As artificial intelligence systems become integral to business processes, so too do the risks they introduce. The NIST AI Risk Management Framework (AI RMF) provides a comprehensive, actionable structure for managing AI-related risks. It defines 72 controls across four core functions—Govern, Map, Measure, and Manage—tailored for diverse stakeholders from security engineers to GRC professionals.

While the full framework is valuable, here are 10 high-priority controls that every security team should operationalize today. Each aligns with key risk domains and can be directly supported using PointGuard AI’s platform.

1. Govern 1.6 – AI System Inventory

What It Is
An AI system inventory is a comprehensive, structured record of an organization’s AI assets, including models, datasets, source code, documentation, responsible parties, and incident response plans. This inventory facilitates system governance, audit readiness, and rapid incident response. By prioritizing resources based on organizational risk profiles, it enables smarter allocation of security efforts and ensures visibility into AI deployments across departments and use cases.

How PointGuard AI Helps
PointGuard’s AI Discovery module auto-detects AI systems, models, and assets. It builds a living inventory, enriched with metadata like model owners, data sources, and change histories, allowing for comprehensive visibility and audit alignment.

2. Govern 4.3 – AI Testing & Incident Sharing

What It Is
This control emphasizes the institutionalization of AI testing and proactive incident identification. Organizations should develop formal testing protocols—including adversarial and red-team assessments—and share identified risks internally and, when appropriate, externally. This helps detect emerging threats like model drift, bias, and under specification before they escalate into harm.

How PointGuard AI Helps
PointGuard automates AI red teaming and testing, continuously evaluating model behavior under stress. Detected issues are logged, prioritized, and can be shared through incident reporting features, enhancing transparency and collaboration across teams.

3. Govern 6.1 – Third-Party Risk Management

What It Is
AI systems frequently integrate third-party models, APIs, datasets, and platforms. This control focuses on identifying risks from these external components—such as IP violations, privacy breaches, or unreliable software—and treating them with the same rigor as internal systems. Transparent documentation and testing are key.

How PointGuard AI Helps
PointGuard offers supply chain visibility across third-party AI components, flagging anomalies or risks in open-source models, datasets, and vendor libraries. It also provides compliance and integrity checks on third-party contributions.

4. Measure 2.4 – Runtime Monitoring of AI Behavior

What It Is
AI systems can evolve or degrade once deployed—a phenomenon known as drift. This control mandates continuous monitoring of AI system functionality in production. The goal is to detect misalignments between actual behavior and original design assumptions, enabling timely interventions to preserve safety, fairness, and reliability.

How PointGuard AI Helps
PointGuard’s Runtime Guardrails monitor model inputs and outputs to detect prompt injections, hallucinations, or unsafe recommendations in real time. These runtime defenses help mitigate production-stage vulnerabilities.

5. Measure 2.5 – Validity and Generalization

What It Is
Before deployment, organizations must validate that AI systems perform accurately and reliably on relevant tasks. They should also assess generalizability—how well models function on new, unforeseen data. Documenting limitations helps prevent over reliance and reduces the risk of applying models inappropriately.

How PointGuard AI Helps
PointGuard performs automated robustness assessments and validation checks during pre-deployment. Its continuous red teaming identifies edge cases and weaknesses, ensuring models are hardened against known failure modes before going live.

6. Measure 2.6 – AI Safety and Risk Tolerance

What It Is
This control ensures systems are regularly evaluated for safety—especially when used in unfamiliar or adversarial contexts. Residual risk should be documented and compared to the organization’s risk tolerance. Systems should fail safely when limits are exceeded, with metrics guiding intervention and redesign.

How PointGuard AI Helps
PointGuard conducts ongoing risk evaluations through model simulations and safety testing. Runtime Detection & Response features alert teams to violations of safety thresholds and enable graceful degradation when failures occur.

7. Manage 1.2 – Risk-Based Prioritization

What It Is
Not all AI risks are equal. Organizations must prioritize treatment of documented risks based on likelihood, potential impact, and available mitigation methods. This approach ensures scarce resources are focused on the most consequential risks and enables agile response planning across the AI lifecycle.

How PointGuard AI Helps
PointGuard uses business context and asset criticality to assign dynamic risk scores. It highlights high-severity findings and aligns remediation workflows with enterprise risk priorities—ensuring that the most urgent threats get addressed first.

8. Manage 1.3 – Response Planning

What It Is
This control calls for formal, documented response plans for prioritized AI risks. Responses may include mitigation, transfer (e.g., insurance), avoidance, or acceptance. Clear workflows, ticketing systems, and stakeholder notifications are essential for rapid action.

How PointGuard AI Helps
PointGuard orchestrates automated response workflows integrated with platforms like Jira, ServiceNow, Slack, and PagerDuty. It reduces alert fatigue, supports exception management, and ensures that critical issues trigger appropriate escalation paths.

9. Manage 3.1 – Third-Party Monitoring

What It Is
Even after deployment, organizations must monitor third-party components for ongoing risk exposure. This includes tracking changes in vendor software, data updates, and the emergence of new threats or vulnerabilities. All third-party AI resources should be documented and evaluated regularly.

How PointGuard AI Helps
PointGuard’s supply chain monitoring continuously evaluates third-party libraries and model dependencies for vulnerabilities, licensing issues, and operational degradation. It alerts teams to risks from updates or changes in external systems.

10. Manage 3.2 – Monitoring Pre-Trained Models

What It Is
Pre-trained models are often treated as black boxes, yet they may introduce significant security, fairness, or performance risks. This control ensures that pre-trained components are integrated into broader monitoring regimes, with performance, behavior, and residual risks tracked continuously.

How PointGuard AI Helps
PointGuard automatically catalogs pre-trained models and subjects them to ongoing security and quality assessments. It flags unexpected behaviors or dependencies, enabling teams to enforce governance policies even on inherited model components.

Conclusion

The NIST AI RMF provides a clear, flexible roadmap to navigate AI risks—but implementation can be daunting. PointGuard AI simplifies the process by automating core controls across discovery, inventory, testing, monitoring, and risk response. Whether you’re building, deploying, or governing AI systems, aligning with these 10 controls can help your organization embed security and trust from the ground up.