AI systems are now embedded into modern, cloud-based systems, where scalability, automation, and rapid iteration are core design principles. As organizations operationalize artificial intelligence (AI) systems to achieve faster deployment cycles, informed decision making, and more intensive handling of sensitive data, the adoption of a structured AI governance framework and its continuous maturity becomes imperative.
Despite the efficiency cloud-native AI systems are highly distributed in a way that poses security threats, leaving them vulnerable to model drift, algorithmic bias, and targeted adversarial attacks. Therefore, security practitioners have a role to protect these systems using various measures and AI security frameworks. This article presents a phased approach to AI governance framework adoption, using ISO IEC 42001:2023 and the NIST AI Risk Management Framework to depict foundational implementation steps as well as additional considerations for organizations with mature AI governance programs.
Create a cross-functional team to establish governance and accountability (AI/ML, DevOps, Security, and Legal)
Establish a cross-functional governance structure with accountable owners from all the critical AI/ML areas, this could include groups such as: AI/ML, DevOps, security, legal, and GRC. The ownership can be defined using a RACI-based model to clearly assign accountability and responsibility across the AI lifecycle. The designated owners should at a minimum have a working understanding of the ISO 42001 and NIST AI RMF frameworks to ensure that the governance requirements are translated to operational control requirements.
Map both the frameworks for close integration to establish common AI security framework
The goal here is to map the ISO 42001 clauses to NIST AI-RMF functions (Govern, Map, Measure, and Manage) to establish a unified and operational AI security framework. This unified framework would serve as a foundation to protect and continuously evolve the AI security specific controls. The ISO 42001 would serve as the governance backbone and management system controls, while the NIST AI-RMF specifically provides structure to identify, assess, and manage AI specific risks.
Align governance and risk controls at each stage of the cloud native AI lifecycle to ensure consistent and actionable implementation.
The ISO 42001 and NIST AI-RMF control requirements should be mapped to all the key stages of the AI lifecycle; this includes: model training, data ingestion, data pipelines, CI/CD pipelines, and continuous monitoring. Alignment of controls to various stages of lifecycle not only establishes governance intent but also enhances operational execution. For this alignment and implementation to be actionable it’s recommended to maintain an inventory of AI specific assets and models against associated risk levels. This alignment must be regularly reviewed and updated for changes.
Define measurable indicators and success criteria to evaluate the effectiveness and maturity of AI governance and security controls.
It’s critical to define measurable KPIs and CSFs to assess whether the framework controls are effective and operational as needed. KPIs should be designed to quantify AI-specific risks and control performance (e.g. model drift detection latency, AI bias outcomes assessments, or remediation metrics for any findings) while the CSFs define the specific conditions that are critical for sustained program success such as: cross functional accountability, continuous risk monitoring, or any assessment results. When combined, KPIs and CSFs both provide objective measurement of AI trustworthiness and continuous improvement.
Implement observability to support transparency, accountability, and continuous oversight of AI systems.
Security practitioners and leaders should establish end to end observability across the AI lifecycle. This should include telemetry collection at each lifecycle stage that feeds a centralized AI security observability layer. This observability layer enables early detection of anomalous behavior across the lifecycle and supports evidence-based governance and effective AI risk management.
Apply strong governance and technical controls across data, models, and dependencies.
AI supply chain security is extremely critical because cloud native AI systems increasingly rely on external datasets, predefined models, open-source libraries, and managed services, all of these introduce significant risks to the AI lifecycle beyond organization’s control. Compromised training data, data poisoning, or even unverified dependencies can affect the model integrity, lead to biased/unsafe outcomes, and can increase the risk across various systems at scale. Organizations should incorporate controls that can mitigate these risks some of these include data governance, data encryption (at rest/in transit), implementing key management protocols, automated policy checks, data integrity checks, and data lineage tracking. Implementation of these controls should extend across the complete AI lifecycle and enable early detection of supply chain risks.
Prevent unauthorized access and misuse of AI assets by enforcing least privilege and strong identity governance.
Strong identity and access management (IAM) controls are critical and foundational. However, they become critical in complex and mature organizations where datasets, models, training environments, and inference endpoints are often distributed across multiple platforms and teams. Insufficient access control practices can expose training data, allow unauthorized model modification, or enable abuse of any supporting services thereby undermining both security and trustworthiness of the AI systems. Organizations should enforce role-based and attribute-based access controls aligned with least-privilege principles across all AI lifecycle stages.
As AI systems become more distributed and embedded in critical AI workflows especially in cloud environments, effective security and trust requires more than implementing isolated/standalone technical countermeasures. Organizations must adopt a structured AI governance framework that aligns governance intent with operational execution. A phased approach to AI governance framework adoption utilizing ISO/IEC 42001:2023 as the governance backbone and the NIST AI-RMF to assess & treat AI risks. This methodology proactively enables organizations to establish foundational controls and progressively mature their AI security governance & posture. For mature organizations building advanced capabilities to the AI lifecycle such as: Observability, supply-chain security, and access controls further strengthen trust across the AI lifecycle. All in all, AI Governance must be an ongoing framework that must evolve alongside organizational maturity and innovation.
Gagan Koneru is a seasoned cybersecurity professional with deep expertise across multiple security domains including Security Governance, Risk & Compliance (GRC), Cloud Security, and Technology Risk. With extensive international experience across complex environments, he has led critical enterprise-wide security programmes while building organisational trust through security governance, and is highly distinguished for maturing risk-driven security posture and implementing robust security and compliance frameworks that improve digital trust, enhance end-user security, and create long-term business value.
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