A 12-Step Roadmap for Secure & Compliant LLMs

LLMs pose novel risks such as unpredictable outputs, AI hallucinations (false information), data leakage, prompt injections, and model theft. These risks can lead to reputational damage, compliance violations, and operational disruptions that traditional cybersecurity tools may not address.

Organizations should implement comprehensive AI governance frameworks including AI asset inventories, threat modeling, AI-specific security training, clear data access controls, and cross-functional governance bodies. Continuous monitoring, testing, and legal oversight are also essential to maintain accountability and compliance.

Key steps include assessing adversarial risks with frameworks like NIST AI RMF, conducting threat modeling, enforcing least-privilege data access for AI, deploying runtime AI controls (e.g., input validation and monitoring), and performing continuous testing and red teaming to identify and mitigate vulnerabilities proactively.

LLMs constantly evolve and can produce inconsistent or incorrect outputs. Continuous Test, Evaluate, Verify, and Validate (TEVV) processes ensure reliability, safety, and quality over time. Documenting AI models with model and risk cards improves transparency, helping stakeholders understand risks and mitigations.

Gencore AI provides a holistic platform for secure AI deployments with features like AI model discovery, risk assessment, data sanitization, real-time monitoring, access controls, and automated testing. It helps organizations confidently leverage proprietary data while ensuring compliance and minimizing AI-specific risks.