AI Compliance Management

AI Compliance Management

• Create AI Compliance Project
  Creating an AI compliance project is the initial step towards ensuring an organization adheres to regulatory and company requirements. This process involves the establishment of custom projects that reflect the specific compliance initiatives of the company. By fine-tuning the project scope, organizations can target specific cloud accounts and data+AI assets relevant to their compliance needs. Incorporating multiple compliance frameworks into a single project allows for a more comprehensive approach, enabling the configuration of each framework to select relevant controls and tests. This streamlined management of compliance initiatives facilitates the tracking of project-level reports and status assessments to gauge compliance readiness and governance maturity.
• Identify AI Regulations and Standards
  The second step involves identifying the regulations and standards pertinent to the project. This includes selecting relevant data and AI security, privacy, and governance frameworks. 

  It is important that you determine which comprehensive and sectoral AI laws and regulations might apply to your organization by the use or development of AI models and systems (preferably before deployment). This might involve a detailed understanding of the AI models or systems being provided/deployed or used by your organization. You also may be required to determine which sectoral or comprehensive privacy laws might apply to your AI Models.

  Organizations can benefit from leveraging a built-in library of standards and regulations, which encompasses emerging AI regulations such as the NIST AI RMF, the EU AI Act, and the Singapore AI Governance Framework, among others. Regulations like GDPR and CCPA/CPRA can also be included. Furthermore, companies should be able to build their own custom frameworks to report on internal AI compliance requirements, ensuring a tailored approach to compliance.

• Automate Control Assessment
  Automating control assessment marks the third step in the process, wherein common controls and tests are leveraged to efficiently assess AI compliance across multiple frameworks. Once you have determined which AI and data protection laws apply to your organization and to the AI systems and models that you provide or use, it is important that you employ the necessary regulatory controls as per the unique requirements of each law/regulation. 

  Laws such as the EU AI Act envision different regulatory controls for different types of AI models/systems provided or used by your organization. Privacy laws such as the GDPR also require different compliance activities to be performed depending upon the type of AI model or system processing personal data of protected persons. For organizations which provide/deploy or use various AI systems and models across multiple domains or jurisdictions, it can be a difficult challenge to implement and monitor the correct regulatory controls tailored for each unique use case. 

  By utilizing a library of predefined tests, organizations can automate the assessment of controls related to various AI security, privacy, and governance frameworks. This streamlined process allows for thorough verification of compliance status and the development of custom tests tailored to the unique requirements of organization-specific frameworks. Such an approach simplifies the compliance assessment process, facilitating the simultaneous review and resolution of a broad spectrum of AI compliance issues.

• Incorporate Attestation Results
  The incorporation of attestation results constitutes the fourth step. This involves the integration of human input and findings from data privacy and AI risk assessments into the compliance assessment process. By enabling the inclusion of human insights, organizations can assess AI compliance with controls that are challenging to automate. Assigning owners for each control, who can then share notes and upload documents as evidence, enriches the AI compliance process, providing a richer context for decision-making and insight sharing.
• Report AI Compliance Findings
  Finally, reporting AI compliance findings is crucial for communicating the compliance posture to relevant stakeholders. This entails informing executives and key stakeholders about the organization’s AI compliance readiness. Organizations must ensure they have a mechanism to audit the deployment and evaluation of required controls on their AI systems and models. Demonstrating constant compliance with AI laws and regulations and related privacy and sectoral laws to regulators and internal and external stakeholders is a constant effort. 

  The ability to automatically generate reports in formats such as PDF and PowerPoint is particularly beneficial for executive-level compliance reporting and meetings. Sharing these reports with auditors and conducting compliance readiness reviews with data stewards and application owners drives the continuous improvement of AI compliance practices, ensuring that the organization remains aligned with both regulatory expectations and internal governance standards.