What do leaders most often get wrong about AI security risk?

Leaders commonly misframe AI security as a tooling problem focused on prompts and filters, rather than a sociotechnical risk that spans data provenance, model lifecycle governance, and integration boundaries. The NIST AI Risk Management Framework emphasizes system-level governance and assurance across people, processes, and technology. Practical implications include securing ML supply chains, enforcing identity and plugin controls, and demanding model and data cards from providers. This systemic approach aligns with guidance from ENISA and OWASP’s LLM Top 10.

Which frameworks should enterprises prioritize to structure AI security governance?

Enterprises should start with the NIST AI Risk Management Framework for sociotechnical governance, then layer ENISA’s AI Threat Landscape for adversary perspectives, OWASP’s Top 10 for LLM Applications for application vulnerabilities, and MITRE ATLAS to emulate attacker tactics. Together, these frameworks provide a coherent map from risk identification to control implementation. Boards should require control mappings and assurance artifacts to demonstrate ongoing compliance and operational resilience.

How can companies operationalize assurance beyond detection for AI systems?

Operationalize assurance by institutionalizing pre-deployment tests, continuous red-teaming, and rollback plans tied to risk thresholds. Adopt model cards and data cards to document provenance and use secure artifact signing to ensure integrity. Reference OpenAI’s Preparedness Framework and Anthropic’s Responsible Scaling Policy as patterns for pre-deployment testing and pause conditions. Embed these practices into MLOps pipelines on platforms like Microsoft Azure, AWS, and Google Cloud, and verify against MITRE ATLAS scenarios.

What are the key attack vectors unique to AI workloads?

Prompt injection, data poisoning, model extraction, insecure plugin integrations, and output manipulation are prominent AI-specific attack vectors. OWASP’s Top 10 for LLM Applications details these classes, while MITRE ATLAS catalogs adversary techniques against ML systems. ENISA’s report further highlights orchestration risks and supply chain exposure. Organizations should secure identity boundaries, isolate tools and connectors, validate training data provenance, and continuously evaluate model behavior under adversarial conditions.

How do regulations like the EU AI Act change enterprise priorities?

The EU AI Act introduces risk-tiered obligations, documentation requirements, and conformity assessments that push enterprises toward assurance and traceability, not just detection. U.S. policy guidance via the Executive Order on AI similarly emphasizes secure development, reporting mechanisms, and risk management. Boards should invest in governance artifacts—model and data cards, audit trails, and control mappings—while negotiating shared-responsibility models with cloud providers to cover plugin ecosystems, output governance, and lifecycle security.

What Leaders Misunderstand About AI Security Risk and ML Supply Chains

Most boardrooms still treat AI security as a narrow tooling problem. The real risk lives in data provenance, model lifecycle governance, and the sociotechnical systems around AI. Executives who reframe security from detection to assurance will build durable moats and negotiate better with hyperscalers.

Published: January 16, 2026 By Dr. Emily Watson Category: AI Security

What Leaders Misunderstand About AI Security Risk and ML Supply Chains

Executive Summary

AI security risk sits in data supply chains and model lifecycle governance, not just tooling, as emphasized by the NIST AI Risk Management Framework.
Attack surfaces include prompt injection, data poisoning, model theft, and insecure integrations, codified in the OWASP Top 10 for LLM Applications and MITRE ATLAS.
Boards should demand assurance evidence such as pre-deployment tests and traceable provenance, aligning with OpenAI’s Preparedness Framework and Anthropic’s Responsible Scaling Policy.
Regulatory pressure from the EU AI Act and U.S. policy guidance via the Executive Order on AI elevates governance to a strategic imperative.
Capital should shift toward data quality, provenance, and model assurance, supported by industry frameworks from NIST and ENISA.

Leaders Misframe AI Security as a Tool Problem, Not a System Risk Most leadership teams still approach AI security as a tooling purchase—red-teaming and input filtering—rather than a system-level risk discipline covering data, models, and the integrations that bind them. The NIST AI Risk Management Framework is explicit that AI risk is sociotechnical, spanning people, processes, and technology. That means threat modeling must extend beyond model prompts to the entire ML pipeline, third-party connectors, and identity boundaries that are often overlooked.

Vulnerabilities are multifaceted: prompt injection, data poisoning, model theft, insecure plugin integrations, and output misuse. These are documented across the OWASP Top 10 for LLM Applications and mapped to attacker behavior via MITRE ATLAS, which catalogues adversary tactics against ML systems. According to Satya Nadella, CEO of Microsoft, "Safety and security are foundational to how we build and deploy AI" (company blog...

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