Traditional Security Information and Event Management (SIEM) falls short in tackling modern threats. Organizations face an overwhelming volume of data – petabytes of logs, alerts, and telemetry — much of which is irrelevant. This data overload hides real threats, forcing the already stretched security teams to sift through false positives while attackers exploit vulnerabilities.
Modern Security teams face a sea of alerts rather than an actual flood of alerts. The challenge arises from overlapping detections, benign anomalies, and outdated rules, which can generate a confusing noise that can overwhelm even the most seasoned Security Operations Center (SOC) analysts.
Redundant logs: Excessive logs from identities, endpoints or various telemetry sources often flood the SIEM with repetitive events. This can overwhelm the analytics pipelines, increase storage consumption and reduce the signal-to-noise ratio, taxing resources without improving the visibility. Low fidelity indicators, such as generic logins or unrelated access attempts, offer limited context and minimal actionable insights. These weak signals complicate investigations and result in false alarms.
This is where AI-powered SIEM systems become essential, like noise-cancelling headphones. They not only bolster detection capabilities but also enhance the intelligence behind those detections, allowing analysts to focus on genuine threats rather than sifting through a ton of irrelevant alerts.
The Gurucul next-gen AI SIEM is carefully crafted to significantly reduce false positives. It does this by enhancing telemetry through the integration of identity context and thorough risk assessment. This method effectively filters generic signals, enabling the system to concentrate on the most relevant alerts. The AI SIEM serves as a force multiplier for SOC teams to streamline threat detection and improve decision-making. It summarizes incidents with rich context and accelerates the efficiency of SOC teams by prioritizing meaningful alerts.
At the core of Gurucul’s sophisticated ‘noise-cancelling’ capability lies its groundbreaking artificial intelligence engine Sme AI. Agentic AI augments each alert with threat intelligence and aligns it with the MITRE ATT&CK framework and clarifies the blast radius impact on each user, entity or system. By connecting entity actions, detecting anomalies, and evaluating access context across hybrid cloud environments, it provides high-fidelity alerts for security operations.
Gurucul’s AI engine does more than just produce quality alerts; it outlines detailed narratives. This facilitates security analysts with faster validation and enhances the speed and quality of responses. Consequently, SOC teams are enabled to focus on genuine alerts instead of pursuing false positives.
Gurucul’s AI SIEM is driven by its UEBA (User and Entity Behavior Analytics) behavioral analytics engine, comprising more than 4000 machine learning models to help achieve this noise cancellation effect. These models establish a baseline of normal activity across any entity, including users, identities, systems (endpoints) and applications.
For example, a user accessing HR (human resources) files at 11 a.m. might be normal. The same user accessing the same data at 2 AM from another device or location might be suspicious. So, that would be flagged.
Unlike traditional security tools that depend on static rules or signature-based detections, Gurucul uses unsupervised and supervised machine learning to detect anomalies that diverge from normal baseline behaviors. Anomalies combined with a timeline of events and context of what happened before and after, effectively identify real threats. This mitigates alert fatigue and focuses analysts’ efforts only on contextualized threats that pose the highest risk to the business. Gurucul offers an open and flexible ML model library so SOC analysts can add new or tweak existing models, refine detection logic and adjust thresholds tailored to your environment to further reduce noise.
Gurucul quantifies risk dynamically, using over 240 attributes, on a normalized scale of 0–100, helping security teams quickly identify and prioritize major threats. Analysts can view a unified risk score for any user, entity, application, or asset across all activities, enabling decisive action with customizable response playbooks tailored to the organization’s needs.
Security teams can easily adjust risk scores based on established tolerance, guiding security analysts on where to focus their efforts. It also allows teams to create custom groups for critical entities, elevating risk scores to aid when these groups are impacted.
Gurucul’s AI SIEM collects telemetry from various sources. Instead of treating every log as a standalone alert, Gurucul anchors its deep visibility and analytics around identity context — users, identities, service accounts, privileged access and behavioral baselines. It subsequently correlates this information through an identity lens, connecting disparate events and entities to specific identities using patented link chain analysis and connects them back into timeline narratives. So rather than seeing many different alerts triggering, they are tied back to a single security incident (or case). This allows security teams to reduce alert volumes, false positives and truly stay focused on real threats.
As adversaries have become more sophisticated and attack surfaces have broadened, traditional security tools are unable to keep pace. Gurucul’s AI SIEM provides an open, flexible and scalable solution — offering real insights, minimizing alert fatigue and streamlining security teams for faster threat detection and response.
Reduced noise ensures
Gurucul’s AI SIEM empowers SOC teams to become orchestrators of AI-driven defense rather than merely being passive responders to alert fatigue. Explore how these groundbreaking ‘noise-cancelling’ technologies can revolutionize your security operations and enhance your threat detection and response abilities. Request a customized demo today to begin!
Nagesh Swamy, Product Marketing Manager
Nagesh Swamy is a seasoned product marketer at Gurucul with 15+ years of expertise across cybersecurity, IT infrastructure, and enterprise software. He has spearheaded go-to-market campaigns, competitive intelligence programs, and global product launches for marquee brands like Zscaler, Securonix, Wipro, HP, IBM, and EMC.
Traditional SIEMs generate massive volumes of alerts—many of which are false positives or low-fidelity signals. This overwhelms SOC teams and buries real threats under irrelevant noise, making detection slower and less accurate.
Gurucul’s AI SIEM filters out low-value and redundant telemetry using advanced identity context, behavioral analytics, and real-time risk scoring. It prioritizes high-risk, high-fidelity alerts, allowing analysts to focus only on what matters most.
At the core is Sme AI, which enriches every alert with threat intelligence, maps to the MITRE ATT&CK framework, and builds timeline narratives. This helps analysts quickly understand blast radius, context, and impact—leading to faster, more confident response.
Gurucul leverages over 4,000 machine learning models to baseline normal behavior across users, identities, and systems. Anomalies are correlated with surrounding events to isolate real threats and reduce alert fatigue, improving SOC accuracy and productivity.
