Artificial intelligence (AI) is changing the way individuals and organizations conduct many activities, including the way cybercriminals conduct phishing attacks and iterate malware. Cybercriminals are now using AI to generate personalized phishing emails, deepfakes, and malware to evade traditional detection by impersonating normal user activity and bypassing traditional security models. As a result, rule-based models alone are often insufficient for identity security against AI-powered threats. Behavioral analytics must evolve to dynamic identity-based risk modeling that can not only monitor patterns of suspicious activity over time but also identify discrepancies in real time.
Common risks posed by AI-powered attacks
AI-powered cyberattacks pose significantly different security risks than traditional cyberthreats. By relying on automation and mimicking legitimate behavior, AI allows cybercriminals to scale up their attacks while reducing the number of obvious signals going undetected.
AI-powered phishing and social engineering
Unlike traditional phishing attacks that use generic messaging, AI enables personalized phishing messages at scale by using public data, spoofing executive writing style, or referencing real events to create context-aware messages. These AI-powered attacks can reduce obvious red flags, bypass some filtering techniques, and rely on psychological manipulation rather than malware delivery, significantly increasing the risk of credential theft and financial fraud.
Automated credential abuse and account takeover
AI-enhanced credential exploitation can optimize login attempts while avoiding triggering lockout thresholds, mimicking human-like timing between authentication attempts, and targeting privileged accounts based on context. Because these attacks use compromised credentials, they often appear legitimate and blend into normal login activity, making identity security a critical component of modern security strategies.
AI-assisted malware
Before cybercriminals could use AI to accelerate malware development and deployment, they had to manually change code signatures and spend significant time creating new variants. AI can further speed up variation, scripting, and adaptation. Modern adaptive malware allows cybercriminals to automatically modify code to evade detection, change behavior based on the environment, and generate new exploit variants with little or no manual effort. Traditional signature-based detection models struggle to cope with continuously evolving code, so organizations must start relying on behavioral patterns rather than static metrics.
How traditional behavioral surveillance fails against AI-based attacks
Traditional monitoring was designed to detect cyber threats caused by malware, known security vulnerabilities, and visible behavioral anomalies. Here are some ways traditional behavioral monitoring falls short against AI-powered attacks.
Signature-based detection cannot identify the latest threats: Signature-based tools rely on known indicators of compromise. AI-assisted malware constantly rewrites its code and automatically generates new variants, making static code signing obsolete. Rule-based systems rely on predefined thresholds. Many behavioral monitoring systems rely on rules such as login frequency or geographic location. AI-assisted cybercriminals adjust their behavior to stay within set limits, carry out malicious activities over long periods of time, and mimic human behavior to avoid detection. Boundary-based models fail when compromised credentials are involved. Traditional perimeter-based security models assume trust once a user or device is authenticated. Once cybercriminals authenticate with legitimate credentials, these outdated models treat them as valid users and allow them to perform malicious actions. AI-based attacks are designed to appear normal. AI-based cyber threats act within their assigned authority, follow expected workflows, and intentionally introduce activities by performing activities incrementally. Although isolated activities may seem legitimate, the main risk is when the activities are viewed in parallel with the context of the behavior over time.
Why you need to move behavioral analytics towards AI-based attacks
The move to modern behavioral analytics requires an evolution from simple threat detection to dynamic, context-aware risk modeling that can identify subtle privilege abuses.
Identity-based attacks require context
To appear normal, AI-driven cybercriminals often use compromised credentials through phishing or credential abuse, work from known devices and networks, and carry out malicious activities over time to avoid detection. Modern behavioral analytics must assess whether even small changes in behavior match a user’s typical behavior patterns. Advanced behavioral models establish baselines, assess real-time activity, and combine identity, device, and session context.
Monitoring needs to extend across the stack
Once cybercriminals gain access to systems through compromised, weak, or reused credentials, they focus on expanding their access over time. Behavioral visibility should cover the entire security stack, including privileged access, cloud infrastructure, endpoints, applications, and management accounts. To make behavioral analytics more effective against AI-based cyberattacks, organizations must enforce zero trust security and assume that no user or device should have implicit trust or automatic authentication based on network location.
Malicious insiders can use AI tools
AI tools not only empower external cybercriminals, but also make it easier for malicious insiders to operate within an organization’s network. Malicious insiders can use AI to automate credential collection, identify sensitive information, and generate believable phishing content. Because insiders often operate with legitimate privileges, detecting abuse of privilege requires identifying anomalous behavior such as access beyond defined responsibilities, activity outside of normal business hours, or repeated activity within critical systems. By eliminating continuous access by enforcing just-in-time (JIT) access, session monitoring, and session recording, organizations can limit exposure and reduce the impact of compromised accounts and insider abuse.
Protect your identity from autonomous AI-based cyberattacks
AI-powered cyberattacks are becoming increasingly automated, as AI agents can create persuasive social engineering campaigns, test credentials at scale, and reduce the hands-on effort required to carry out attacks. Protecting both human and non-human identities (NHI) now requires more than authentication. Organizations must implement continuous, context-aware behavioral analytics and granular access controls. Modern privileged access management (PAM) solutions like Keeper integrate behavioral analytics, real-time session monitoring, and JIT access to protect identities across hybrid and multicloud environments.
Note: This article was thoughtfully written and contributed by Keeper Security content writer Ashley D’Andrea for our readers.
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