Developing A Governance Structure For AI

✴ AI Governance Blueprint via ISO Standards – The 4-Legged Stool✴ ➡ ISO42001: The Foundation for Responsible AI #ISO42001 is dedicated to AI governance, guiding organizations in managing AI-specific risks like bias, transparency, and accountability. Focus areas include: ✅Risk Management: Defines processes for identifying and mitigating AI risks, ensuring systems are fair, robust, and ethically aligned. ✅Ethics and Transparency: Promotes policies that encourage transparency in AI operations, data usage, and decision-making. ✅Continuous Monitoring: Emphasizes ongoing improvement, adapting AI practices to address new risks and regulatory updates. ➡#ISO27001: Securing the Data Backbone AI relies heavily on data, making ISO27001’s information security framework essential. It protects data integrity through: ✅Data Confidentiality and Integrity: Ensures data protection, crucial for trustworthy AI operations. ✅Security Risk Management: Provides a systematic approach to managing security risks and preparing for potential breaches. ✅Business Continuity: Offers guidelines for incident response, ensuring AI systems remain reliable. ➡ISO27701: Privacy Assurance in AI #ISO27701 builds on ISO27001, adding a layer of privacy controls to protect personally identifiable information (PII) that AI systems may process. Key areas include: ✅Privacy Governance: Ensures AI systems handle PII responsibly, in compliance with privacy laws like GDPR. ✅Data Minimization and Protection: Establishes guidelines for minimizing PII exposure and enhancing privacy through data protection measures. ✅Transparency in Data Processing: Promotes clear communication about data collection, use, and consent, building trust in AI-driven services. ➡ISO37301: Building a Culture of Compliance #ISO37301 cultivates a compliance-focused culture, supporting AI’s ethical and legal responsibilities. Contributions include: ✅Compliance Obligations: Helps organizations meet current and future regulatory standards for AI. ✅Transparency and Accountability: Reinforces transparent reporting and adherence to ethical standards, building stakeholder trust. ✅Compliance Risk Assessment: Identifies legal or reputational risks AI systems might pose, enabling proactive mitigation. ➡Why This Quartet? Combining these standards establishes a comprehensive compliance framework: 🥇1. Unified Risk and Privacy Management: Integrates AI-specific risk (ISO42001), data security (ISO27001), and privacy (ISO27701) with compliance (ISO37301), creating a holistic approach to risk mitigation. 🥈 2. Cross-Functional Alignment: Encourages collaboration across AI, IT, and compliance teams, fostering a unified response to AI risks and privacy concerns. 🥉 3. Continuous Improvement: ISO42001’s ongoing improvement cycle, supported by ISO27001’s security measures, ISO27701’s privacy protocols, and ISO37301’s compliance adaptability, ensures the framework remains resilient and adaptable to emerging challenges.

This new white paper "Steps Toward AI Governance" summarizes insights from the 2024 EqualAI Summit, cosponsored by RAND in D.C. in July 2024, where senior executives discussed AI development and deployment, challenges in AI governance, and solutions for these issues across government and industry sectors. Link: https://lnkd.in/giDiaCA3 * * * The white paper outlines several technical and organizational challenges that impact effective AI governance: Technical Challenges: 1) Evaluation of External Models:  Difficulties arise in assessing externally sourced AI models due to unclear testing standards and development transparency, in contrast to in-house models, which can be customized and fine-tuned to fit specific organizational needs. 2) High-Risk Use Cases: Prioritizing the evaluation of AI use cases with high risks is challenging due to the diverse and unpredictable outputs of AI, particularly generative AI. Traditional evaluation metrics may not capture all vulnerabilities, suggesting a need for flexible frameworks like red teaming. Organizational Challenges: 1) Misaligned Incentives: Organizational goals often conflict with the resource-intensive demands of implementing effective AI governance, particularly when not legally required. Lack of incentives for employees to raise concerns and the absence of whistleblower protections can lead to risks being overlooked. 2) Company Culture and Leadership: Establishing a culture that values AI governance is crucial but challenging. Effective governance requires authority and buy-in from leadership, including the board and C-suite executives. 3) Employee Buy-In: Employee resistance, driven by job security concerns, complicates AI adoption, highlighting the need for targeted training. 4) Vendor Relations: Effective AI governance is also impacted by gaps in technical knowledge between companies and vendors, leading to challenges in ensuring appropriate AI model evaluation and transparency. * * * Recommendations for Companies: 1) Catalog AI Use Cases: Maintain a centralized catalog of AI tools and applications, updated regularly to track usage and document specifications for risk assessment. 2) Standardize Vendor Questions: Develop a standardized questionnaire for vendors to ensure evaluations are based on consistent metrics, promoting better integration and governance in vendor relationships. 3) Create an AI Information Tool: Implement a chatbot or similar tool to provide clear, accessible answers to AI governance questions for employees, using diverse informational sources. 4) Foster Multistakeholder Engagement: Engage both internal stakeholders, such as C-suite executives, and external groups, including end users and marginalized communities. 5) Leverage Existing Processes: Utilize established organizational processes, such as crisis management and technical risk management, to integrate AI governance more efficiently into current frameworks.

𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐬𝐧’𝐭 𝐚 𝐠𝐚𝐭𝐞 𝐢𝐭’𝐬 𝐭𝐡𝐞 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐭𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐀𝐈 𝐬𝐜𝐚𝐥𝐞. Most teams bolt governance on. Then wonder why scaling stalls. The shift: 𝐃𝐞𝐬𝐢𝐠𝐧 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐬 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞. → Policies as Code Hard-code data boundaries, approvals, and retention. No PDFs on SharePoint. → Evaluation Harnesses Test safety, bias, drift, and instruction-following before release continuously. → Observability Trace every decision: inputs, tools, and model versions audits in hours, not weeks. Change Management Bake in gates, rollout plans, and feature flags. 𝐂𝐚𝐬𝐞 𝐢𝐧 𝐩𝐨𝐢𝐧𝐭: A bank deployed onboarding agents under regulatory scrutiny. ↳ Policies-as-code enforced KYC + disclosures automatically. ↳ Eval harness caught risky prompts pre-production. ↳ Deployment time dropped 60%. ↳ Incidents trended toward zero. Result? Governance wasn’t friction it became the feature buyers trusted most. Ready to turn governance from blocker into competitive advantage? ♻️ Repost to your LinkedIn empower your network & follow Timothy Goebel for expert insights #GenerativeAI #EnterpriseAI #AIProductManagement #LLMAgents #ResponsibleAI

In my work with organizations rolling out AI and generative AI solutions, one concern I hear repeatedly from leaders, and the c-suite is how to get a clear, centralized “AI Risk Center” to track AI safety, large language model's accuracy, citation, attribution, performance and compliance etc. Operational leaders want automated governance reports—model cards, impact assessments, dashboards—so they can maintain trust with boards, customers, and regulators. Business stakeholders also need an operational risk view: one place to see AI risk and value across all units, so they know where to prioritize governance. One of such framework is MITRE’s ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Matrix. This framework extends MITRE ATT&CK principles to AI, Generative AI, and machine learning, giving us a structured way to identify, monitor, and mitigate threats specific to large language models. ATLAS addresses a range of vulnerabilities—prompt injection, data leakage, malicious code generation, and more—by mapping them to proven defensive techniques. It’s part of the broader AI safety ecosystem we rely on for robust risk management. On a practical level, I recommend pairing the ATLAS approach with comprehensive guardrails - such as: • AI Firewall & LLM Scanner to block jailbreak attempts, moderate content, and detect data leaks (optionally integrating with security posture management systems). • RAG Security for retrieval-augmented generation, ensuring knowledge bases are isolated and validated before LLM interaction. • Advanced Detection Methods—Statistical Outlier Detection, Consistency Checks, and Entity Verification—to catch data poisoning attacks early. • Align Scores to grade hallucinations and keep the model within acceptable bounds. • Agent Framework Hardening so that AI agents operate within clearly defined permissions. Given the rapid arrival of AI-focused legislation—like the EU AI Act, now defunct  Executive Order 14110 of October 30, 2023 (Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence) AI Act, and global standards (e.g., ISO/IEC 42001)—we face a “policy soup” that demands transparent, auditable processes. My biggest takeaway from the 2024 Credo AI Summit was that responsible AI governance isn’t just about technical controls: it’s about aligning with rapidly evolving global regulations and industry best practices to demonstrate “what good looks like.” Call to Action: For leaders implementing AI and generative AI solutions, start by mapping your AI workflows against MITRE’s ATLAS Matrix. Mapping the progression of the attack kill chain from left to right - combine that insight with strong guardrails, real-time scanning, and automated reporting to stay ahead of attacks, comply with emerging standards, and build trust across your organization. It’s a practical, proven way to secure your entire GenAI ecosystem—and a critical investment for any enterprise embracing AI.

A New Path for Agile AI Governance To avoid the rigid pitfalls of past IT Enterprise Architecture governance, AI governance must be built for speed and business alignment. These principles create a framework that enables, rather than hinders, transformation: 1. Federated & Flexible Model: Replace central bottlenecks with a federated model. A small central team defines high-level principles, while business units handle implementation. This empowers teams closest to the data, ensuring both agility and accountability. 2. Embedded Governance: Integrate controls directly into the AI development lifecycle. This "governance-by-design" approach uses automated tools and clear guidelines for ethics and bias from the project's start, shifting from a final roadblock to a continuous process. 3. Risk-Based & Adaptive Approach: Tailor governance to the application's risk level. High-risk AI systems receive rigorous review, while low-risk applications are streamlined. This framework must be adaptive, evolving with new AI technologies and regulations. 4. Proactive Security Guardrails: Go beyond traditional security by implementing specific guardrails for unique AI vulnerabilities like model poisoning, data extraction attacks, and adversarial inputs. This involves securing the entire AI/ML pipeline—from data ingestion and training environments to deployment and continuous monitoring for anomalous behavior. 5. Collaborative Culture: Break down silos with cross-functional teams from legal, data science, engineering, and business units. AI ethics boards and continuous education foster shared ownership and responsible practices. 6. Focus on Business Value: Measure success by business outcomes, not just technical compliance. Demonstrating how good governance improves revenue, efficiency, and customer satisfaction is crucial for securing executive support. The Way Forward: Balancing Control & Innovation Effective AI governance balances robust control with rapid innovation. By learning from the past, enterprises can design a resilient framework with the right guardrails, empowering teams to harness AI's full potential and keep pace with business. How does your Enterprise handle AI governance?

AI Governance: Map, Measure and Manage 1. Governance Framework:   - Contextualization: Implement policies and practices to foster risk management in development cycles.   - Policies and Principles: Ensure generative applications comply with responsible AI, security, privacy, and data protection policies, updating them based on regulatory changes and stakeholder feedback.   - Pre-Trained Models: Review model information, capabilities, limitations, and manage risks.   - Stakeholder Coordination: Involve diverse internal and external stakeholders in policy and practice development.   - Documentation: Provide transparency materials to explain application capabilities, limitations, and responsible usage guidelines.   - Pre-Deployment Reviews: Conduct risk assessments pre-deployment and throughout the development cycle, with additional reviews for high-impact uses. 🎯Map 2. Risk Mapping:   - Critical Initial Step: Inform decisions on planning, mitigations, and application appropriateness.   - Impact Assessments: Identify potential risks and mitigations as per the Responsible AI Standard.   - Privacy and Security Reviews: Analyze privacy and security risks to inform risk mitigations.   - Red Teaming: Conduct in-depth risk analysis and identification of unknown risks. 🎯Measure 3. Risk Measurement:   - Metrics for Risks: Establish metrics to measure identified risks.   - Mitigation Performance Testing: Assess effectiveness of risk mitigations. 🎯Manage 4. Risk Management:   - Risk Mitigation: Manage risks at platform and application levels, with mechanisms for incident response and application rollback.   - Controlled Release: Deploy applications to limited users initially, followed by phased releases to ensure intended behavior.   - User Agency: Design applications to promote user agency, encouraging users to edit and verify AI outputs.   - Transparency: Disclose AI roles and label AI-generated content.   - Human Oversight: Enable users to review AI outputs and verify information.   - Content Risk Management: Incorporate content filters and processes to address problematic prompts.   - Ongoing Monitoring: Monitor performance and collect feedback to address issues.   - Defense in Depth: Implement controls at every layer, from platform to application level. Source: https://lnkd.in/eZ6HiUH8