{"id":40264,"date":"2024-04-25T11:45:16","date_gmt":"2024-04-25T15:45:16","guid":{"rendered":"https:\/\/ourpublicservice.org\/?p=40264"},"modified":"2024-06-17T13:21:37","modified_gmt":"2024-06-17T17:21:37","slug":"how-enterprise-risk-managers-can-help-federal-agencies-implement-artificial-intelligence-guidance","status":"publish","type":"post","link":"https:\/\/ourpublicservice.org\/blog\/how-enterprise-risk-managers-can-help-federal-agencies-implement-artificial-intelligence-guidance\/","title":{"rendered":"How enterprise risk managers can help federal agencies implement artificial intelligence guidance"},"content":{"rendered":"\n

Recently, the federal government has issued guidance<\/a>, frameworks<\/a> and an executive order<\/a> tied to the management of artificial intelligence risks.  <\/p>\n\n\n\n

Enterprise Risk Management, or ERM, supports the strategic and operational responsibilities of agency leaders in meeting these standards. In January, the Partnership for Public Service hosted an event with Deloitte & Touche LLP on this topic featuring two panelists: Kevin Walsh<\/a>, the Government Accountability Office\u2019s director of information technology and cybersecurity, and Martin Stanley<\/a>, strategic technology branch chief at the Cybersecurity and Infrastructure Security Agency.<\/p>\n\n\n

\n
\"\"
Cynthia Vitters from Deloitte, Kevin Walsh from the Government Accountability Office, and Martin Stanley from the Cybersecurity and Infrastructure Agency in a virtual panel discussion, “The Evolving Enterprise Risk Manager: Understanding the New AI Guidance.”<\/em><\/figcaption><\/figure><\/div>\n\n\n

Walsh and Stanley examined recent federal AI guidance and requirements (including the AI executive order and NIST AI Risk Management Framework<\/a>, discussed AI use cases, and shared innovative practices for ERM practitioners to support AI risk management efforts in collaboration with chief artificial intelligence officers and their teams.  <\/p>\n\n\n\n

A recent GAO report<\/a>, authored by Walsh, highlights the continued need for the effective and responsible implementation of AI across government. Released prior to the publication of OMB\u2019s draft memorandum<\/a>, the report notes that only 10 of the 23 agencies examined had implemented \u201ckey AI requirements,\u201d while 12 had implemented some and one did not apply.  <\/p>\n\n\n\n

An enterprise view of AI: Accelerating value<\/strong> <\/h3>\n\n\n\n

Agency ERM programs are uniquely positioned to aid in the safe, secure and trustworthy development and use of AI technologies\u2014coalescing AI risk management from an enterprise perspective. As agencies begin to frame initial AI risk management conversations, it\u2019s critical to employ three practices:  <\/p>\n\n\n\n

1. Employ a defined governance structure. <\/strong><\/h5>\n\n\n\n

Governance enables an organization\u2019s risk management program by building AI risk management processes and procedures into the lifecycle of an AI system. A defined governance structure with clear roles and responsibilities sets the tone for the organization\u2019s risk culture and bolsters risk-informed decision-making.  <\/p>\n\n\n\n

ERM offers existing risk governance structures that streamline accountability and transparency, and foster engagement, among key AI actors and oversight responsibilities. Stanley shared that \u201cthe govern function is the first thing that I recommend an organization adopts from the AI Risk Management Framework.\u201d He recommended that agencies \u201cpick out a few govern subfunctions that represent the needs of the organization and get going on building those capabilities into the ERM framework.\u201d <\/p>\n\n\n\n

2<\/strong>. Develop a risk-based approach to AI use and implementation.<\/strong> <\/h5>\n\n\n\n

By integrating AI considerations into existing ERM programs, agencies can develop a more complete risk picture of AI to improve decision-making. The panelists unanimously determined that AI has the potential to drastically improve governmental operations but also comes with large-scale societal and national risks that need to be managed appropriately.  <\/p>\n\n\n\n

Stanley noted that risk professionals need to do more than just create a list of internal controls and move on. \u201cThe intent [of the AI risk management framework],\u201d he said, \u201cis to build a capacity where the most important [and] the most concerning risks can be addressed, measured and managed.\u201d Organizations should examine their risk profiles to understand how risks to and from AI could shift existing risk scenarios. <\/p>\n\n\n\n

3. Collaborate with necessary stakeholders. <\/strong><\/h5>\n\n\n\n

It\u2019s important to have a clear governance structure, complete with appropriate roles and delineated responsibilities, to effectively operationalize AI risk management. Collaboration may begin with the chief artificial intelligence officer and the chief risk officer but should extend across the enterprise, potentially including IT, strategy, talent and HR, cyber and procurement functions. Walsh agreed, noting that \u201ca chief artificial intelligence officer ought to, if they don\u2019t already, have a strong working relationship with the ERM team.\u201d <\/p>\n\n\n\n

Read our <\/em>previous blog post<\/em><\/a> for a recap of our previous working session in this series.<\/em><\/p>\n\n\n\n

\n","protected":false},"excerpt":{"rendered":"

Recently, the federal government has issued guidance, frameworks and an executive order tied to the management of artificial intelligence risks.   Enterprise Risk Management, or ERM, supports the strategic and operational responsibilities of agency leaders in meeting these standards. 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