4 critical benefits of AI in cybersecurity

• 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.

While your organization rushes to adopt AI for a competitive edge, your security team struggles to manage new cyber risks they cannot see. This intersection is where you get an AI exposure management gap — a dangerous disconnect where an invisible attack surface creates hidden attack paths, potential data leaks from everyday interactions, and shadow AI risks that legacy tools can’t see or secure.

To close this security gap, you need a unified exposure management approach that treats AI in cybersecurity as both a tool and a unique attack surface to govern.

Unify all your security data, including AI assets and the cloud, in a single exposure management platform.

To understand how to close the AI exposure gap, you must first understand why it exists. The core issue here is more than the volume of AI tools across your enterprise. It’s also about where they live. 

On top of that, your organization likely doesn’t contain AI within a single lab or server. It’s everywhere, like your employee productivity tools, SaaS platforms, developer libraries, cloud services, and public-facing apps.

Dynamic AI assets create a fragmented and largely invisible attack surface that most security teams and traditional security tools can’t see and don’t know how to handle. When you can’t see shadow AI, you can’t govern it. This lack of visibility prevents you from enforcing your AI acceptable use policy (AI AUP) and leaves your organization vulnerable to data leakage, model theft, and unauthorized access.

To regain control, you must address the AI exposure management gap by moving beyond siloed security tools. To reap the full benefits of AI cybersecurity, you need a comprehensive AI security strategy that can discover and map hidden AI assets so you have visibility to secure them.

See how Tenable One can give you visibility into your AI attack surface.

The AI exposure management gap is widening every day. As your organization adopts more AI tools to drive business growth, your attack surface becomes more complex and less visible. You cannot solve this problem by throwing more people or siloed security tools at it. You must match security for AI with AI for security.

1. Get visibility into shadow AI and tools

The first step to closing the exposure gap is finding all your shadow AI. Employees across your organization are likely using unauthorized browser extensions to summarize confidential meetings, public generative AI chatbots to write proprietary code, and unvetted SaaS applications to analyze customer data. While well-intentioned, this creates blind spots where sensitive corporate data can leak into public models.

To address this, you need unified visibility across your entire attack surface. Exposure management allows you to perform continuous attack surface analysis and automatically discover installed AI software, browser plugins, and external-facing AI assets. 

By mapping these AI tools, you can see which users introduce risk, validate correct configurations for approved tools, and remove unauthorized applications before they are an entry point for attackers.

2. Secure your AI infrastructure with AI-SPM

You must also secure the foundation where your AI lives. Your development teams are likely rushing to build and deploy AI models in the cloud. They often bypass standard security processes, so your organization is vulnerable to misconfigurations.

To prevent possible breaches, you need to extend your cloud security strategy to include AI security posture management (AI-SPM). By integrating AI security insights into your broader cloud security program, you can automatically detect and fix vulnerabilities in your AI infrastructure. AI-SPM helps you secure end-user applications, critical AI training data, AI pipelines, and compute resources that power them.

3. Accelerate productivity with natural language search

As fast as your attack surface changes, attackers move faster, making it hard to stay ahead of attackers who are already using AI to accelerate their operations. 

Here, generative AI security is a force multiplier for your team. By acting as a translator between your analysts and your security data, gen AI removes technical barriers that slow down remediation and drives dwell time reduction.

Instead of writing complex queries or sifting through endless dashboards, you can leverage AI exposure capabilities to search your environment using natural language. Simply ask, "Show me all assets with CVE#," and get an immediate, prioritized list. This democratization of data speeds up decision-making, so your team can find and fix risks faster without writing specialized queries

4. Reduce noise to focus on critical risk

One of the biggest benefits of AI cybersecurity is the ability to silence alert noise that static, contextless vulnerability alerts create. Your security analysts are likely overwhelmed by thousands of these alerts every day, many of which are benign anomalies or low-risk misconfigurations. Traditional vulnerability detection tools often lack the context to distinguish between a critical flaw on a public-facing server and a minor bug on a disconnected test machine.

AI-driven exposure management solves this by correlating data across your IT, cloud, and identity environments to identify attack path analysis patterns that human analysts might miss. By analyzing asset criticality, threat intelligence, and exploitation likelihood in real-time, AI for security allows you to move beyond static vulnerability management. Instead of chasing false positives, your team can focus strictly on the top 1% of exposures that pose an immediate material risk to your business.

See how Tenable One can help reduce alert noise across your attack surface, including AI.

By adopting a unified exposure management platform like Tenable One for AI Exposure, you can regain control. You can find shadow AI, secure your critical AI models, use the power of generative AI security to outpace your adversaries and unlock the full benefits of AI cybersecurity.

Request a demo of Tenable One for AI Exposure.

There are a lot of questions when it comes to AI security. Let's take a look at some of the most pressing ones as it relates to the critical benefits of AI in cybersecurity.

What are the benefits of AI in cybersecurity?

AI in cybersecurity closes the AI exposure management gap by giving you the visibility to govern shadow AI and complex models. It accelerates productivity and decision-making by allowing your team to search for threats using natural language, slashing dwell time, and eliminating false positives.

What is the AI exposure management gap?

The AI exposure management gap represents three critical exposures: you do not know where AI exists (shadow AI), AI workloads create hidden attack paths across connected infrastructure, and every interaction with an AI tool can potentially expose sensitive data. This disconnect creates an invisible attack surface that traditional security tools cannot manage.

How does AI cybersecurity reduce false positives?

Legacy security tools often flood analysts with alerts for every minor anomaly. AI-driven exposure management reduces this noise by correlating data across your IT, cloud, and identity environments. It learns your specific baseline behavior to filter out benign events, delivering false positive reduction so you can focus only on confirmed, high-risk exposure paths.

什么是影子 AI？

Shadow AI represents unauthorized use of AI tools, like public generative AI chatbots, browser extensions, or unvetted SaaS apps. Because these tools often bypass standard security reviews, they create invisible entry points for attackers and data leakage risks.

Request a demo and close your AI exposure gap.