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Response plans, escalation procedures, and incident management for operational emergencies.
Also in Operations & Security
Establish procedures for investigating, documenting, and remediating harmful content incidents that bypass moderation systems, including coordination with relevant authorities, root cause analysis, and system improvement protocols. Include specific procedures for suspected malicious use cases.
Reasoning
Establishes post-deployment incident response procedures for investigating, documenting, and remediating harmful content incidents.
Establish AI system access controls
Implement comprehensive access management including role-based access control (RBAC), authentication mechanisms, and audit logging for AI models and associated resources.
2.3.2 Access & Security ControlsImplement AI asset protection framework
Deploy technical protection measures including encryption, secure enclaves, and versioning controls for AI models and associated data.
1.2.4 Security InfrastructureEstablish security validation framework
Execute comprehensive pre-deployment security validation including AI-specific vulnerability assessments, penetration testing, and security requirement verification.
2.2.2 Testing & EvaluationImplement continuous security testing system
Deploy ongoing security testing mechanisms including automated vulnerability scanning, continuous security monitoring, and periodic reassessment of security controls.
2.2 Risk & AssuranceImplement AI security defense system
Deploy active defense mechanisms combining continuous security monitoring, input validation, adversarial detection, and adaptive response capabilities specific to AI systems.
1.2 Non-ModelEstablish AI system integration framework
Define and implement a comprehensive framework for AI system integration including architecture review, compatibility testing, and integration validation processes.
2.2.2 Testing & EvaluationThe Unified Control Framework: Establishing a Common Foundation for Enterprise AI Governance, Risk Management and Regulatory Compliance
Eisenberg, Ian W.; Gamboa, Lucía; Sherman, Eli (2025)
The rapid adoption of AI systems presents enterprises with a dual challenge: accelerating innovation while ensuring responsible governance. Current AI governance approaches suffer from fragmentation, with risk management frameworks that focus on isolated domains, regulations that vary across jurisdictions despite conceptual alignment, and high-level standards lacking concrete implementation guidance. This fragmentation increases governance costs and creates a false dichotomy between innovation and responsibility. We propose the Unified Control Framework (UCF): a comprehensive governance approach that integrates risk management and regulatory compliance through a unified set of controls. The UCF consists of three key components: (1) a comprehensive risk taxonomy synthesizing organizational and societal risks, (2) structured policy requirements derived from regulations, and (3) a parsimonious set of 42 controls that simultaneously address multiple risk scenarios and compliance requirements. We validate the UCF by mapping it to the Colorado AI Act, demonstrating how our approach enables efficient, adaptable governance that scales across regulations while providing concrete implementation guidance. The UCF reduces duplication of effort, ensures comprehensive coverage, and provides a foundation for automation, enabling organizations to achieve responsible AI governance without sacrificing innovation speed.
Deploy
Releasing the AI system into a production environment
Deployer
Entity that integrates and deploys the AI system for end users
Govern
Policies, processes, and accountability structures for AI risk management
