Saryu Nayyar is an internationally acknowledged cybersecurity professional, creator, speaker and member of the Forbes Know-how Council. She has greater than 15 years of expertise within the data safety, id and entry administration, IT danger and compliance, and safety danger administration sectors.
She was named EY Entrepreneurial Profitable Ladies in 2017. She has held management roles in safety services technique at Oracle, Simeio, Solar Microsystems, Vaau (acquired by Solar) and Disney. Saryu additionally spent a number of years in senior positions on the expertise safety and danger administration follow of Ernst & Younger.
Gurucul is a cybersecurity firm that makes a speciality of behavior-based safety and danger analytics. Its platform leverages machine studying, AI, and large information to detect insider threats, account compromise, and superior assaults throughout hybrid environments. Gurucul is understood for its Unified Safety and Danger Analytics Platform, which integrates SIEM, UEBA (Person and Entity Conduct Analytics), XDR, and id analytics to supply real-time menace detection and response. The corporate serves enterprises, governments, and MSSPs, aiming to scale back false positives and speed up menace remediation by way of clever automation.
What impressed you to begin Gurucul in 2010, and what downside had been you aiming to unravel within the cybersecurity panorama?
Gurucul was based to assist Safety Operations and Insider Danger Administration groups acquire readability into essentially the most essential cyber dangers impacting their enterprise. Since 2010 we’ve taken a behavioral and predictive analytics method, fairly than rules-based, which has generated over 4,000+ machine studying fashions that put person and entity anomalies into context throughout a wide range of completely different assault and danger eventualities. We’ve constructed upon this as our basis, transferring from serving to massive Fortune 50 corporations clear up Insider Danger challenges, to serving to corporations achieve radical readability into ALL cyber danger. That is the promise of REVEAL, our unified and AI-Pushed Information and Safety Analytics platform. Now we’re constructing on our AI mission with a imaginative and prescient to ship a Self-Driving Safety Analytics platform, utilizing Machine Studying as our basis however now layering on Generative and Agentic AI capabilities throughout all the menace lifecycle. The aim is for analysts and engineers to spend much less time within the myriad in complexity and extra time centered on significant work. Permitting machines to amplify the definition of their day-to-day actions.
Having labored in management roles at Oracle, Solar Microsystems, and Ernst & Younger, what key classes did you deliver from these experiences into founding Gurucul?
My management expertise at Oracle, Solar Microsystems, and Ernst & Younger strengthened my potential to unravel complicated safety challenges and offered me with an understanding of the challenges that Fortune 100 CEOs and CISOs face. Collectively, it allowed me to achieve a front-row seat the technological and enterprise challenges most safety leaders face and impressed me to construct options to bridge these gaps.
How does Gurucul’s REVEAL platform differentiate itself from conventional SIEM (Safety Data and Occasion Administration) options?
Legacy SIEM options rely on static, rule-based approaches that result in extreme false positives, elevated prices, and delayed detection and response. Our REVEAL platform is totally cloud-native and AI-driven, using superior machine studying, behavioral analytics, and dynamic danger scoring to detect and reply to threats in actual time. In contrast to conventional platforms, REVEAL constantly adapts to evolving threats and integrates throughout on-premises, cloud, and hybrid environments for complete safety protection. Acknowledged because the ‘Most Visionary’ SIEM answer in Gartner’s Magic Quadrant for 3 consecutive years, REVEAL redefines AI-driven SIEM with unmatched precision, velocity, and visibility. Moreover, SIEMs wrestle with an information overload downside. They’re too costly to ingest all the things wanted for full visibility and even when they do it simply provides to the false optimistic downside. Gurucul understands this downside and it’s why we’ve a local and AI-driven Information Pipeline Administration answer that filters non-critical information to low-cost storage, saving cash, whereas retaining the flexibility to run federated search throughout all information. Analytics methods are a “rubbish in, rubbish out” state of affairs. If the information coming in is bloated, pointless or incomplete then the output is not going to be correct, actionable or in the end trusted.
Are you able to clarify how machine studying and behavioral analytics are used to detect threats in actual time?
Our platform leverages over 4,000 machine studying fashions to constantly analyze all related datasets and establish anomalies and suspicious behaviors in actual time. In contrast to legacy safety methods that depend on static guidelines, REVEAL uncovers threats as they emerge. The platform additionally makes use of Person and Entity Conduct Analytics (UEBA) to determine baselines of regular person and entity conduct, detecting deviations that might point out insider threats, compromised accounts, or malicious exercise. This conduct is additional contextualized by an enormous information engine that correlates, enriches and hyperlinks safety, community, IT, IoT, cloud, id, enterprise utility information and each inner and exterior sourced menace intelligence. This informs a dynamic danger scoring engine that assigns real-time danger scores that assist prioritize responses to essential threats. Collectively, these capabilities present a complete, AI-driven method to real-time menace detection and response that set REVEAL aside from typical safety options.
How does Gurucul’s AI-driven method assist scale back false positives in comparison with typical cybersecurity methods?
The REVEAL platform reduces false positives by leveraging AI-driven contextual evaluation, behavioral insights, and machine studying to differentiate respectable person exercise from precise threats. In contrast to typical options, REVEAL refines its detection capabilities over time, enhancing accuracy whereas minimizing noise. Its UEBA detects deviations from baseline exercise with excessive accuracy, permitting safety groups to concentrate on respectable safety dangers fairly than being overwhelmed by false alarms. Whereas Machine Studying is a foundational side, generative and agentic AI play a major position in additional appending context in pure language to assist analysts perceive precisely what is going on round an alert and even automate the response to stated alerts.
What position does adversarial AI play in trendy cybersecurity threats, and the way does Gurucul fight these evolving dangers?
First all we’re already seeing adversarial AI being utilized to the bottom hanging fruit, the human vector and identity-based threats. Because of this behavioral, and id analytics are essential to having the ability to establish anomalous behaviors, put them into context and predict malicious conduct earlier than it proliferates additional. Moreover, adversarial AI is the nail within the coffin for signature-based detection strategies. Adversaries are utilizing AI to evade these TTP outlined detection guidelines, however once more they’ll’t evade the behavioral primarily based detections in the identical method. SOC groups usually are not resourced adequately to proceed to put in writing guidelines to maintain tempo and would require a contemporary method to menace detection, investigation and response. Conduct and context are the important thing components. Lastly, platforms like REVEAL rely on a steady suggestions loop and we’re continuously making use of AI to assist us refine our detection fashions, suggest new fashions and inform new menace intelligence our complete ecosystem of shoppers can profit from.
How does Gurucul’s risk-based scoring system enhance safety groups’ potential to prioritize threats?
Our platform’s dynamic danger scoring system assigns real-time danger scores to customers, entities, and actions primarily based on noticed behaviors and contextual insights. This permits safety groups to prioritize essential threats, decreasing response occasions and optimizing assets. By quantifying danger on a 0–100 scale, REVEAL ensures that organizations concentrate on essentially the most urgent incidents fairly than being overwhelmed by low-priority alerts. With a unified danger rating spanning all enterprise information sources, safety groups achieve higher visibility and management, resulting in quicker, extra knowledgeable decision-making.
In an age of accelerating information breaches, how can AI-driven safety options assist organizations stop insider threats?
Insider threats are an particularly difficult safety danger attributable to their refined nature and the entry that workers possess. REVEAL’s UEBA detects deviations from established behavioral baselines, figuring out dangerous actions comparable to unauthorized information entry, uncommon login occasions, and privilege misuse. Dynamic danger scoring additionally constantly assesses behaviors in actual time, assigning danger ranges to prioritize essentially the most urgent insider dangers. These AI-driven capabilities allow safety groups to proactively detect and mitigate insider threats earlier than they escalate into breaches. Given the predictive nature of behavioral analytics Insider Danger Administration is race towards the clock. Insider Danger Administration groups want to have the ability to reply and collaborate rapidly, with privateness top-of-mind. Context once more is essential right here and appending behavioral deviations with context from id methods, HR functions and all different related information sources offers these groups the ammunition to rapidly construct and defend a case of proof so the enterprise can reply and remediate earlier than information exfiltration happens.
How does Gurucul’s id analytics answer improve safety in comparison with conventional IAM (id and entry administration) instruments?
Conventional IAM options concentrate on entry management and authentication however lack the intelligence and visibility to detect compromised accounts or privilege abuse in actual time. REVEAL goes past these limitations by leveraging AI-powered behavioral analytics to constantly assess person danger, dynamically alter danger scores, and implement adaptive entry entitlements, minimizing misuse and illegitimate privileges. By integrating with present IAM frameworks and implementing least-privilege entry, our answer enhances id safety and reduces the assault floor. The issue with IAM governance is id system sprawl and the dearth of interconnectedness between completely different id methods. Gurucul offers groups a 360° view of their id dangers throughout all id infrastructure. Now they’ll cease rubber stamping entry however fairly take risk-oriented method to entry insurance policies. Moreover, they’ll expedite the compliance side of IAM and display a steady monitoring and totally holistic method to entry controls throughout the group.
What are the important thing cybersecurity threats you foresee within the subsequent 5 years, and the way can AI assist mitigate them?
Identification-based threats will proceed to proliferate, as a result of they’ve labored. Adversaries are going to double-down on gaining entry by logging in both through compromising insiders or attacking id infrastructure. Naturally insider threats will proceed to be a key danger vector for a lot of companies, particularly as shadow IT continues. Whether or not malicious or negligent, corporations will more and more want visibility into insider danger. Moreover, AI will speed up the variations of typical TTPs, as a result of adversaries know that’s how they are going to be capable to evade detections by doing so and will probably be low price for them to artistic adaptive techniques, technics and protocols. Therefore once more why specializing in conduct in context and having detection methods able to adapting simply as quick will probably be essential for the foreseeable future.
Thanks for the good interview, readers who want to study extra ought to go to Gurucul.