Topic

Risk Management

Risk management in AI governance refers to the systematic identification, assessment, and mitigation of potential harms, failures, and negative outcomes associated with AI systems throughout their lifecycle. This includes evaluating technical risks like model bias and system failures, as well as operational risks such as regulatory non-compliance and reputational damage. Effective risk management is essential for enterprises to maintain control over AI deployments, protect stakeholders, and ensure responsible use of AI technologies while meeting compliance obligations and board-level accountability requirements.

70 items

ResearchISO/OECD/UN2026-05-26

AI Governance Problems, Policy Options, and Research Gaps Mapped in LawAI Literature Review

LawAI released a comprehensive literature review titled 'Advanced AI Governance: A Literature Review of Problems, Options and Research Challenges,' surveying recent academic and policy research across compute security, software export controls, AI licensing, system evaluations, and procurement rules for AI safety. The review also examines corporate governance proposals including Responsible Scaling Policies and AI certification schemes. Published in January 2025, the document is intended to map the current state of knowledge and identify open research questions for policymakers and governance practitioners.

AI governance compute security export controls AI licensing model evaluation procurement risk management accountability responsible scaling transparency

Corporate PolicyUS2026-05-26

AI Incidents Rose 26% From 2022 to 2023, NACD Guidance Urges Boards to Adapt Oversight

The National Association of Corporate Directors has published governance guidance titled 'Tuning Corporate Governance for AI Adoption,' calling on boards to adapt oversight mechanisms to address AI-specific risks including hallucinations, data privacy concerns, and algorithmic bias. The guidance references AI Incident Database figures showing a 26 percent increase in AI incidents from 2022 to 2023, with 2024 data suggesting a further rise exceeding 32 percent. It is directed at US corporate boards and positions AI risk oversight as a core board-level responsibility.

United States board oversight AI risk incident tracking corporate governance algorithmic bias transparency accountability data privacy risk management

Corporate PolicyUS2026-05-03

Boards Urged to Overhaul AI Oversight as Deepfakes, Data Leaks Expose Governance Gaps, NACD Warns

The National Association of Corporate Directors (NACD) published guidance in January 2025 urging U.S. corporate boards to refine existing oversight mechanisms to address AI-specific governance failures. The guidance cites real-world incidents involving AI-generated deepfakes, confidential data leaks, and algorithmic bias as evidence that current board structures are inadequate for AI risk. NACD identifies a cross-functional leadership model as central to effective AI governance, placing the Chief AI Officer in coordination with the Chief Risk Officer, Chief Compliance Officer, Chief Legal Officer, and Chief Data Officer. For enterprise compliance teams, the guidance signals growing boardroom pressure to formalize AI accountability chains and integrate AI risk into existing enterprise risk management frameworks. Compliance professionals should expect boards to request clearer reporting lines, defined AI risk tolerances, and documented incident response protocols as standard governance requirements.

NACD board oversight AI governance enterprise risk management Chief AI Officer accountability risk management deepfakes United States algorithmic bias

ResearchUS2026-04-30

AI Incidents Rising Sharply While Responsible AI Evaluations Stay Rare, Stanford HAI 2025 Index Finds

Stanford University's Human-Centered Artificial Intelligence institute released its 2025 AI Index Report, documenting a sharp increase in AI-related incidents alongside a persistent gap between enterprise recognition of responsible AI risks and concrete action to address them. The report finds that standardized responsible AI evaluations remain uncommon among major industrial model developers, even as new benchmarking tools such as HELM Safety, AIR-Bench, and FACTS emerge to assess factuality and safety. A key finding is that increased global government cooperation on AI governance frameworks has not yet translated into widespread adoption of rigorous internal evaluation practices by private sector actors. For enterprise compliance teams, the report signals that voluntary responsible AI commitments are insufficient as a standalone posture, and that regulators and investors are increasingly scrutinizing the gap between stated AI risk awareness and documented risk management practice. Compliance professionals should use the report's benchmarking analysis to assess whether their organizations' model evaluation processes align with emerging industry standards and regulatory expectations.

Stanford HAI AI incidents responsible AI model evaluation risk management auditing transparency benchmarking AI governance United States

Corporate PolicyUS2026-04-22

AI Incidents Up 32% in 2024, NACD Urges Boards to Strengthen Oversight Structures

The National Association of Corporate Directors (NACD) has published its 2025 Governance Outlook, urging corporate boards in the United States to adapt oversight structures for AI adoption in response to a measurable rise in AI-related incidents. According to the AI Incident Database, AI incidents increased 26% between 2022 and 2023, with a further increase exceeding 32% in 2024. The guidance identifies hallucinations, bias, and data privacy failures as primary risk areas and calls for tuned governance frameworks and updated board reporting structures to address them. While non-binding, the guidance signals growing director-level accountability expectations that enterprise compliance and risk teams should factor into internal AI governance programs. Compliance professionals should note that board-level engagement on AI risk is increasingly treated as a baseline governance expectation, with implications for how responsible AI policies are documented, escalated, and reported to senior leadership.

NACD board oversight AI governance AI incidents risk management responsible AI transparency accountability United States compliance

ResearchGlobal2026-04-19

Seven Core Themes Shape Global AI Policy in AI Governance Dialogue's 2025 White Paper

The AI Governance Dialogue has released its second annual white paper, titled 'Steering the Future of AI,' examining seven themes central to the global AI governance landscape: autonomous agents, verification, socioeconomic impacts, multilateral coordination, standards, infrastructure, and risk management. The report gives particular attention to the role of AI Safety Institutes in conducting testing and red-teaming exercises, as well as to the development of multilateral protocols for AI safety. Published in January 2025, the paper draws on multi-stakeholder input to provide evidence-based insights intended to inform policymakers across jurisdictions. For enterprise compliance teams, the report serves as a structured reference for understanding where international consensus is forming and where regulatory gaps remain, particularly on autonomous agent governance and cross-border coordination mechanisms. Organizations monitoring alignment between internal AI risk frameworks and emerging international standards will find the thematic analysis relevant to gap assessments and board-level reporting.

AI governance risk management AI safety multilateral coordination standards autonomous agents transparency auditing model evaluation AI Safety Institutes

Corporate PolicyAPAC/China2026-04-19

22 Chinese AI Firms, Including Alibaba and Baidu, Sign Voluntary AI Safety Commitments

In December 2024, the China AI Industry Alliance coordinated the signing of AI Security and Safety Commitments by 17 major Chinese technology companies, a group that has since grown to 22 signatories. Participants include Alibaba, Baidu, ByteDance, Huawei, and Tencent. The commitments establish a voluntary framework for managing AI risks across development and deployment, with 18 companies having disclosed their AI security and safety practices publicly. While non-binding, the initiative signals that Chinese regulators and industry bodies are using voluntary disclosure mechanisms alongside China's existing binding AI regulations, creating a layered compliance environment that multinational enterprises operating in or partnering with Chinese AI companies should monitor.

China Alibaba Baidu ByteDance Huawei Tencent voluntary commitments AI safety risk management transparency industry self-regulation

Corporate PolicyUS2026-04-19

Mandatory AI Audits, Disclosures, and Red Teaming Recommended in NTIA Accountability Report

The National Telecommunications and Information Administration (NTIA) published its AI Accountability Policy Report in March 2024, setting out U.S. government recommendations to strengthen oversight of artificial intelligence systems. The report calls for mandatory AI audits, public disclosures, and liability rules, and advocates federal investment in tools, standards, and research supporting AI testing, evaluation, and red teaming. NTIA also recommends amending existing regulations to require these practices across sectors, signaling a potential shift toward binding accountability mechanisms at the federal level. Although the report is non-binding, it represents an authoritative statement of policy direction that enterprise compliance teams should track as a precursor to formal rulemaking. Organizations operating AI systems in U.S. markets should use the report's framework to benchmark their current audit, disclosure, and testing practices against emerging federal expectations.

NTIA United States auditing red teaming transparency accountability risk management model evaluation AI governance

ResearchISO/OECD/UN2026-05-10

Proactive, Adaptive AI Governance Frameworks Needed Globally, ITU Report Finds

The International Telecommunication Union (ITU) has released 'The Annual AI Governance Report 2025: Steering the Future of AI,' contributing to global discourse on how nations and institutions should structure AI oversight. The report emphasizes the need for proactive, inclusive, and adaptive governance approaches to address the rapid evolution and cross-border impact of AI systems. It is directed at policymakers, standards bodies, and international stakeholders seeking to align national and regional frameworks with global principles.

ITU international governance adaptive frameworks global coordination standards bodies cross-border compliance risk management transparency accountability EU AI Act

Corporate PolicyUS2026-05-10

GSA Establishes EDGE Board and AI Oversight Committee Under Updated CIO Directive 2185.1A

The U.S. General Services Administration has published its AI Strategies and Compliance Plan, establishing a formal AI Governance Board known as the EDGE Board, co-chaired by the agency's Chief Data Officer and Deputy Administrator, alongside a cross-functional AI Oversight Committee responsible for reviewing all internal AI requests and enforcing privacy and security controls. The updated CIO Directive 2185.1A expands the agency's AI governance scope beyond generative AI to cover the full spectrum of AI systems in use or under consideration at GSA. The structure sets a precedent for layered federal agency AI oversight with defined executive accountability.

United States GSA federal AI governance oversight structures accountability risk management procurement executive order government compliance AI policy

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