Table of Contents

• 1. Introduction

• 2. Why This Matters: The Importance of NIST’s Cont …

• 3. NIST’s Targeted AI Use Cases in COSAIS

• 4. ContinuousOS: Operationalizing NIST Overlays in …

  • 4.1. Generative AI (LLMs with RAG)

  • 4.2. Predictive AI (AI-Driven Decisioning)

  • 4.3. AI Agent Systems (Single & Multi-Agent)

  • 4.4. AI Developer Security Practices

• 5. Beyond Compliance: ContinuousOS as a Strategic …

• 6. Call to Action

1. Introduction

NIST is drafting new overlays to secure AI systems—from generative assistants to predictive models and agentic AI. While these frameworks are still in development, ContinuousOS is already implementing secure AI practices aligned with these emerging standards in real-world GxP environments.

2. Why This Matters: The Importance of NIST’s Control Overlays (COSAIS)

NIST’s Control Overlays for Securing AI Systems (COSAIS) represent a critical step in defining the security and trust framework for AI technologies in regulated industries. These overlays extend the established SP 800-53 security controls, tailoring them for AI’s unique cybersecurity challenges.

Key focus areas include:

• Continuous monitoring of AI system components and environments

• Ensuring the confidentiality, integrity, and availability of AI models, training data, and outputs

• Implementing robust agent guardrails for autonomous AI systems

• Embedding human-in-the-loop (HITL) oversight to maintain control and accountability

• Addressing unique AI attack surfaces, such as adversarial inputs and misuse risks

For life sciences and other regulated sectors, COSAIS will directly influence validation processes, audit readiness, and compliance with GxP standards—a foundation for safe, reliable, responsible AI adoption.

3. NIST’s Targeted AI Use Cases in COSAIS

NIST’s initial overlay development focuses on five core categories of AI adoption, each with tailored cybersecurity controls:

1. Generative AI (Large Language Models with Retrieval-Augmented Generation - RAG): 
   Addressing data integrity, secure information retrieval, and the need for human oversight on generated outputs.

2. Predictive AI (AI Models for Decisioning): 
   Managing lifecycle risks through governance of training data, model updates, and abuse prevention.

3. AI Agent Systems (Single and Multi-Agent): 
   Ensuring proper permissioning, secure communication, and control over autonomous agent actions.

4. Security Controls for AI Developers: 
   Implementing secure software development lifecycle (SDLC) practices to protect model artifacts and mitigate misuse risks.

5. AI System Maintenance and Output Protection: 
   Safeguarding configuration, deployment infrastructures, and AI-generated outputs under rigorous cybersecurity controls.

Each use case will apply SP 800-53 controls, informed by NIST’s adversarial machine learning research and risk frameworks.

4. ContinuousOS: Operationalizing NIST Overlays in GxP Environments

While NIST’s COSAIS overlays are formalizing standards, ContinuousOS is translating these principles into practical solutions, delivering measurable benefits for regulated AI use. The platform’s alignment with NIST’s use cases demonstrates how theory meets practice:

4.1. Generative AI (LLMs with RAG)

• What NIST Requires: Data integrity, auditability of retrieval and generation, HITL control

• What ContinuousOS Delivers: ContinuousOS provides a hybrid AI architecture (generalist + specialist + QA-as-LLM + HITL) that ensures every AI-driven decision is accurate, traceable, and audit-ready. This approach combines the broad capabilities of generalist models with the precision of domain-specific specialists, while QA-as-LLM offers automated validation and HITL oversight to ensure critical decisions receive appropriate scrutiny. The result is a robust system that maintains flexibility for diverse GxP applications while meeting strict security and compliance requirements.

4.2. Predictive AI (AI-Driven Decisioning)

• What NIST Requires: Governance of data and updates, protection against misuse

• What ContinuousOS Delivers: ContinuousOS offers continuous validation pipelines that govern datasets, updates, and risk-based approvals—reducing validation time from weeks to under 24 hours. The platform's intelligent governance framework automatically manages data quality, tracks model performance, and implements risk-based decision-making to ensure predictive AI systems remain accurate, secure, and compliant throughout their lifecycle. This approach eliminates traditional bottlenecks in pharmaceutical validation processes while maintaining the rigorous oversight that is required.

4.3. AI Agent Systems (Single & Multi-Agent)

• What NIST Requires: Permissioning, secure communication protocols, autonomy guardrails

• What ContinuousOS Delivers: Modular GxP agents automate tasks like test generation, monitoring, and evidence packaging—always scoped, permissioned, and audit-logged. Each agent operates within defined boundaries, maintains comprehensive security protocols, and incorporates HITL oversight for critical decision points. This ensures that automation enhances human capability rather than replacing human judgment, particularly in scenarios where regulatory compliance and patient safety are paramount. The modular design allows organizations to deploy agents incrementally while maintaining control and visibility over autonomous operations.

4.4. AI Developer Security Practices

• What NIST Requires: Secure SDLC aligned with SSDF controls and misuse mitigation

• What ContinuousOS Delivers: Compliance-by-design automation reduces documentation burdens by up to 95% while producing secure, regulator-ready evidence packages. The platform incorporates governance as a fundamental component of xLM's SDLC process, ensuring security considerations are embedded at every development stage. This integrated approach results in more secure systems, dramatically reduced compliance costs, and faster time-to-market for critical pharmaceutical applications while maintaining high standards of regulatory readiness.

5. Beyond Compliance: ContinuousOS as a Strategic Advantage

NIST overlays define secure AI best practices. ContinuousOS enhances this by transforming compliance into a productivity engine—cutting cycle times, reducing human error, and freeing talent for innovation. Compliance is no longer a cost center; it's a strategic advantage. Organizations implementing ContinuousOS find that regulatory requirements become catalysts for operational excellence rather than obstacles. The platform's ability to maintain continuous audit readiness while improving efficiency represents a fundamental shift in how pharmaceutical companies approach regulatory compliance, enabling them to move faster, innovate more effectively, and respond to market opportunities with unprecedented agility.

By automating validation, governance, and audit readiness, ContinuousOS:

• Cuts cycle times dramatically

• Reduces the risk of human error

• Frees valuable talent to focus on innovation and higher-value activities

ContinuousOS empowers organizations to integrate secure, trusted AI deeply into their operations, turning compliance into a competitive edge rather than a bottleneck.

6. Call to Action

NIST overlays define secure AI best practices. ContinuousOS enhances this by transforming compliance into a productivity engine—cutting cycle times, reducing human error, and freeing talent for innovation. Compliance is no longer a cost center; it's a strategic advantage. Organizations implementing ContinuousOS discover that regulatory requirements become catalysts for operational excellence rather than obstacles. The platform's ability to maintain continuous audit readiness while improving efficiency represents a fundamental shift in how pharmaceutical companies approach regulatory compliance, enabling them to move faster, innovate more effectively, and respond to market opportunities with unprecedented agility.