AI Security Governance: A Practical Framework for 2026 | Harness Blog

AI security governance is presented as a structured approach for keeping AI models, agents, and assistants within an organization’s security and compliance boundaries through four connected pillars: clear policies, repeatable risk assessments, monitoring and audit records, and named accountability. The framework responds to growing regulatory requirements, including EU AI Act transparency and high-risk obligations, as well as the rapid adoption of AI tools in software development, where shadow AI, unowned systems, and incomplete activity records can create security and compliance gaps. Its central artifact is an AI risk register that inventories AI systems and use cases, assigns risk tiers based on data sensitivity, autonomy, and potential impact, identifies an accountable individual owner, and establishes review schedules. The approach aligns with the NIST AI Risk Management Framework and ISO/IEC 42001, emphasizing that governance should be continuously maintained rather than treated as a one-time approval process. Harness advocates embedding these controls into development pipelines through policy as code, service scorecards, observability, audit trails, and role-based access controls, citing customer examples involving consolidated deployment records and faster, more standardized feature-flag releases.

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