What is AI cybersecurity?

AI in cybersecurity is the convergence of AI for security, like using machine learning to accelerate exposure management and facilitate remediation, and security for AI to safeguard AI models, like employee use of generative AI tools, to stop data leakage, poisoning, and theft.

Key takeaways:

• Automate threat detection at scale with machine learning, and harden your own AI models so attackers can't manipulate them.
• Protect your organization from AI-specific risks, like rogue AI models, prompt injection, and unsafe data handling, with dedicated AI security posture management (AI-SPM).
• Analyze behavior patterns instead of known signatures to catch emerging cyber threats and sophisticated AI attacks that bypass legacy cybersecurity tools.
• Tenable One tells you what to fix first by using AI-driven analytics to prioritize critical exposures across your entire surface, including your IT assets, cloud resources, and AI models.

At its core, artificial intelligence cybersecurity relies on three layers of technology:

• Machine learning: Uses statistical models to classify data and detect pattern deviations across network traffic and your organization’s AI model outputs.
• Deep learning: A more advanced subset of machine learning that uses multi-layered neural networks to solve complex problems, such as recognizing zero-day threats or identifying deepfakes and AI model manipulation.
• Natural language processing (NLP): Enables systems to interpret human language to detect sensitive data in employee AI prompts and helps analysts query data using plain English.

Unlike legacy security tools that rely on static signatures, AI uses statistical analysis to parse unstructured data. AI can spot traditional cyber risks and specialized threats targeting your AI models that would typically take security teams weeks to find.

To unite detection, remediation, and governance, AI operates across three functional stages:

• Predictive AI: Prioritizes high-risk incidents by flagging deviations in user activity and AI model interactions.
• Generative AI: Accelerates investigations by explaining risks in plain language, while simultaneously monitoring employee AI prompts for unsafe data handling.
• Agentic AI: Takes autonomous action to complete sophisticated tasks, block threats, and enforce AI acceptable use policy (AUP).

This multi-layered approach helps your organization leverage AI to defend your entire attack surface while simultaneously securing AI models to drive innovation.

Ready to securely innovate with AI? Discover how Tenable One helps you leverage generative AI for faster analysis without compromising the security of your AI use.

At a high level, AI works by learning the unique DNA of your environment, like what normal network, endpoint, user behavior, and employee AI use looks like, and then flagging or acting on dangerous deviations.

1. Behavioral baselines 

Instead of relying on fixed rules, AI models train on large datasets to establish a baseline of normal activity. The system learns standard patterns, such as typical login times or data transfer volumes, or API calls to external AI models, specific to your organization.

2. Deviation and threat detection 

The system monitors for deviations from that baseline to detect anomalies that don't match known signatures. AI can identify new malware variants, prompt injection attacks, zero-day attacks, or phishing attempts that traditional cybersecurity tools would miss.

3. Complex pattern recognition 

Using deep learning, the system identifies subtle, complex relationships between seemingly unrelated events. It can correlate a minor endpoint alert with a suspicious network request to reveal a sophisticated attack path that might be invisible to human analysts.

4. Predictive prioritization 

In vulnerability management, AI moves beyond static vulnerability severity scores. Predictive Prioritization analyzes asset criticality, real-world threat intelligence, and exploit probability to predict which vulnerabilities attackers are most likely to exploit to focus remediation on exposures that pose the most significant actual risk.

5. Operational assistance 

Through NLP, you can interact with your data using natural language, like asking a security assistant to summarize complex logs, generate risk reports, or explain alerts to reduce manual burden on your team.

6. Continuous adaptation 

Unlike static detection tools, AI continuously adapts to your changing attack surface. It learns from false positives and new data to become more accurate over time as it integrates with your exposure data and threat intelligence.

The result? AI helps you ask better questions and surface high-risk conditions sooner to reduce alert fatigue and filter out the noise so your team can focus on critical exposures.

Want proactive security? See how Tenable’s AI Assistant uses LLMs to search your data, summarize risks, and generate mitigation guidelines in seconds.

By automating complex analysis, AI is a force multiplier that allows security teams to scale their defense without scaling headcount.

Primary advantages of AI-driven cybersecurity:

• Enhanced productivity by automating routine triage and data correlation to reduce manual investigation time.
• Precision prioritization using behavioral models that filter out alert noise and false positives, so your teams focus only on critical risks that threaten business operations.
• Generative AI translates complex technical findings into clear, actionable steps for IT to speed up the time between discovery and fix.
• Unlike legacy cybersecurity tools, AI identifies novel and zero-day attacks based on behavioral deviations, covering both traditional cyber threats and risks targeting your AI pipeline.
• Secure AI innovation by providing guardrails to safely adopt AI technologies, so employees can use authorized AI tools without leaking sensitive data.

Explore the full value proposition of AI in security inside our detailed guide about the benefits of AI-powered cybersecurity.

Here’s how AI enhances the cybersecurity tools you already use:

Take a deeper dive into detailed real-world AI use scenarios in our complete guide to common AI cybersecurity use cases.

Generative AI helps your analysts query complex environments using plain English. Instead of manually filtering dashboards, to get fast and context-rich answers, teams can ask questions like: "Can you show me all unapproved AI models in use?"

Generative AI has two outcomes:

• Accelerated triage
• Tools like Tenable ExposureAI use generative AI to automatically find attack paths, explain risks, audit AI use, and generate actionable remediation evidence.
• Counter-adversarial speed
• Generative AI accelerates response by instantly decoding complex behaviors to quickly neutralize sophisticated threats like polymorphic malware and automated social engineering.

Want to take a deeper dive? Check out this generative AI page in our cybersecurity guide.

The same qualities that make AI powerful also introduce new and often overlooked cyber risks: 

1. AI risk in cloud environments - Cloud AI deployments introduce unique risks like shadow AI and exposed endpoints that traditional security tools miss. Learn more about securing these assets in our guide to AI risk in cloud environments.
2. Model bias and training flaws - AI models trained on incomplete data can have inaccurate outcomes and lead to missed threats or false positives.
3. Lack of explainability (black box AI) - Without transparency into how AI models function, you can't explain to executives or your auditors why the system suspended an account or tell compliance why the model dismissed a critical alert.
4. Data privacy and leakage - If you don’t use proper segmentation, LLMs can accidentally expose sensitive training data.
5. Prompt injection attacks - Adversaries can create specific inputs to trick AI models into revealing internal logic or bypassing safety filters.
6. Overreliance on automation - Automating decisions without human validation risks missing nuance and context.
7. Weaponization by attackers - Threat actors use AI to accelerate phishing, create deepfakes, and write polymorphic malware.
8. Foreign-hosted and untrusted AI models - Using low-cost, foreign-hosted models (like DeepSeek) may bypass Western privacy standards and safety guardrails.

Want to explore these challenges more deeply? See how AI is reshaping the cybersecurity threat landscape and its implications for your security program.

Adopting AI introduces distinct risk vectors, from data poisoning to model theft. To safely deploy AI systems, your security teams must adapt governance for unpredictable AI behavior and machine-to-machine authentication.

Core strategies:

1. Demand transparency - Select tools with explainability features to better understand how AI models score risk and justify decisions.
2. Enforce least privilege - Prevent standing access for AI models. Use just-in-time (JIT) protocols to limit permissions to grant access on demand and revoke it when no longer needed.
3. Control identity sprawl - Integrate AI security posture management (AI-SPM) with cloud infrastructure entitlement management (CIEM) to prevent privilege creep in cloud environments.
4. Harden AI model inputs - Block prompt injection attacks and adversarial inputs with rigorous validation processes.
5. Vet your supply chain - Audit third-party datasets and open-source models to find integrity issues and known vulnerabilities.
6. Counter adversarial AI - Deploy defensive AI to match the speed and sophistication of attackers using generative AI tools for phishing and malware.
7. Operationalize insights - Move beyond detection by using AI to automate prioritization and explain remediation steps to IT teams.
8. Define AI acceptable use - Establish and enforce clear policies detailing which AI tools employees can use and which data classifications are safe to share so that sensitive IP never enters public models.

Want to know more? Read the AI cybersecurity best practices guide for more details.

When internally deploying AI models or integrating AI into your security tools, you should ensure your AUP aligns with your organization’s compliance requirements.

Start by embedding governance into your AI lifecycle:

• Document each AI model’s intended use, training data sources, and known limitations.
• Conduct regular risk assessments to evaluate AI model misuse potential or exposure to sensitive data.
• Track access to AI models and pipelines, especially in cloud environments.
• Align your controls to trusted frameworks like NIST AI Risk Management Framework (AI RMF) and ISO/IEC 42001.

Tools like ExposureAI support AI compliance by:

• Generating evidence for decisions, prioritization and risk remediation.
• Logging natural-language queries and recommendations for audit trails.
• Highlighting compliance-relevant exposures across cloud and hybrid environments.

AI governance is critical to building trust with internal stakeholders, regulators and customers. And, with the rise of AI-SPM, you now have the tools to secure your AI models just like any other critical workload.

The Tenable One Exposure Management Platform unifies two distinct AI capabilities to cover AI for security and security for AI.

But AI only works if it is grounded in your environment. That is why the Tenable Data Fabric, the industry's largest repository of exposure data, powers the Tenable One platform. 

Tenable Data Fabric has more than one trillion data points from millions of sensors across endpoint, cloud, OT, and identity. This massive dataset fuels ExposureAI. A specialized team of AI researchers and data scientists, who have more than 40 patents in machine learning and algorithms, developed this engine to turn raw data into decision intelligence.

This combination of superior data and expert engineering powers two key outcomes:

1. ExposureAI drives your core exposure management operations. It uses generative AI and predictive insights to help you find high-risk assets using natural language queries, explain complex attack paths, and prioritize remediation based on real-world exploitability.
2. Tenable AI Exposure helps you secure your organization’s use of generative AI tools. It gives you visibility into how your teams use AI platforms like ChatGPT Enterprise or Microsoft Copilot, so you can effectively govern AI use, detect data leaks, and mitigate prompt injection risks.

Ultimately, Tenable One helps you harness the power of AI to scale your exposure management program, without compromising the security of AI innovation in your daily workflows. The platform filters out alert noise and identifies risky AI usage so your security teams can focus on finding and fixing critical exposures, whether in your network, the cloud, identity, or your AI models.

Explore how Tenable One combines the world's largest exposure dataset with expert-led AI to help you eliminate security blind spots.

AI security is a growing and fast-evolving practice, and given the nature of its velocity and emergence, there are many questions, and every day creates scenarios that lead to even more. Let’s answer some of the most common, and impactful questions.

What is AI in cybersecurity?

AI in cybersecurity is the convergence of protecting your AI pipeline (data and models) and applying machine learning to automate exposure management, threat detection, and response.

How are predictive AI and generative AI different in cybersecurity?

Predictive AI, often called predictive analytics in cybersecurity, looks for patterns in data to forecast potential threats or malicious behaviors. Generative AI creates content, such as summaries, queries or responses, to better respond to risk.

Is AI replacing human analysts?

No. AI supports analysts by accelerating cyber threat detection, summarizing data, and mitigating alert fatigue. Even agentic AI, which autonomously manages multi-step tasks, is an extension of human effort. AI still needs strategic oversight and final validation. Ultimately, AI helps you scale, but analysts are essential for contextual judgment, escalating decisions, and navigating complex scenarios that need human expertise.

What’s DeepSeek and why does it matter in cybersecurity?

DeepSeek is an LLM built in China. It’s important because it signals a growing landscape of high-performance, open-access LLMs that attackers may use, which changes how you need to think about AI threat models and AI abuse.

What is AI-SPM?

AI-SPM monitors and secures AI systems in cloud environments, including models, pipelines, and data. It builds on cloud security posture management (CSPM) with capabilities tailored to AI-specific risks.

What are the risks of using AI in compliance-heavy industries like healthcare or finance?

AI tools that touch sensitive data, like diagnostic models or fraud detection engines, can create exposure if not properly secured. It’s essential to monitor data flow, access rights and model inputs/outputs. Using AI-SPM and ExposureAI helps ensure your AI models meet compliance requirements like HIPAA or PCI DSS while minimizing risk to customer data.

Want to see how AI fits into your cybersecurity program? Explore Tenable One now.