AI System Life Cycle Management Controls

The Secure AI Lifecycle (SAIL) Framework is one of the actionable roadmaps for building trustworthy and secure AI systems. Key highlights include: • Mapping over 70 AI-specific risks across seven phases: Plan, Code, Build, Test, Deploy, Operate, Monitor • Introducing “Shift Up” security to protect AI abstraction layers like agents, prompts, and toolchains • Embedding AI threat modeling, governance alignment, and secure experimentation from day one • Addressing critical risks including prompt injection, model evasion, data poisoning, plugin misuse, and cross-domain prompt attacks • Integrating runtime guardrails, red teaming, sandboxing, and telemetry for continuous protection • Aligning with NIST AI RMF, ISO 42001, OWASP Top 10 for LLMs, and DASF v2.0 • Promoting cross-functional accountability across AppSec, MLOps, LLMOps, Legal, and GRC teams Who should take note: • Security architects deploying foundation models and AI-enhanced apps • MLOps and product teams working with agents, RAG pipelines, and autonomous workflows • CISOs aligning AI risk posture with compliance and regulatory needs • Policymakers and governance leaders setting enterprise-wide AI strategy Noteworthy aspects: • Built-in operational guidance with security embedded across the full AI lifecycle • Lifecycle-aware mitigations for risks like context evictions, prompt leaks, model theft, and abuse detection • Human-in-the-loop checkpoints, sandboxed execution, and audit trails for real-world assurance • Designed for both code and no-code AI platforms with complex dependency stacks Actionable step: Use the SAIL Framework to create a unified AI risk and security model with clear roles, security gates, and monitoring practices across teams. Consideration: Security in the AI era is more than a tech problem. It is an organizational imperative that demands shared responsibility, executive alignment, and continuous vigilance.

This new white paper "Introduction to AI assurance" by the UK Department for Science, Innovation, and Technology from Feb 12, 2024, provides an EXCELLENT overview of assurance methods and international technical standards that can be utilized to create and implement ethical AI systems. The new guidance is based on the UK AI governance framework, laid out in the 2023 white paper "A pro-innovation approach to AI regulation". This white paper defined 5 universal principles applicable across various sectors to guide and shape the responsible development and utilization of AI technologies throughout the economy: - Safety, Security, and Robustness - Appropriate Transparency and Explainability - Fairness - Accountability and Governance - Contestability and Redress The 2023 white paper also introduced a suite of tools designed to aid organizations in understanding "how" these outcomes can be achieved in practice, emphasizing tools for trustworthy AI, including assurance mechanisms and global technical standards. See: https://lnkd.in/gydvi9Tt The new publication, "Introduction to AI assurance," is a deep dive into these assurance mechanisms and standards. AI assurance encompasses a spectrum of techniques for evaluating AI systems throughout their lifecycle. These range from qualitative assessments for evaluating potential risks and societal impacts to quantitative assessments for measuring performance and legal compliance. Key techniques include: - Risk Assessment: Identifies potential risks like bias, privacy, misuse of technology, and reputational damage. - Impact Assessment: Anticipates broader effects on the environment, human rights, and data protection. - Bias Audit: Examines data and outcomes for unfair biases. - Compliance Audit: Reviews adherence to policies, regulations, and legal requirements. - Conformity Assessment: Verifies if a system meets required standards, often through performance testing. - Formal Verification: Uses mathematical methods to confirm if a system satisfies specific criteria. The white paper also explains how organizations in the UK can ensure their AI systems are responsibly governed, risk-assessed, and compliant with regulations: 1.) For demonstrating good internal governance processes around AI, a conformity assessment against standards like ISO/IEC 42001 (AI Management System) is recommended. 2.) To understand the potential risks of AI systems being acquired, an algorithmic impact assessment by a accredited conformity assessment body is advised. This involves (self) assessment against a proprietary framework or responsible AI toolkit. 3.) Ensuring AI systems adhere to existing data protection regulations involves a compliance audit by a third-party assurance provider. This white paper also has exceptional infographics! Pls, check it out, and TY Victoria Beckman for posting and providing us with great updates as always!

The ISO - International Organization for Standardization has adopted ISO/IEC 42001, the world's first "Artificial Intelligence Management System (#AIMS)." #ISO42001 is an international #standard that specifies requirements for establishing, implementing, maintaining, and continually improving an #artificialintelligence management system for entities providing or utilizing #AI-based products or services. The #ISO 42001 aims to provide a comprehensive approach for organizations to systematically address and control the #risks related to the development and deployment of AI. The standard emphasizes a commitment to responsible AI practices, fostering global interoperability, and setting a foundation for the development and deployment of #responsibleAI. The new standard is based on the High-Level Structure (#HLS) of ISO/IEC, which gives management system standards a uniform structure and similar core content. It also provides a list of #controls for organizations to choose which ones they deem relevant for implementation. At a glance, implementation of ISO/IEC 42001 requires: - Integrating AI management with the current systems and structures in your organization (AIMS). - Performing an impact analysis evaluating how AI systems affect individuals and society as a whole, taking safety, transparency, and fairness into account. - Creating and enforcing AI-related policies, with an emphasis on internal structure, AI resources, and the lifecycle of AI systems. - Managing data responsibly, including training data preparation and management, utilized in AI systems. - Monitoring and ongoing development to make sure that the AI systems in use are in line with organizational objectives. https://lnkd.in/eiVEuxSY

♻ AI Lifecyle Management for ISO42001 Certification ♻ To establish an Artificial Intelligence Management System (AIMS) compliant with ISO42001, it's essential to integrate the AI lifecycle management processes detailed in ISO5338. This alignment ensures that AI systems are developed, deployed, and managed in a manner that adheres to the requirements of ISO 42001, focusing on ethical, transparent, and responsible AI provision, development, and use. ✅1. Design and Development: ⬜ Utilize ISO5338's guidance on AI-specific risk assessment to address ISO42001's emphasis on identifying, analyzing, and mitigating AI risks effectively. Remember, you will use ISO23894 (or NIST AI RMF) for a solid framework for your AI risk management program. ⬜ Adhere to data quality and provenance requirements, essential for AI transparency and accountability. In my opinion, this is the area where most companies will struggle. ✅2. Verification and Validation: ⬜ Follow ISO5338's protocols for verifying and validating AI systems, ensuring they meet predefined criteria and are aligned with ISO42001's standards for impact assessment. ISO42005 (DIS) will be your source of truth for planning, executing, and documenting your in-scope AI impact assessments. ✅3. Implementation and Operation: ⬜ Implement operational planning and human oversight controls as outlined in ISO5338, crucial for the deployment and operation phases and in line with ISO42001's requirements for operational control and human oversight. This is the area that ISO5338 truly shines. ✅ 4. Monitoring and Continuous Improvement: ⬜ Engage in continuous monitoring and improvement processes as per ISO5338, aligning with ISO42001's guidelines for performance evaluation and continual improvement. Remember (and operationalize) the Deming Cycle, Plan-Do-Check-Act (PDCA). You will not regret the investment you make in ISO5338 as it will both treat your risks associated with compliance with ISO42001 AND foster the development of AI systems that are ethically grounded, transparent, and accountable. This standard will allow you and your organization to meet the overarching goals of responsible AI management, in a way that allows you to optimize risk and overall cost. If you have questions, or need help getting started, please don't hesitate to let me know! #iso42001 #ethicalAI #iso5338 #ALIGN A-LIGN #ComplianceAlignedtoYou #TheBusinessofCompliance