Every enterprise is deploying AI. Few are securing it properly. Traditional security controls were not designed for systems that learn, generate content, and make autonomous decisions. If you are rolling out AI in a production environment, you need a security framework built for the unique risks these systems introduce.
This checklist covers the ten controls that matter most. Each one addresses a specific attack surface or compliance requirement that conventional security programs miss.
Why AI Security Is Different
Traditional applications follow deterministic logic. Given the same input, they produce the same output. AI systems are fundamentally different. They are probabilistic, context-dependent, and often opaque in their reasoning. This creates security challenges that do not map neatly to existing frameworks.
AI models can be manipulated through their inputs. They can leak training data through their outputs. They can behave differently in production than they did in testing. And because they often process natural language, the boundary between "data" and "instructions" becomes dangerously blurred. Securing AI requires controls that account for these properties.
The 10 Controls
1. Data Classification
Before any data enters an AI pipeline, classify it. Know what is public, internal, confidential, and restricted. Apply labels consistently and enforce policies that prevent sensitive data from being used in training or inference without explicit approval. If you cannot classify the data, you cannot control where it goes once a model has ingested it.
2. Access Controls
Apply the principle of least privilege to every component of your AI stack. This means model endpoints, training data repositories, configuration files, and monitoring dashboards. Use role-based access control (RBAC) and ensure that service accounts have only the permissions they need. Pay special attention to who can modify model weights, update prompts, or change inference parameters.
3. Prompt Injection Defenses
Prompt injection is the SQL injection of the AI era. Attackers craft inputs designed to override system instructions, extract sensitive information, or cause the model to behave in unintended ways. Implement input validation, use system-level prompt boundaries that are difficult to override, and test your applications against known injection techniques regularly. No single defense is sufficient, so layer multiple mitigations.
4. Output Filtering
Do not trust model outputs blindly. Implement filters that check generated content for sensitive data leakage, harmful content, and hallucinated information before it reaches end users. This is especially critical for customer-facing applications where model outputs become part of your brand's communication. Define clear policies for what the model should and should not produce, then enforce them programmatically.
5. Audit Logging
Log every interaction with your AI systems. This includes prompts, responses, model versions, user identities, timestamps, and any tool calls or function invocations. Comprehensive audit logs are essential for incident investigation, compliance reporting, and understanding model behavior over time. Store logs securely, protect them from tampering, and establish retention policies that meet your regulatory requirements.
6. Model Supply Chain Security
If you are using open-source models, fine-tuned models, or models from third-party providers, you have a supply chain to manage. Verify model provenance. Scan model files for embedded malware. Pin specific model versions in production and test updates in isolated environments before deployment. Treat model artifacts with the same rigor you apply to software dependencies, because a compromised model can be just as dangerous as a compromised library.
7. API Rate Limiting
AI model inference is computationally expensive, making API endpoints attractive targets for denial-of-service attacks and resource exhaustion. Implement rate limiting per user, per API key, and per endpoint. Set token-level limits for large language models. Monitor for unusual usage patterns that could indicate automated extraction attempts, where an attacker systematically queries the model to reconstruct training data or map its behavior.
8. Privacy Compliance (GDPR/CCPA)
AI systems that process personal data must comply with applicable privacy regulations. Under GDPR and CCPA, individuals have the right to know how their data is used, to request deletion, and to opt out of certain processing activities. Ensure your AI pipelines can honor these rights, even when personal data has been incorporated into model training. Document your lawful basis for processing, conduct data protection impact assessments, and maintain records of processing activities.
9. Incident Response for AI
Your incident response plan needs AI-specific playbooks. What happens if your model starts generating harmful content? What if you discover training data contamination? What if an attacker successfully performs prompt injection at scale? Define escalation procedures, containment strategies (including the ability to quickly roll back or disable a model), and communication templates. Practice these scenarios before they happen.
10. Continuous Monitoring
AI systems degrade and drift over time. Model performance changes as the world changes. Adversaries adapt their techniques. Continuous monitoring should cover model accuracy, output quality, latency, error rates, and security-relevant anomalies. Set up alerts for distribution shifts in inputs and outputs, unexpected token usage patterns, and spikes in error rates. Treat monitoring as a first-class security control, not an operational afterthought.
The Role of Trust Infrastructure
Implementing these ten controls is essential, but they work best when built on a foundation of verifiable trust. As AI agents increasingly interact with each other and with external services, establishing identity, intent, and authorization becomes a prerequisite for security.
This is the problem that trust infrastructure solves. CraftedTrust provides the identity and trust layer for AI agent ecosystems, enabling organizations to verify who is making requests, what permissions they have, and whether their behavior aligns with stated intent. When your AI controls can reference a trust framework rather than relying solely on perimeter defenses, you gain the ability to make nuanced, context-aware security decisions at every point in the AI lifecycle.
"Securing AI is not about adding one more tool to the stack. It is about rethinking your security model for systems that learn, adapt, and act autonomously."
This checklist is a starting point, not a finish line. AI security is an evolving discipline, and the specific implementations will vary based on your use cases, risk tolerance, and regulatory environment. The organizations that get this right will be the ones that treat AI security as a continuous practice, not a one-time project.