Maintain model and system cards, data lineage and decision records.
Monitor
Log, audit and review incidents; reassess when models or uses change.
Key concepts
- Prompt injection
- Malicious instructions hidden in user input, documents, web pages or tool results.
- Excessive agency
- Giving a model more tools or permissions than the task needs.
- Sensitive data exposure
- PII, secrets or confidential data leaking via prompts, training data, logs or outputs.
- Guardrails
- Input and output checks for policy, safety, PII and topic boundaries.
- Red teaming
- Structured adversarial testing to find failures before attackers or users do.
- Risk frameworks
- NIST AI RMF and its Generative AI Profile, and ISO/IEC 42001 for AI management systems.
- Regulation
- The EU AI Act's risk-based obligations, sector rules and emerging state and national laws.
Tools & platforms
Security references
Guardrails and filtering
Testing
Skills employers look for
- Threat-modeling LLM applications and agents against the OWASP LLM Top 10.
- Designing defenses against prompt injection, including isolation of untrusted content.
- Implementing PII detection and redaction, access control and audit logging.
- Running red-team exercises and turning findings into regression tests.
- Mapping AI use cases to NIST AI RMF, ISO/IEC 42001 and EU AI Act obligations.
- Writing AI usage policies and review processes the business will follow.
Enterprise use cases
AI acceptable-use policy
Clear rules for which tools staff may use, with which data.
Secure internal assistants
Assistants that respect permissions and never expose restricted data.
Vendor due diligence
Assessing AI vendors' data handling, training use and security controls.
Regulatory readiness
Inventory, risk classification and documentation ahead of audits.
Common pitfalls
- Relying on the system prompt for security — instructions are not access control.
- Treating guardrails as complete — filters reduce risk; architecture and permissions contain it.
- Shadow AI — unapproved tools with company data are a bigger risk than approved ones.
- Governance as paperwork — controls must be built into delivery, not written after it.
How MrAI.dev helps
We run AI risk assessments and red-team exercises, design guardrails and permission models for your AI systems, and set up a governance process that satisfies auditors without slowing delivery to a crawl.
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