AI Security Framework

Responsible AI adoption begins with a strong AI Security Framework. Creating a strong AI foundation will provide building blocks that will adapt to future requirements, particularly for those companies in regulated environments.  This four-step framework gives Chief Information Security Officers, Chief Information Officers, and AI Innovation Officers a blueprint to align AI adoption with enterprise security, modeled on best practices. These steps allow you to move forward with a much greater level of confidence and without fear of exposure or failure.

STEP 1: DISCOVERY & CLASSIFICATION

You cannot control what you cannot see.

  • Inventory all AI tools — both sanctioned and unsanctioned — using CASB and visibility tools to uncover Shadow AI

  • Gain visibility into AI usage patterns with insights that measure employee adoption, ROI, and business value

  • Identify data retention risks by detecting which applications can store, train on, or learn from company data

  • Assess AI vendors and external APIs for data residency, training practices, and model integrity — require documented proof of security, compliance, and ethical standards before approval

  • Enable risk-based decision-making with AI third-party risk insights that empower governance committees to approve or deny new applications

  • Monitor all AI interactions by auditing prompts and outputs to prevent sensitive data leakage and maintain compliance

  • Guide employees effectively with ongoing training that balances GenAI productivity with secure usage

  • Enforce consistent policies using AI-aware tools deployed at both host and gateway levels

STEP 2: APPLY DATA LOSS PREVENTION & ZERO TRUST TO AI

Identity, context, and data awareness must extend into every AI interaction.

  • Enforce identity-based access controls using context and device posture for all AI tools

  • Apply microsegmentation to isolate and protect AI workloads

  • Inspect prompts with DLP, including prompt-level auditing, policies, and real-time detections

  • Monitor sensitive data uploads with continuous scanning and instant threat detection

STEP 3: OPERATIONALIZE YOUR AI DEFENSE

AI threats evolve as fast as AI itself. Defense must be continuous and adaptive.

  • Deploy AI-specific threat detection, including prompt injection monitoring, PII/secret scanning, and unsafe content controls

  • Apply runtime protections for custom-built applications and AI agents

  • Establish incident response protocols with dedicated runbooks for AI-related security incidents

  • Integrate AI security into SOC operations using AI-driven XDR and SIEM platforms

  • Maintain continuous compliance with ongoing regulatory audits and reporting

STEP 4: SECURE AI DEVELOPMENT (ADVANCED)

For organizations building AI internally, innovation increases value — and risk.

  • Harden AI infrastructure by securing APIs and model configurations

  • Scan open-source models to detect vulnerabilities, backdoors, or malicious code before deployment

  • Enforce model supply chain integrity with verification and validation processes

  • Embed security in development by integrating controls directly into CI/CD pipelines

  • Apply runtime policy enforcement to ensure safe deployment and operation