As artificial intelligence becomes embedded across business operations, many organizations are reaching the same conclusion: understanding AI risk is only the first step. The real challenge is operationalizing that understanding into controls, governance, and repeatable practices that reduce exposure over time.

As discussed in Carson & SAINT’s Webinar “Including AI Risks as Part of Your Business Risk Analysis”, AI risk management must extend beyond assessment into mitigation, prioritization, and continuous monitoring. Organizations that fail to take this step risk allowing AI-enabled systems to operate outside traditional security and risk frameworks—often without visibility or accountability.

The following AI risk management best practices provide a practical roadmap for organizations looking to move from awareness to action.

1. Training and Testing to Close Skills and Knowledge Gaps

Firstly, AI-driven threats often exploit human behavior, particularly through social engineering and phishing. Training helps employees recognize these threats, but training alone is not enough.

Testing—such as simulated phishing exercises—validates whether training is effective and reinforces good decision-making. Continuous training and testing ensure employees remain vigilant as AI-generated attacks become more convincing and frequent.

2. Asset Management for AI Tools, Agents, Models, and Data

Organizations cannot manage AI risk without knowing where AI exists in the environment. So, asset management must expand to include:

• AI tools and platforms

• Autonomous agents and models

• Training data and output data

This visibility is foundational for any effective Cyber Risk Management program, as unmanaged AI assets quickly become blind spots.

3. Policies, Procedures, and Rules of Behavior

Many organizations still lack formal guidance on AI adoption and use. Clear policies should define:

• How AI can be adopted and deployed

• What data AI systems can access or generate

• How AI outputs may be shared internally and externally

So, these rules must be established collaboratively across security, legal, compliance, and business stakeholders to ensure consistent enforcement.

4. Vulnerability Scanning, Patching, and Configuration Benchmarks

Traditional vulnerability management practices must evolve to include AI technologies. This includes scanning and monitoring:

• AI platforms and APIs

• Encryption keys and secrets

• Network traffic anomalies tied to AI workflows

• Access control lists (ACLs) and MFA enforcement

Modern Vulnerability Risk Management (VRM) approaches help prioritize these findings based on business impact and exploitability—not just severity scores.

5. Remediation Scripts and Compensating Controls

Not all AI-related vulnerabilities can be patched immediately. In these cases, vendors may provide remediation scripts or guidance that act as compensating controls.

These measures—sometimes referred to as “virtual patches”—can isolate vulnerable components, restrict access, or reduce attack surface until a permanent fix becomes available.

6. Threat Intelligence Focused on AI Products

Threat intelligence must now account for how adversaries use AI to create new attack techniques. This includes tracking:

• AI-specific vulnerabilities

• Emerging exploitation methods

• Sector-specific targeting trends

Integrating AI-focused threat intelligence improves an organization’s ability to anticipate risk rather than react to it.

7. Logging, Auditing, and Advanced Analysis

AI-driven attacks generate complex signals that traditional logging tools may not detect. Organizations should ensure that logs capture AI-related activity and that analysis tools can process large volumes of data efficiently.

Luckily, modern analytics platforms provide the visibility needed to detect subtle, AI-enabled behaviors that older tools may miss.

8. Penetration Testing and Red Teaming

Penetration testing and red teaming must evolve alongside AI. Testing programs should simulate:

• AI-enabled phishing and social engineering

• Abuse of AI agents and automation

• Attacks that blend human and machine behavior

This approach helps organizations understand how well their defenses hold up against modern adversaries.

9. Testing Out-of-Range Behavior, Not Just What Passes

Too many assessments focus on whether a control passes a test. AI risk management requires looking beyond pass/fail outcomes to identify anomalies and out-of-range behavior.

For example, it is not enough to confirm that a transaction processes successfully. Organizations must also examine abnormal values, unexpected frequencies, and unusual patterns that could signal exploitation or misuse.

10. Data Loss Prevention and Data Leak Protection

AI systems introduce new data exposure risks—not only through training data, but also through generated outputs. Organizations must evaluate:

• How AI systems protect sensitive inputs

• How outputs are stored, shared, or correlated

• Whether AI-generated data can be leaked or misused

So, DLP strategies must adapt to account for AI-driven data creation and transformation.

11. Zero Trust for AI Environments

Zero Trust principles apply directly to AI systems. Rather than assuming trust, organizations should:

• Verify every request

• Limit session duration and access scope

• Enforce strong authentication and authorization

As a result, this approach reduces blast radius if AI systems or credentials are compromised.

12. Third-Party Vendor Risk Management

Third-party risk has always mattered—but AI raises the stakes. Organizations must understand:

• How vendors use AI internally

• What AI capabilities are embedded in third-party tools

• How vendor AI systems connect to internal environments

Ultimately, vendor risk assessments should explicitly address AI practices and dependencies.

13. Change Management for AI Enablement

A system that did not use AI six months ago may be AI-enabled today. Change management processes must account for this shift.

Also, any introduction of AI functionality should trigger a reassessment of risk exposure, data handling, and security controls—even if the underlying software appears unchanged.

14. DevSecOps and Security by Design

AI-powered code generation introduces new risks into development pipelines. Organizations should ensure that:

• AI-generated code follows secure coding practices

• Security testing occurs throughout development

• Vulnerabilities introduced by AI tools are identified early

Consequently, security by design is no longer optional—it is critical in AI-driven development environments.

15. Cyber Insurance Alignment

Lastly, Cyber insurance providers are becoming more sophisticated in how they evaluate AI risk. Policies increasingly require evidence of:

• Governance and policies

• Technical controls

• Data protection measures

Organizations that fail to implement these best practices may find their coverage limited—or unenforceable—when they need it most.

Operationalizing AI Risk Management

In conclusion, AI risk management is not a single control or checklist. It is a continuous discipline that spans governance, technology, people, and process.

Organizations that adopt these best practices position themselves to use AI responsibly—without sacrificing security, resilience, or trust. Those that do not risk allowing AI-driven exposure to grow unchecked, often outside their line of sight.

Carson & SAINT helps organizations operationalize AI risk management through integrated Cyber Risk Management and Vulnerability Risk Management programs—so innovation can move forward without losing control. Contact us here to get started.