10 common AI cybersecurity use cases and examples

• AI accelerates remediation by prioritizing exposures based on risk-aware context, so your security teams focus on the exposures that matter most rather than isolated findings.
• Behavioral analytics improve threat detection by identifying risky behavior and anomalies, such as insider threats or overprivileged non-human identities and AI agents.
• A unified exposure management platform bridges visibility gaps by using AI to visualize complex attack paths across hybrid environments, correlating infrastructure, identity, and data into a single view.

Your security teams battle cyber risk and potential threat actors on multiple fronts every day. They secure cloud containers, protect critical OT infrastructure, manage user identities, and maintain traditional on-prem assets, all while trying to govern the sudden rise of unauthorized AI tools. 

This intersection is where traditional cybersecurity tools fail to connect the dots between security silos, and that leaves your teams drowning in alerts without clear remediation objectives.

AI cybersecurity bridges this gap by analyzing vast amounts of data across your entire attack surface to surface the risks that truly matter. Whether it is spotting an identity-based attack moving laterally to OT systems or flagging a cloud misconfiguration before attackers can exploit it, AI gives your teams speed and context to reduce critical cyber exposures. 

Explore these real-world AI cybersecurity use cases to see how AI enhances security across every domain:

1. 漏洞管理

• Vulnerability management uses AI to accelerate the identification and prioritization of fixable security flaws. Generative AI (GenAI) capabilities can then recommend or initiate remediation actions based on your security policies and best practices, accelerating time to fix.
• Industry example: A software development company could use AI to pinpoint which specific vulnerabilities in its code attackers are most likely to exploit based on real-world attack data. AI can immediately remediate those issues based on established AI governance and best practices.

2. 风险暴露管理

• Exposure management uses AI to continuously map and understand your complete digital footprint, critical assets, attack paths, threat intelligence, and business criticality. By instantly analyzing vast amounts of data from diverse sources, you can use AI to proactively pinpoint critical security gaps and potential attack paths before attackers find them.
• Industry example: An energy utility might use AI features of its exposure management platform to find interconnected misconfigurations that could lead from an IT network to critical OT systems. With this information, it can then recommend or trigger automated segmentation actions as needed.

3. 云安全

• Cloud security deeply integrates AI to give you real-time visibility and automated protection. AI constantly monitors cloud configurations, network flows and user activity for anomalies and triggers automated responses.
• Industry example: A media streaming service could use AI in its cloud security platform to instantly find and fix a sensitive container workload from making outbound connections to a suspicious IP address, thereby preventing a potential breach.

4. Email threat detection

• Email threat detection is a widespread application of AI in cybersecurity. AI helps filter phishing and spear phishing attempts by analyzing tone, intent, and anomalies in sender behavior. It learns which message systems are threats and adapts accordingly.
• Industry example: A financial institution could use AI in its email gateway to block sophisticated emails that look like they’re from the CEO or other executives to protect sensitive client data.

5. 端点检测和响应 (EDR)

• Endpoint protection uses AI to classify files, flag zero-day malware and block suspicious behavior, even without a signature. Many EDR solutions use machine learning trained on real-world attacks to detect lateral movement, privilege escalation, and other tactics. Industry example: A healthcare provider might use AI-powered EDR to detect and stop a ransomware actor from encrypting and exfiltrating patient data. It can even roll back to its pristine condition, e.g., removing malicious files/content as part of the early-stage ransomware attack.

6. System information and event management (SIEM) and security orchestration, automation and response (SOAR) systems

• In your SIEM and SOAR, AI takes your security analytics to the next level by surfacing correlations between seemingly unrelated events. By filtering out false positives and prioritizing anomalies that indicate active exploitation, AI can reduce alert fatigue and mean time-to-detect. AI-powered threat intelligence also helps identify new indicators of compromise (IoCs) faster than human analysts alone for proactive threat hunting.
• Industry example: A large retail chain's SOC could use AI in its SIEM to correlate a minor login anomaly with a suspicious data transfer, then automatically trigger a SOAR playbook to isolate the endpoint. It is a faster and more effective way to find and stop a potential breach.

7. Identity and access management (IAM)

• You’ll also see AI in IAM, where it enforces zero-trust principles by using behavioral analytics to track how users typically access systems and flag suspicious deviations, like logins from unusual geographies or unexpected off-hours activity.
• Industry example: A technology company could use AI in its IAM system to detect an “employee” logging in from another country during non-working hours. It can then automatically trigger a multi-factor authentication challenge to verify the user’s identity.

8.User and entity behavior analytics (UEBA)

• AI enhances UEBA. It can detect insider threats by analyzing policy violations, human resources data on employee performance, and unauthorized attempts to access data. Drawing on a baseline of normal user activity, AI-driven UEBA can find indicators of malicious behavior before it causes damage.
• Industry example: A manufacturing firm might use UEBA to automatically flag an employee accessing confidential design schematics they don’t need for their role. Using AI for UEBA helps your security team intervene early and decrease the risk of intellectual property theft.

9. 云安全

• Cloud environments are dynamic and complex, often filled with non-human identities and ephemeral cloud workloads that manual security tools cannot track. AI addresses this by continuously mapping relationships between cloud resources, identities, and data to create a unified view of your cloud risk. It applies attack path analysis to visualize how threat actors could exploit seemingly minor misconfigurations, like an over-privileged account, to reach critical assets.
• Industry example: A utility company could use AI to secure cloud infrastructure supporting its smart grid analytics. The AI can find a hidden attack path where a compromised third-party developer identity has excessive permission to access sensitive customer billing databases and cloud-based grid control commands. It can then offer prioritized remediation to lock the identity down and prevent a potential supply chain attack on critical infrastructure.

10.OT 安全

• OT environments often use legacy infrastructure, which can be too fragile for traditional active scanning. When you can’t scan and patch, you can have invisible and vulnerable assets. AI cybersecurity can passively ingest network traffic to find all your IT, OT, IoT, cloud, and other assets and map deep dependencies without disrupting production. AI can correlate this data with insights to create a single, unified view of risk across your converged attack surface.
• Industry example: A global manufacturing company could use AI to secure its converged production floors. The AI could passively identify a rogue IoT sensor on the warehouse network (IT) communicating with a legacy engineering workstation (OT) using an unauthorized protocol. By visualizing this cross-domain behavior, AI can give you context to segment the device before malware spreads to critical assembly lines.

While these AI cybersecurity use cases provide specific value, true resilience comes from seeing the whole picture of all your assets and exposures across your entire attack surface. Tenable One unifies this intelligence into a single exposure management platform, so your teams can find, prioritize, and remediate critical exposures everywhere.

Master the fundamentals of AI security: Read Tenable's “What is AI cybersecurity?” guide.