Saryu Nayyar is an internationally acknowledged cybersecurity knowledgeable, creator, speaker and member of the Forbes Expertise Council. She has greater than 15 years of expertise within the info safety, id and entry administration, IT danger and compliance, and safety danger administration sectors.
She was named EY Entrepreneurial Successful Girls in 2017. She has held management roles in safety services and products technique at Oracle, Simeio, Solar Microsystems, Vaau (acquired by Solar) and Disney. Saryu additionally spent a number of years in senior positions on the know-how safety and danger administration follow of Ernst & Younger.
Gurucul is a cybersecurity firm that focuses on behavior-based safety and danger analytics. Its platform leverages machine studying, AI, and large knowledge to detect insider threats, account compromise, and superior assaults throughout hybrid environments. Gurucul is thought for its Unified Safety and Danger Analytics Platform, which integrates SIEM, UEBA (Consumer and Entity Conduct Analytics), XDR, and id analytics to offer real-time risk detection and response. The corporate serves enterprises, governments, and MSSPs, aiming to scale back false positives and speed up risk remediation by clever automation.
What impressed you to start out 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 get hold of readability into probably 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 consumer and entity anomalies into context throughout a wide range of totally different assault and danger situations. We’ve constructed upon this as our basis, shifting from serving to massive Fortune 50 firms remedy Insider Danger challenges, to serving to firms acquire radical readability into ALL cyber danger. That is the promise of REVEAL, our unified and AI-Pushed Knowledge 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 the complete risk lifecycle. The aim is for analysts and engineers to spend much less time within the myriad in complexity and extra time targeted 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 convey from these experiences into founding Gurucul?
My management expertise at Oracle, Solar Microsystems, and Ernst & Younger strengthened my potential to unravel advanced safety challenges and supplied me with an understanding of the challenges that Fortune 100 CEOs and CISOs face. Collectively, it allowed me to realize 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 upon static, rule-based approaches that result in extreme false positives, elevated prices, and delayed detection and response. Our REVEAL platform is absolutely 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 repeatedly adapts to evolving threats and integrates throughout on-premises, cloud, and hybrid environments for complete safety protection. Acknowledged because the ‘Most Visionary’ SIEM resolution in Gartner’s Magic Quadrant for 3 consecutive years, REVEAL redefines AI-driven SIEM with unmatched precision, velocity, and visibility. Moreover, SIEMs battle with an information overload downside. They’re too costly to ingest the whole lot 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 got a local and AI-driven Knowledge Pipeline Administration resolution that filters non-critical knowledge to low-cost storage, saving cash, whereas retaining the power to run federated search throughout all knowledge. Analytics techniques are a “rubbish in, rubbish out” scenario. If the info coming in is bloated, pointless or incomplete then the output is not going to be correct, actionable or finally 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 repeatedly analyze all related datasets and establish anomalies and suspicious behaviors in actual time. In contrast to legacy safety techniques that depend on static guidelines, REVEAL uncovers threats as they emerge. The platform additionally makes use of Consumer and Entity Conduct Analytics (UEBA) to determine baselines of regular consumer and entity conduct, detecting deviations that might point out insider threats, compromised accounts, or malicious exercise. This conduct is additional contextualized by an enormous knowledge engine that correlates, enriches and hyperlinks safety, community, IT, IoT, cloud, id, enterprise software knowledge and each inner and exterior sourced risk 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 risk detection and response that set REVEAL other than typical safety options.
How does Gurucul’s AI-driven method assist cut back false positives in comparison with typical cybersecurity techniques?
The REVEAL platform reduces false positives by leveraging AI-driven contextual evaluation, behavioral insights, and machine studying to differentiate legit consumer 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 deal with legit safety dangers fairly than being overwhelmed by false alarms. Whereas Machine Studying is a foundational side, generative and agentic AI play a big function in additional appending context in pure language to assist analysts perceive precisely what is occurring round an alert and even automate the response to mentioned alerts.
What function does adversarial AI play in fashionable 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. That is why 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 will’t evade the behavioral based mostly detections in the identical manner. SOC groups will not be resourced adequately to proceed to write down guidelines to maintain tempo and would require a contemporary method to risk detection, investigation and response. Conduct and context are the important thing components. Lastly, platforms like REVEAL rely upon a steady suggestions loop and we’re consistently making use of AI to assist us refine our detection fashions, advocate new fashions and inform new risk intelligence our total 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 based mostly 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 deal with probably the most urgent incidents fairly than being overwhelmed by low-priority alerts. With a unified danger rating spanning all enterprise knowledge sources, safety groups acquire higher visibility and management, resulting in quicker, extra knowledgeable decision-making.
In an age of accelerating knowledge breaches, how can AI-driven safety options assist organizations forestall insider threats?
Insider threats are an particularly difficult safety danger because of their delicate nature and the entry that workers possess. REVEAL’s UEBA detects deviations from established behavioral baselines, figuring out dangerous actions reminiscent of unauthorized knowledge entry, uncommon login occasions, and privilege misuse. Dynamic danger scoring additionally repeatedly assesses behaviors in actual time, assigning danger ranges to prioritize probably 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 in opposition to 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 techniques, HR functions and all different related knowledge sources provides these groups the ammunition to rapidly construct and defend a case of proof so the enterprise can reply and remediate earlier than knowledge exfiltration happens.
How does Gurucul’s id analytics resolution improve safety in comparison with conventional IAM (id and entry administration) instruments?
Conventional IAM options deal with 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 repeatedly assess consumer 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 resolution enhances id safety and reduces the assault floor. The issue with IAM governance is id system sprawl and the dearth of interconnectedness between totally different id techniques. Gurucul provides groups a 360° view of their id dangers throughout all id infrastructure. Now they will cease rubber stamping entry however fairly take risk-oriented method to entry insurance policies. Moreover, they will expedite the compliance side of IAM and exhibit a steady monitoring and absolutely 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?
Id-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 by way of 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, firms 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 may have the ability to evade detections by doing so and it is going to be low value for them to artistic adaptive techniques, technics and protocols. Therefore once more why specializing in conduct in context and having detection techniques able to adapting simply as quick might be essential for the foreseeable future.
Thanks for the nice interview, readers who want to be taught extra ought to go to Gurucul.
