AI Governance Framework Adoption in Cloud-Native AI Systems: Phased Approach and Considerations

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.

Phased model for AI Governance framework adoption:

1. Establish Governance Ownership

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.

2. Framework Mapping (ISO 42001 to NIST AI-RMF)

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.

3. Align Requirements with the Cloud-Native AI Lifecycle

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.

4. Measurable Key Performance Indicators (KPIs) and Critical Success Factors (CSFs)

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.

Additional Considerations for mature AI security & governance programs:

1. Establish End to End Observability across the AI lifecycle

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.

2. Strengthen AI Supply-Chain Security Controls

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.

3. Enforce Strong Identity and Access Controls across the AI lifecycle

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.

Conclusion:

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.

Industry Data References:

• 2025 State of Cloud Security Report from Orca Security suggests that 80% of the organizations now use AI in cloud and 62% of organizations have at least one vulnerable AI package in their cloud environment: https://orca.security/lp/2025-state-of-cloud-security-report/
• 2025 State of Cloud and AI Security global survey from Cloud Security Alliance (CSA) has also noted that 34% organizations have reported at least one AI related data breach: https://cloudsecurityalliance.org/artifacts/the-state-of-cloud-and-ai-security-2025