UEBA (User Entity Behavior Analysis) is a security layer that uses machine learning and analytics to detect threats by analyzing patterns in user and entity behavior. UEBA (User Entity Behavior Analysis) Here’s an oversimplified example of UEBA: suppose you live in Chicago. You’ve lived there for several years and rarely travel. But suddenly there’s a charge to your credit card from a restaurant in Italy. Someone is using your card to pay for their lasagna! Luckily, your credit card company recognizes the behavior as suspicious, flags the transaction, and stops it from settling. This is easy for your credit card company to flag: they have plenty of historical information on your habits and have created a set of logical rules and analytics for when to flag your transactions. But most threats are not this easy to detect. Attackers are continuously becoming more sophisticated and learning to work around established rules. As a result, traditional UEBA that relies primarily on static, rigid rules is no longer enough to protect your systems. traditional UEBA that is no longer enough to protect your systems. The End of Traditional UEBA–or, Why Your UEBA No Longer Works Many UEBA tools were built around static rules and predefined behavioral thresholds. Those approaches were useful for catching predictable, well-understood behavior patterns, but are not great in modern environments where user activity, applications, and attacker behavior change constantly. Soon, AI agents will act on behalf of users and introduce even more diversity. static rules and predefined behavioral thresholds Here are the main limitations of traditional, static-rule-driven UEBA: Static behavioral thresholds don’t adapt to real user behavior over time. They rely on fixed assumptions (e.g., “alert if X happens more than Y times”), which quickly become outdated as user behavior and environments evolve.
Rules require continuous manual tuning. Security teams spend time chasing false positives or rewriting rules as user behavior changes, which slows response and increases operational overhead.
Isolated detection logic lacks context. Legacy UEBA often analyzes events in silos, instead of correlating activity across identity, endpoint, network, and application layers. This limits the ability to detect subtle behavioral anomalies. Static behavioral thresholds don’t adapt to real user behavior over time. They rely on fixed assumptions (e.g., “alert if X happens more than Y times”), which quickly become outdated as user behavior and environments evolve. Static behavioral thresholds don’t adapt to real user behavior over time. Rules require continuous manual tuning. Security teams spend time chasing false positives or rewriting rules as user behavior changes, which slows response and increases operational overhead. Rules require continuous manual tuning. Isolated detection logic lacks context. Legacy UEBA often analyzes events in silos, instead of correlating activity across identity, endpoint, network, and application layers. This limits the ability to detect subtle behavioral anomalies. Isolated detection logic lacks context. As a result, certain types of threats that blend into normal activity can go unnoticed despite the presence of rules. So, if legacy UEBA is not effective …what’s the solution? What Modern Enterprises Actually Need From UEBA Modern enterprises need UEBA systems that can do three things: Immediately detect attacks. When attackers can morph in an instant, you need a security layer that moves just as fast.
Recognize attacks that are highly sophisticated and complex. Attacks are no longer simple enough to be caught by a set of rules — even advanced ones backed with behavioral analytics.
Integrate seamlessly with existing security operations. Immediately detect attacks. When attackers can morph in an instant, you need a security layer that moves just as fast. Immediately detect attacks Recognize attacks that are highly sophisticated and complex. Attacks are no longer simple enough to be caught by a set of rules — even advanced ones backed with behavioral analytics. Recognize attacks that are highly sophisticated and complex Integrate seamlessly with existing security operations. Integrate seamlessly with existing security operations Let’s look at each in more detail and how it can be achieved. Immediately detect attacks (without a long training period) Traditional UEBA training periods leave organizations vulnerable for months and chronically behind on detecting the latest threats. A typical three to six-month learning period creates a huge security gap. Day-one detection capabilities for behavioral threats and compromised accounts require first-seen and outlier rules that can spot anomalous behavior immediately without waiting for machine learning models to mature. You need near-instant behavioral baselines. How? You need near-instant behavioral baselines Luckily, most organizations already have the data they need: years of historical log information sitting in their Security Information and Event Management (SIEM) systems. Modern UEBA systems use this information to create behavioral baselines instantly. For example, companies like Sumo Logic — who advocate for the “log everything” approach, have tools that use the data you already have to create powerful baselines in just minutes. Sumo Logic log everything Detect Highly Sophisticated Attacks Today’s attacks blend in with normal business operations. Correlation rules miss behavioral threats that show only subtle anomalies; they can’t identify compromised accounts or insider threats that are performing normal-looking activities. Modern UEBA solutions must be able to detect first-seen activities, such as unusual file sharing via OneDrive. They need to gain access to new proxy categories and suspicious cloud service usage that don’t match historical user behavior. Modern UEBA solutions must be able to detect first-seen activities This comes down to using the right tools. For example, Microsoft Sentinel can identify unusual Azure identity behaviors such as abnormal cloud service access patterns that could indicate account compromise or data exfiltration. And Sumo Logic has first-seen and outlier rules that can detect when an attacker is trying to use a network sniffing tool. They catch endpoint enumeration and suspicious email forwarding rules that deviate from established patterns. Microsoft Sentinel first-seen outlier Integration with existing security operations UEBA delivers meaningful value when it fits naturally into existing security workflows. Security teams rely on SIEM, SOAR, identity systems, and endpoint tools to build a complete picture of activity across their environment. UEBA works best when its behavioral insights are delivered in the same place analysts already investigate and respond to alerts. UEBA delivers meaningful value Effective UEBA solutions, therefore, integrate directly with the broader security platform, allowing behavioral anomalies to be correlated with logs, identity events, and threat intelligence. This unified context helps analysts make faster, more accurate decisions without switching tools or managing separate consoles. Flexibility is also important. Organizations must be able to adjust detection logic and behavioral thresholds to match their environment, risk tolerance, and operational needs. When UEBA is tightly integrated and adaptable, it becomes an extension of the security workflow rather than an additional system to maintain. UEBA as the Foundation for AI Security Agents UEBA hasn’t been replaced by AI. Instead, UEBA has become the way to train AI. AI-powered detection and response solutions perform best when they ingest clean, comprehensive behavior baselines, and that’s exactly what mature UEBA can provide. clean, comprehensive behavior baselines AI agents need quality behavioral baselines AI security agents aren’t magic. They follow the GIGO (garbage in, garbage out) principle just like any other data-intensive system. Feed an AI agent high-quality behavioral data, and it will thrive. But if you feed it insufficient or poor-quality data, then you’ll become part of the 95% statistic of AI pilots that fail to deliver real business value. 95% statistic of AI pilots that fail to deliver real business value Structured UEBA rules also give the agents specialist knowledge, such as who should log in where, how often a service account connects to S3, and typical overnight file volumes. AI agents can learn (and extend) these rulesets. AI detects, then AI responds Security teams often drown in alerts. Teams can’t keep up. But when UEBA feeds AI: First-seen rules become automatic triggers. Instead of waiting for an analyst, an agent can begin gathering data and context within seconds.
AI can rank threats, helping to make sure human attention is given to the events with the biggest deviation or highest blast radius.
Entity relationship maps derived from UEBA help agents model lateral-movement risk and choose containment tactics (for example: quarantine the host, revoke credentials, etc.). First-seen rules become automatic triggers. Instead of waiting for an analyst, an agent can begin gathering data and context within seconds. AI can rank threats, helping to make sure human attention is given to the events with the biggest deviation or highest blast radius. Entity relationship maps derived from UEBA help agents model lateral-movement risk and choose containment tactics (for example: quarantine the host, revoke credentials, etc.). Once the system can reliably detect threats, you can take it to the next level and allow your AI agents to take action, too. From UEBA rules to autonomous security operations Manual security operations have a scaling problem. They can’t keep pace with modern threat volumes and complexity. As a result, organizations miss threats or burn out security analysts with overwhelming alert fatigue. But with UEBA first-seen rules, AI agents can immediately begin collecting evidence and correlating events when anomalous behavior is detected. Outlier detection can feed AI-driven risk scoring and prioritization algorithms. And behavioral baselines can ensure that automated responses are based on a solid understanding of what constitutes legitimate versus suspicious activity within the specific organizational context. You can still have a human in the loop, authorizing each action recommended by the AI system. Eventually, you may delegate action to the AI system as well, with humans merely being notified after the fact. Building AI-Ready Behavioral Foundations Now Modern UEBA platforms are already generating AI-compatible behavioral data. These platforms structure their outputs in ways that AI agents can easily consume and act upon. For example: Ongoing discovery of the best ways to format and organize data to fit optimally into the context of LLMs, and how to provide them with effective tools.
Signal-clustering algorithms to reduce the noise that might confuse an AI agent’s decision-making. This ensures that only meaningful behavioral anomalies reach automated systems for action.
Rule customization and match lists provide structured data that AI agents can interpret and act upon. This creates clear decision trees for autonomous responses.
Historical baseline capabilities create rich training datasets without waiting months for AI deployment. Organizations can leverage years of existing log data. AI agents can begin operating with sophisticated behavioral understanding from day one rather than starting with blank behavioral models. Ongoing discovery of the best ways to format and organize data to fit optimally into the context of LLMs, and how to provide them with effective tools. Ongoing discovery Signal-clustering algorithms to reduce the noise that might confuse an AI agent’s decision-making. This ensures that only meaningful behavioral anomalies reach automated systems for action. Rule customization and match lists provide structured data that AI agents can interpret and act upon. This creates clear decision trees for autonomous responses. Historical baseline capabilities create rich training datasets without waiting months for AI deployment. Organizations can leverage years of existing log data. AI agents can begin operating with sophisticated behavioral understanding from day one rather than starting with blank behavioral models. With these capabilities already in place, organizations can transition seamlessly from manual to automated security operations. The Bottom Line When implementing UEBA, focus on true principles and actionable strategies: 1. Ensure comprehensive, high‑quality data integration Ensure comprehensive, high‑quality data integration Use all relevant data sources: existing logs, new telemetry, identity systems, endpoints, and cloud apps to build complete behavioral profiles. If critical data is missing, you should collect it and add it to the UEBA’s scope. For example, a user’s calendar showing a business trip to Tokyo is very relevant when the system detects login attempts from Japan. 2. Accelerate meaningful baselines using historical data and rapid observation periods Accelerate meaningful baselines using historical data and rapid observation periods Leverage historical log data to establish baselines quickly, but expect this to take a couple of days to a few weeks. Supplement with fresh data as needed to ensure the baseline reflects current behaviors. For example, if an employee moves to a different team, the system should expect a change in behavior. 3. Integrate UEBA insights with your current security workflows Integrate UEBA insights with your current security workflows UEBA should capitalize on SIEM and other security tools to deliver high-impact alerts and operational value. Avoid requiring extensive new infrastructure unless necessary, and always align the solution to your organization’s needs. These approaches deliver immediate value and adapt to changes to maximize the coverage and accuracy of behavioral analytics. Your success metrics matter just as much as your implementation. Track the following: How many sophisticated threats does UEBA catch that your traditional systems miss
The reduction in dwell time for compromised accounts
Coverage improvements for lateral movement and unknown attack patterns
Analyst efficiency gains from richer contextual alerts. How many sophisticated threats does UEBA catch that your traditional systems miss The reduction in dwell time for compromised accounts Coverage improvements for lateral movement and unknown attack patterns Analyst efficiency gains from richer contextual alerts. These metrics prove value to stakeholders and help you continuously refine your approach. While classic rule-based UEBA relied on manual configuration, today’s UEBA platforms enhance these foundations with autonomous analytics using statistical models, adaptive baselines, and AI-driven outlier detection. Functions like first-seen and outlier detection do leverage rules, but they operate as part of a dynamic, context-aware system rather than a set of static match expressions. AI agents continuously monitor and analyze vast streams of behavioral data, learning what constitutes normal activity and identifying subtle anomalies that may indicate emerging threats. By correlating signals across users, devices, and time, these agents enable real-time, adaptive detection and response. This elevates security operations from manually maintained static rule matching to intelligent and proactive protection. first-seen outlier detection

Here's Why Your UEBA Isn’t Working (and How to Fix It)

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