Building Secure AI Systems: Why Asset Management Matters More Than Ever
AI security isn't about firewalls—it's about knowing your assets. Discover how leading organizations protect their AI investments.
3 min read
top of page
AI Governance & Risk
AI Governance and Risk Management focuses on ensuring AI systems are secure, ethical, and aligned with organizational objectives. With AI adoption accelerating, organizations face increasing risks that require robust frameworks like the NIST Risk Management Framework (RMF) to proactively identify, assess, and mitigate potential threats. By integrating governance structures and risk-based methodologies, businesses can enhance accountability, maintain regulatory compliance with standards like the EU AI Act, and foster sustainable, trustworthy AI innovation.
AI Trust Framework
The AI Trust Framework outlines the core pillars—Transparency and Oversight, Technical Integrity, Ethical Considerations and Operational Excellence — essential for fostering responsible, secure, and accountable AI systems in today's evolving regulatory and operational landscape.
Holistic AI Risk Management
Holistic AI Risk Management requires a lifecycle-based approach that addresses risks at every stage of an AI system's journey—from design and development to deployment and ongoing operation. By integrating risk management practices throughout the AI lifecycle, organizations can proactively identify vulnerabilities, mitigate emerging threats, and ensure compliance with ethical and regulatory standards, fostering trust and resilience in their AI systems.
Here is a comprehensive list of AI risks to consider for securing and managing AI systems effectively across their lifecycle:
Traditional GRC frameworks are becoming increasingly irrelevant in the face of AI's rapid evolution; organizations must adopt AI-specific governance strategies to effectively manage emerging risks and ensure compliance.
Balanced AI Risk Management (BARM)™ Approach
Given the complexity of AI systems and the diverse risks they present throughout their lifecycle, a comprehensive and adaptable risk management strategy is essential. The Balanced AI Risk Management approach outlined below addresses these challenges by integrating key elements from established frameworks with practical, actionable steps. This structured methodology not only promotes responsible AI development and deployment but also ensures that organizations can effectively navigate the evolving landscape of AI technologies. Importantly, this approach is aligned with the NIST AI Risk Management Framework (AI RMF), providing a robust foundation for managing AI risks while maintaining compliance with ethical and regulatory standards.
bottom of page