The Rise of AI-Generated Attacks: Why UEBA is the Best Defense

Introduction: The New Cyberthreat Landscape

The cybersecurity industry is facing a new challenge: AI-generated attacks. With the rapid advancement of generative AI, cybercriminals now have access to sophisticated tools that enable them to craft highly targeted attacks with minimal technical expertise. Unlike traditional attack methods that require deep programming knowledge, AI-driven attacks allow even non-technical malicious actors to create malware, exploit scripts, and launch phishing campaigns with ease. This shift is leading to a surge in novel, previously unseen attack patterns that evade traditional security measures.

The problem is clear: If attacks don’t have known indicators of compromise (IoCs), how can they be detected? Conventional security approaches—such as threat intelligence feeds and correlation rules—are struggling to keep up with this new wave of AI-powered threats. The evolving nature of these attacks requires a behavior-based security model—one that analyzes patterns, detects anomalies, and adapts dynamically to emerging threats.

This blog explores how generative AI is fueling unknown attacks, why traditional detection methods are failing, and why User and entity behavior analytics (UEBA) is the best defense against this growing threat.

How Generative AI Is Fueling Unknown Attacks

Cyberattacks used to follow predictable patterns, often relying on known vulnerabilities or established social engineering tactics. AI-generated attacks are breaking this mold, allowing threat actors to automate, customize, and evolve their methods in real time.

The Democratization of Cybercrime

Traditionally, cyberattacks required significant technical expertise. Now, generative AI tools can create exploit scripts, phishing emails, and malware variants at the click of a button. This means that less-skilled individuals can launch highly effective attacks, increasing both the volume and sophistication of cyberthreats.

For example, AI-powered tools can generate convincing spear-phishing emails tailored to specific individuals, making them much harder to detect. Attackers can also create polymorphic malware that continuously mutates to evade signature-based detection methods.

The Rise of Adaptive Attacks

Unlike static, pre-programmed attacks, AI-assisted cyberthreats can adjust their tactics dynamically. For example:

• Malware can rewrite its own code to avoid detection by antivirus software.
• Brute-force attacks can leverage AI to modify login attempts, IP addresses, and device fingerprints to appear legitimate.
• Phishing attacks can generate hyper-personalized messages that bypass spam filters.

As a result, traditional security tools are struggling to keep pace with AI’s ability to adapt and create attacks that don’t fit known patterns.

Why Threat Intelligence Feeds and Correlation Rules Are Less Effective

For years, security teams have relied on threat intelligence feeds and correlation rules to detect cyberthreats. However, these traditional methods are proving to be insufficient in stopping AI-generated attacks.

Threat Intelligence Feeds: Falling Behind Evolving Attacks

Threat intelligence feeds collect known IoCs—such as malware signatures, suspicious IP addresses, and attack patterns—and alert security teams when a match is found. However, this method has a critical flaw: AI-generated attacks often have no known IoCs.

Since AI can generate unique, customized attack variants for each target, traditional threat intelligence feeds cannot keep up. Attackers can easily modify code structures, execution methods, and attack delivery mechanisms, making detection by signature-based defenses nearly impossible.

Correlation Rules: Too Rigid for AI-Powered Attacks

Most security information and event management (SIEM) platforms rely on correlation rules to detect attacks based on predefined sequences of events. For example, a rule might trigger an alert if a user logs in from an unusual location and downloads large amounts of data. However, AI-generated attacks are highly adaptable, often changing just enough to avoid triggering these static rules.

Correlation rules have two major limitations:

• They require constant updates. As new attack methods emerge, security teams must rewrite and fine-tune rules.
• They generate too many false positives. Legitimate activity is often flagged as suspicious, leading to alert fatigue.

With AI-powered threats evolving in real time, rule-based detection methods simply cannot keep pace.

Why UEBA Is the Best Defense Against AI-Generated Attacks

To effectively combat unknown, AI-generated attacks, security teams need a behavior-based approach. This is where UEBA comes in.

Understanding Behavioral-Based Detection

Unlike traditional security methods that rely on static rules or known signatures, UEBA detects anomalies by establishing a baseline of normal behavior and flagging deviations. This allows security teams to identify threats even when there are no prior IoCs or predefined attack patterns.

For example, UEBA can detect if:

• A user logs in from an unusual location at an odd hour.
• A privileged account accesses sensitive files it has never interacted with before.
• A system begins transferring unusually large volumes of data to an external service.

By analyzing patterns of behavior instead of relying on predefined rules, UEBA can spot subtle, emerging threats that other tools miss.

How Exabeam UEBA Eliminates Noise and Reduces False Positives

One of the biggest challenges in cybersecurity is alert fatigue, where security teams are overwhelmed by false positives. Exabeam UEBA addresses this issue with two key features:

• Detection groupings: UEBA stitches together multiple alerts into a single timeline, eliminating one-off anomaly alerts that might otherwise be dismissed as false positives.
• Dynamic multi-layer risk scoring: Instead of flagging every small anomaly, UEBA assigns a risk score to each automatically generated case (or alert), allowing security teams to prioritize high-risk activity effectively.

By automating anomaly detection and intelligently grouping alerts, UEBA enables security teams to focus on the threats that pose the highest level of risk.

The Exabeam Difference: A Full-Stack Security Operations Platform

While many vendors offer point solutions, the Exabeam New-Scale Security Operations Platform delivers a full-stack, cloud-native security operations platform. We understand that different threat detection methods are better suited for specific use-cases—and we support them all. This means customers get the best of all worlds:

• Baselining normal behavior: Establishing a comprehensive understanding of typical user and system activities, enabling the detection of subtle deviations that may indicate advanced or AI-generated threats
• Patented session data model: Automatically correlating security events into unified, behavior-based sessions
• Automated investigation and response: Reducing analyst workload by grouping threats into a structured timeline based on user and entity groupings
• Support for correlation rules and logic trees: Allowing customers to leverage traditional security methodologies alongside UEBA
• Open-source threat intelligence feed – Providing IoCs to supplement behavior-based detection for known threats

With Exabeam New-Scale Analytics, security teams can detect and respond to AI-generated attacks in real time, without relying solely on static rules or threat feeds.

Are you a LogRhythm customer looking to add UEBA to uplevel threat detection and support defense against generative AI attacks and insider threats? We have a solution for you. LogRhythm Intelligence delivers Exabeam UEBA directly to the LogRhythm UI.

Conclusion: The Future of Security Is Behavior-Based

As AI-generated attacks continue to evolve, security strategies must adapt. Static rules and threat feeds are no longer enough. UEBA is the only approach that can effectively detect unknown threats by focusing on behavioral anomalies rather than signatures.

By implementing Exabeam New-Scale Analytics, organizations gain:

• Real-time, self-learning threat detection
• Automated correlation of user and entity behaviors
• Precise risk scoring to alleviate alert fatigue

With our full-stack security operations platform, Exabeam customers receive the best of all security methodologies—threat intelligence, correlation rules, and UEBA—all in one solution. No matter how sophisticated AI-generated attacks become, security teams can stay one step ahead.

Want to experience Exabeam industry-leading UEBA capabilities for yourself? See, first-hand, how Exabeam stops hard to detect unknown threats with ease and automation. Free expert lead demo