7 must-follow AI security best practices to govern your new attack surface

• Your teams adopt AI faster than you can secure it.
• Shadow AI creates cyber risk and attack paths that traditional cybersecurity tools can't see.
• AI-driven exposure management uncovers and governs AI use across your entire attack surface.
• Generative AI translates plain English into threat searches to decrease dwell time.

When employees use AI tools without telling you, or developers accidentally open up hidden attack paths across your cloud, or a simple AI prompt leaks sensitive data — that's your AI exposure management gap. It's a massive blind spot that leaves your security team in the dark about where AI is running and how it's all connected.

Meanwhile, those same security teams are under pressure to use AI security tools to keep up with the rapidly changing threat landscape.

When these issues combine, they leave your organization dealing with two challenges:

1. The risks AI introduces
2. The opportunity AI presents to strengthen your cyber defenses

See your entire AI attack surface and all its attack paths with Tenable One.

To find all your known and unknown AI assets, you need an AI security strategy that addresses the unique characteristics of AI systems. These seven best practices draw from real-world exposure management principles to help you secure AI implementation.

1. Use risk-aware context to justify security decisions

When selecting AI tools for your stack, prioritize those that offer clear decision-making transparency. You need to understand exactly what data the models rely on and how they score risk. 

If a tool cannot explain why it flagged a vulnerability or how it prioritized a remediation action, it introduces uncertainty into your AI cybersecurity posture. Demand full transparency on exposure reasoning and prioritization logic so you can build trust and justify your security decisions to leadership. 

The NIST AI Risk Management Framework emphasizes explainability as a core pillar of trust.

2. Enforce least-privilege access for AI agents

Developers often train AI models with broad, overprivileged permissions to ingest vast amounts of data. A critical mistake your organization can make is failing to revoke access after AI model deployment.

As best practice, enforce least privilege access protocols so that non-human identities and AI agents only keep elevated permissions for the exact moments they need them. Rigorously audit your service accounts and remove any standing access not strictly necessary for the AI model's runtime function.

3. Prevent identity-driven AI exposure

Cloud-hosted AI systems are notorious for generating overprivileged and unnoticed roles. You might grant a single AI agent read-write access to an S3 bucket and then forget about it, which creates a hidden attack path.

To tightly scope access, you must combine AI security posture management (AI-SPM) with cloud infrastructure entitlement management (CIEM). This integration helps detect overprivileged identities across your entire attack surface so that non-human identities don’t become the weak link in your chain.

Stop identity-driven AI exposure before it starts. See how with Tenable One.

4. Defend against adversarial AI attacks

AI models face threats that slip right past traditional firewalls, like prompt injection, jailbreaking, and data poisoning. 

Attackers use these methods to trick the models into revealing sensitive data or executing malicious instructions. 

To stay ahead of threat actors, implement policy-based guardrails specifically for LLM apps, like OWASP Top 10 for LLM Applications. These frameworks can help your teams better understand these distinct threats, and actively monitor for misuse to contain AI exposure the moment it appears.

5. Maintain a unified AI inventory

If your team uses open-source AI models or third-party libraries, your organization inherits their security posture. Because of that, you must validate the integrity of these external assets before they enter your environment.

Keep a unified inventory of all AI software, models, and services to ensure no asset operates outside your awareness. Track exactly where your AI models originate and if there are any known vulnerabilities. Address third-party AI components with the same scrutiny you apply to standard software.

6. Visualize attack paths to anticipate threats

Threat actors use generative AI to write convincing phishing campaigns and polymorphic malware. You cannot fight these machine-speed attacks with manual processes.

Use AI exposure management (security for AI) to outpace this curve. Instead of reacting to isolated alerts, leverage Tenable One for AI Exposure to get attack path insight. See how identity weaknesses and infrastructure flaws combine to expose your critical AI resources, so you can break the chain of exposure before an attacker exploits it.

7. Unify AI security within exposure management

Finally, avoid treating AI security as a silo. An isolated AI cybersecurity team often lacks the context to see how a compromised AI model affects the rest of your operations.

Eliminate fragmented security tools and data by consolidating AI security into a unified view. By correlating AI risks with your comprehensive exposure management, you empower your security team to see the big picture, like how assets, identities, and vulnerabilities connect, and prioritize the AI exposures that pose the greatest risk to your business.

For security leaders, rapid AI adoption and innovation create a dual challenge. You must close your AI exposure management gap to prevent data leakage and shadow IT, while simultaneously adopting AI tools to keep pace with the threat landscape.

You cannot solve this problem with isolated point tools. Tenable One correlates AI, infrastructure, agents, and data exposure into a unified view for prioritized remediation across your entire attack surface.

Ready to operationalize your AI security strategy? Request a demo of Tenable One.

Let's answer some of the most common questions out there right now regarding best practices when it comes to AI in cybersecurity, and its impact on the attack surface. 

What is AI-SPM?

AI-SPM is a security framework that discovers, prioritizes, and remediates risks within AI environments. Unlike traditional security tools, AI-SPM specifically targets unique AI vulnerabilities, like model misconfigurations, sensitive training data exposure, and excessive permissions that give access to non-human identities.

How do I secure generative AI models?

Securing generative AI requires a multi-layered approach beyond traditional firewalls. You must implement policy-based guardrails to prevent prompt injection and jailbreaking, enforce least privilege to limit access to sensitive data, and maintain a unified inventory of all third-party AI models and AI libraries to ensure no asset operates outside your awareness.

Why is identity security critical for AI?

AI models need non-human identities (like service accounts) to function. Overprivileged accounts create hidden attack paths that attackers can exploit to move laterally from a compromised agent to sensitive cloud infrastructure. Security teams should enforce least privilege to break these exposure chains.

Learn more about how to secure all your known and unknown AI assets in a single exposure management platform.