Quick Reference Guide
How to Safely Implement AI in Your Organization
A practical, step-by-step roadmap for deploying AI responsibly. Each phase maps to lifecycle domains and links to specific controls you can implement today.
Where to Start
You do not need to implement all six phases simultaneously. Start where your risk is highest.
- Write your AI Acceptable Use Policy
- Inventory all AI tools currently in use
- Assign a governance owner
- Implement code review for AI outputs
- Deploy prompt injection defenses
- Set agent permission boundaries
- Establish monitoring baselines
- Complete threat modeling for all AI systems
- Deploy readiness gates and canary deployments
- Launch bias monitoring and drift detection
- Conduct first red team exercise
- Track framework updates quarterly
- Run post-incident reviews
- Assess governance maturity annually
- Retrain governance cycles
Establish Governance
governBefore writing a single line of AI-assisted code, establish the policies, roles, and risk boundaries that will guide every decision that follows.
Define an AI Acceptable Use Policy
Specify what AI tools are approved, what data they can access, and what outputs require human review. This is your organization's single source of truth.
Set your AI Risk Appetite
Determine how much AI-related risk your organization will accept. Define thresholds for autonomy, data sensitivity, and failure impact that trigger escalation.
Assign Roles and Responsibilities
Designate an AI governance owner, define who approves model deployments, and clarify accountability for AI-generated outputs at every level.
Inventory All AI Assets
Catalog every AI model, API, agent, and tool in use — including shadow AI. You cannot govern what you do not know exists.
Evaluate and Approve Vendors
Assess each AI vendor and model against security, privacy, and reliability criteria before adoption. No exceptions.
Build Responsibly
buildAI-generated code is not inherently trustworthy. Every line needs the same rigor as human-written code — and often more, because AI makes confident-looking mistakes.
Mandate Code Review for AI Outputs
Every AI-generated code artifact requires human review before merge. Establish diff-level review standards that catch hallucinated APIs, insecure patterns, and logic errors.
Assess Vibe Coding Risks
If developers use conversational AI coding ("vibe coding"), quantify the risks: reduced code understanding, dependency on AI context, and testing gaps.
Enforce Human Review Gates
Insert mandatory human checkpoints at design, pre-merge, and pre-deploy stages. No AI-generated change ships without explicit human approval.
Secure Your Prompts
Treat system prompts as security-critical configuration. Store them in version control, review changes, and test for injection resistance.
Require Tests for AI Code
AI-generated code must meet the same test coverage requirements as human code. Add specific tests for edge cases AI tends to miss.
Secure Your AI
secureAI systems introduce attack surfaces that traditional security controls do not cover. Prompt injection, data exfiltration through model outputs, and excessive agent autonomy require purpose-built defenses.
Threat Model Your AI Systems
Map every AI component's attack surface: input channels, data flows, output destinations, and privilege levels. Use STRIDE or OWASP frameworks as your lens.
Defend Against Prompt Injection
Implement input validation, output filtering, and privilege separation. Test with adversarial prompts. This is the #1 LLM vulnerability.
Prevent Data Exfiltration
Block AI models from leaking sensitive data through outputs, logs, or side channels. Classify data before it enters any AI pipeline.
Set Agent Permission Boundaries
Every AI agent must operate with least-privilege access. Define what each agent can read, write, execute, and communicate — then enforce it technically.
Red Team Your AI
Conduct adversarial testing against your AI systems. Attempt prompt injection, jailbreaks, data extraction, and privilege escalation before attackers do.
Deploy Safely
deployAI deployments require deployment gates, rollback capabilities, and environment isolation that go beyond traditional CI/CD. A bad model deployment can silently degrade quality across your entire product.
Implement Deployment Readiness Gates
No AI component deploys without passing security review, performance benchmarks, bias checks, and stakeholder sign-off. Automate what you can, require humans for the rest.
Harden Your AI Infrastructure
Isolate AI workloads, encrypt data in transit and at rest, restrict network access, and apply CIS benchmarks to all hosting infrastructure.
Version Models and Enable Rollback
Track every model version with metadata. Maintain the ability to roll back to any previous version within minutes, not hours.
Use Canary Deployments
Route a small percentage of traffic to new model versions first. Monitor for regressions before full rollout. Automate rollback on threshold breach.
Define SLAs and Baselines
Establish measurable performance baselines for latency, accuracy, throughput, and error rates. SLAs create accountability and trigger alerts when breached.
Monitor Continuously
monitorAI systems degrade silently. Models drift, biases amplify, and adversarial inputs evolve. Without active monitoring, you will not know something is wrong until a customer, regulator, or attacker tells you.
Build an AI Monitoring Dashboard
Centralize visibility into model performance, usage patterns, error rates, and security events. If it is not on the dashboard, it is not being monitored.
Detect Model Drift
Monitor input distributions and output quality over time. Detect when a model's real-world performance diverges from its training or validation benchmarks.
Monitor for Bias and Fairness
Track model outputs across demographic groups, use cases, and edge cases. Bias that was acceptable at launch may become unacceptable as usage patterns shift.
Prepare AI Incident Response
Define what constitutes an AI incident, who responds, and how. Include model rollback, data breach notification, and stakeholder communication in your playbook.
Maintain Audit Logs
Log all AI decisions, inputs, outputs, and configuration changes. These logs are your evidence trail for compliance, forensics, and continuous improvement.
Improve Iteratively
improveAI governance is not a one-time project. Frameworks evolve, threats change, and your organization's AI maturity grows. Build improvement into the process from day one.
Assess Your Maturity Level
Use a maturity model to understand where you are today and what capabilities to build next. Do not try to implement everything at once — prioritize by risk.
Conduct Post-Incident Reviews
After every AI incident or near-miss, run a blameless retrospective. Document root causes, update controls, and share lessons across teams.
Track Framework Updates
ISO 42001, NIST AI RMF, and OWASP all evolve. Assign someone to monitor updates and assess impact on your controls. Falling behind creates compliance gaps.
Govern Retraining Cycles
Model retraining introduces risk. Apply the same governance to retraining that you apply to initial deployment: review, test, approve, monitor.
Schedule Annual Reviews
At minimum, review your entire AI governance program annually. Assess control effectiveness, update risk assessments, and recalibrate priorities.
Dive Deeper
This guide provides the starting path. Each linked control contains full implementation guidance, code examples, evidence requirements, and audit checklists.