Here is the Executive Summary — please fill out the contact form linked below for the complete whitepaper.
1. Executive Summary
This white paper addresses the critical need for extending robust DevSecOps practices from traditional software applications to the rapidly evolving domain of Artificial Intelligence (AI) models. To be effective in today’s threat landscape, this extension must be founded on Zero Trust Architecture (ZTA) principles and runtime Continuous Threat Exposure Management (CTEM)—not merely on static inventories or compliance checklists. It outlines a strategic approach for establishing a parallel AI Model DevSecOps pipeline, building upon an existing automated application DevSecOps framework powered by ProCap360™. Our approach mandates that no entity, process, or model component is trusted by default. Every access request—whether to source code, model weights, training data, or inference endpoints—is dynamically verified against real-time policy and risk context. The core challenge lies in managing the unique risks and lifecycle characteristics of AI systems while maintaining a cohesive security posture across the enterprise.
Security is not a one-time event. Our unified framework integrates runtime Continuous Threat Exposure Management (CTEM) to provide:
● Real-time attack surface discovery for both applications and AI models,
● Continuous monitoring for anomalous behaviors, privilege escalations, and policy violations,
● Automated response to detected threats, including isolation, rollback, and forensic capture.
Key contributions of this paper include the definition of an AI Bill of Materials (AIBOM) as a comprehensive manifest for AI models, detailing their components, data lineage, and operational parameters. Furthermore, it emphasizes the integration of leading global AI security standards from bodies such as NIST, CISA, OWASP, and industry leaders like Google, Microsoft, and IBM, into the requirements of the AI Model DevSecOps pipeline. A novel AI Model Security Assurance Score, derived from a proposed Model Class Profile (MCP) framework, is introduced to provide a quantifiable measure of an AI model’s security posture. This score encompasses critical facets such as AI model data provenance and classification, configuration security, user data access levels, AI agent data access classification, and overall model pedigree. The paper introduces an AI Bill of Materials (AIBOM) and a Model
Security Assurance Score, but these are just the first steps needed by the broader industry as they must be dynamically updated and validated at runtime. With the rampant growth and adoption of Generative AI, provenance, configuration, and access levels need to be continuously enforced and monitored by ZTA-driven controls, not just statically documented.
The ultimate vision presented is a unified operational assurance capability. This framework aims to provide real-time visibility and control over both application and AI model lifecycles, ensuring security, compliance, and trustworthiness in an increasingly AI-driven landscape. The dual-pipeline approach is not merely an incremental improvement but a necessary evolution. Traditional application security pipelines are often ill-equipped to handle the distinct risk categories introduced by AI models, such as data-dependent vulnerabilities, model integrity issues, and complex ethical
considerations. AI models, frequently perceived as “black boxes,” can obscure internal workings and data dependencies, making specialized DevSecOps treatment essential.
Organizations that fail to adapt their security practices to this new paradigm by implementing a unified yet specialized strategy will likely encounter significant blind spots in their risk posture. Such oversight can lead to severe consequences, including compliance failures, reputational harm, and compromised decision-making stemming from insecure or unreliable AI systems.
Our vision is a unified, adaptive operational assurance platform that:
● Enforces ZTA at every pipeline stage (development, training, deployment, inference, and retirement),
● Implements runtime CTEM for both applications and AI models,
● Delivers continuous, automated policy enforcement and rapid incident response,
● Integrates with threat intelligence and behavioral analytics to proactively manage emerging risks.
Organizations that do not adopt a ZTA- and CTEM-based approach will face:
● Unchecked lateral movement and privilege escalation across pipelines, Blind spots to runtime threats and insider attacks,
● Regulatory non-compliance with emerging mandates for zero trust and continuous risk management,
● Increased likelihood of catastrophic breaches involving sensitive AI models and data leakage.
Continuous monitoring and automated response are now integral, enabling the enterprise to detect, analyze, and mitigate threats as they emerge—whether from supply chain risks, insider threats, or novel AI attack vectors.