Saryu Nayyar, Ceo And Founder Of Gurucul – Interview Series

Saryu Nayyar is an internationally recognized cybersecurity expert, author, speaker and personnel of nan Forbes Technology Council. She has much than 15 years of acquisition successful nan accusation security, personality and entree management, IT consequence and compliance, and information consequence guidance sectors.

She was named EY Entrepreneurial Winning Women successful 2017. She has held activity roles successful information products and services strategy astatine Oracle, Simeio, Sun Microsystems, Vaau (acquired by Sun) and Disney. Saryu besides spent respective years successful elder positions astatine nan exertion information and consequence guidance believe of Ernst & Young.

Gurucul is a cybersecurity institution that specializes successful behavior-based information and consequence analytics. Its level leverages instrumentality learning, AI, and large information to observe insider threats, relationship compromise, and precocious attacks crossed hybrid environments. Gurucul is known for its Unified Security and Risk Analytics Platform, which integrates SIEM, UEBA (User and Entity Behavior Analytics), XDR, and personality analytics to supply real-time threat discovery and response. The institution serves enterprises, governments, and MSSPs, aiming to trim mendacious positives and accelerate threat remediation done intelligent automation.

What inspired you to commencement Gurucul successful 2010, and what problem were you aiming to lick successful nan cybersecurity landscape?

Gurucul was founded to thief Security Operations and Insider Risk Management teams get clarity into nan astir captious cyber risks impacting their business. Since 2010 we’ve taken a behavioral and predictive analytics approach, alternatively than rules-based, which has generated complete 4,000+ instrumentality learning models that put personification and entity anomalies into discourse crossed a assortment of different onslaught and consequence scenarios. We’ve built upon this arsenic our foundation, moving from helping ample Fortune 50 companies lick Insider Risk challenges, to helping companies summation extremist clarity into ALL cyber risk. This is nan committedness of REVEAL, our unified and AI-Driven Data and Security Analytics platform. Now we’re building connected our AI ngo pinch a imagination to present a Self-Driving Security Analytics platform, utilizing Machine Learning arsenic our instauration but now layering connected Generative and Agentic AI capabilities crossed nan full threat lifecycle. The extremity is for analysts and engineers to walk little clip successful nan myriad successful complexity and much clip focused connected meaningful work. Allowing machines to amplify nan meaning of their day-to-day activities.

Having worked successful activity roles astatine Oracle, Sun Microsystems, and Ernst & Young, what cardinal lessons did you bring from those experiences into founding Gurucul?

My activity acquisition astatine Oracle, Sun Microsystems, and Ernst & Young strengthened my expertise to lick analyzable information challenges and provided maine pinch an knowing of nan challenges that Fortune 100 CEOs and CISOs face. Collectively, it allowed maine to summation a front-row spot nan technological and business challenges astir information leaders look and inspired maine to build solutions to span those gaps.

How does Gurucul’s REVEAL level differentiate itself from accepted SIEM (Security Information and Event Management) solutions?

Legacy SIEM solutions dangle connected static, rule-based approaches that lead to excessive mendacious positives, accrued costs, and delayed discovery and response. Our REVEAL level is afloat cloud-native and AI-driven, utilizing precocious instrumentality learning, behavioral analytics, and move consequence scoring to observe and respond to threats successful existent time. Unlike accepted platforms, REVEAL continuously adapts to evolving threats and integrates crossed on-premises, cloud, and hybrid environments for broad information coverage. Recognized arsenic nan ‘Most Visionary' SIEM solution successful Gartner’s Magic Quadrant for 3 consecutive years, REVEAL redefines AI-driven SIEM pinch unmatched precision, speed, and visibility. Furthermore, SIEMs struggle pinch a information overload problem. They are excessively costly to ingest everything needed for complete visibility and moreover if they do it conscionable adds to nan mendacious affirmative problem. Gurucul understands this problem and it’s why we person a autochthonal and AI-driven Data Pipeline Management solution that filters non-critical information to low-cost storage, redeeming money, while retaining nan expertise to tally federated hunt crossed each data. Analytics systems are a “garbage in, garbage out” situation. If nan information coming successful is bloated, unnecessary aliases incomplete past nan output will not beryllium accurate, actionable aliases yet trusted.

Can you explicate really instrumentality learning and behavioral analytics are utilized to observe threats successful existent time?

Our level leverages complete 4,000 instrumentality learning models to continuously analyse each applicable datasets and place anomalies and suspicious behaviors successful existent time. Unlike bequest information systems that trust connected fixed rules, REVEAL uncovers threats arsenic they emerge. The level besides utilizes User and Entity Behavior Analytics (UEBA) to found baselines of normal personification and entity behavior, detecting deviations that could bespeak insider threats, compromised accounts, aliases malicious activity. This behaviour is further contextualized by a large information motor that correlates, enriches and links security, network, IT, IoT, cloud, identity, business exertion information and some soul and outer originated threat intelligence. This informs a move consequence scoring motor that assigns real-time consequence scores that thief prioritize responses to captious threats. Together, these capabilities supply a comprehensive, AI-driven attack to real-time threat discovery and consequence that group REVEAL isolated from accepted information solutions.

How does Gurucul’s AI-driven attack thief trim mendacious positives compared to accepted cybersecurity systems?

The REVEAL level reduces mendacious positives by leveraging AI-driven contextual analysis, behavioral insights, and instrumentality learning to separate morganatic personification activity from existent threats. Unlike accepted solutions, REVEAL refines its discovery capabilities complete time, improving accuracy while minimizing noise. Its UEBA detects deviations from baseline activity pinch precocious accuracy, allowing information teams to attraction connected morganatic information risks alternatively than being overwhelmed by mendacious alarms. While Machine Learning is simply a foundational aspect, generative and agentic AI play a important domiciled successful further appending discourse successful earthy connection to thief analysts understand precisely what is happening astir an alert and moreover automate nan consequence to said alerts.

What domiciled does adversarial AI play successful modern cybersecurity threats, and really does Gurucul combat these evolving risks?

First each we’re already seeing adversarial AI being applied to nan lowest hanging fruit, nan quality vector and identity-based threats. This is why behavioral, and personality analytics are captious to being capable to place anomalous behaviors, put them into discourse and foretell malicious behaviour earlier it proliferates further. Furthermore, adversarial AI is nan nail successful nan coffin for signature-based discovery methods. Adversaries are utilizing AI to evade these TTP defined discovery rules, but again they can’t evade nan behavioral based detections successful nan aforesaid way. SOC teams are not resourced adequately to proceed to constitute rules to support gait and will require a modern attack to threat detection, investigation and response. Behavior and discourse are nan cardinal ingredients.  Finally, platforms for illustration REVEAL dangle connected a continuous feedback loop and we’re perpetually applying AI to thief america refine our discovery models, urge caller models and pass caller threat intelligence our full ecosystem of customers tin use from.

How does Gurucul’s risk-based scoring strategy amended information teams’ expertise to prioritize threats?

Our platform's move consequence scoring strategy assigns real-time consequence scores to users, entities, and actions based connected observed behaviors and contextual insights. This enables information teams to prioritize captious threats, reducing consequence times and optimizing resources. By quantifying consequence connected a 0–100 scale, REVEAL ensures that organizations attraction connected nan astir pressing incidents alternatively than being overwhelmed by low-priority alerts. With a unified consequence people spanning each endeavor information sources, information teams summation greater visibility and control, starring to faster, much informed decision-making.

In an property of expanding information breaches, really tin AI-driven information solutions thief organizations forestall insider threats?

Insider threats are an particularly challenging information consequence owed to their subtle quality and nan entree that labor possess. REVEAL’s UEBA detects deviations from established behavioral baselines, identifying risky activities specified arsenic unauthorized information access, different login times, and privilege misuse. Dynamic consequence scoring besides continuously assesses behaviors successful existent time, assigning consequence levels to prioritize nan astir pressing insider risks. These AI-driven capabilities alteration information teams to proactively observe and mitigate insider threats earlier they escalate into breaches. Given nan predictive quality of behavioral analytics Insider Risk Management is title against nan clock. Insider Risk Management teams request to beryllium capable to respond and collaborate quickly, pinch privateness top-of-mind. Context again is captious present and appending behavioral deviations pinch discourse from personality systems, HR applications and each different applicable information sources gives these teams nan ammunition to quickly build and take sides a lawsuit of grounds truthful nan business tin respond and remediate earlier information exfiltration occurs.

How does Gurucul’s personality analytics solution heighten information compared to accepted IAM (identity and entree management) tools?

Traditional IAM solutions attraction connected entree power and authentication but deficiency nan intelligence and visibility to observe compromised accounts aliases privilege maltreatment successful existent time. REVEAL goes beyond these limitations by leveraging AI-powered behavioral analytics to continuously measure personification risk, dynamically set consequence scores, and enforce adaptive entree entitlements, minimizing misuse and illegitimate privileges. By integrating pinch existing IAM frameworks and enforcing least-privilege access, our solution enhances personality information and reduces nan onslaught surface. The problem pinch IAM governance is personality strategy sprawl and nan deficiency of interconnectedness betwixt different personality systems. Gurucul gives teams a 360° position of their personality risks crossed each personality infrastructure. Now they tin extremity rubber stamping entree but alternatively return risk-oriented attack to entree policies. Furthermore, they tin expedite nan compliance facet of IAM and show a continuous monitoring and afloat holistic attack to entree controls crossed nan organization.

What are nan cardinal cybersecurity threats you foresee successful nan adjacent 5 years, and really tin AI thief mitigate them?

Identity-based threats will proceed to proliferate, because they person worked. Adversaries are going to double-down connected gaining entree by logging successful either via compromising insiders aliases attacking personality infrastructure. Naturally insider threats will proceed to beryllium a cardinal consequence vector for galore businesses, particularly arsenic protector IT continues. Whether malicious aliases negligent, companies will progressively request visibility into insider risk. Furthermore, AI will accelerate nan variations of accepted TTPs, because adversaries cognize that is really they will beryllium capable to evade detections by doing truthful and it will beryllium debased costs for them to imaginative adaptive tactics, technics and protocols. Hence again why focusing connected behaviour successful discourse and having discovery systems tin of adapting conscionable arsenic accelerated will beryllium important for nan foreseeable future.

Thank you for nan awesome interview, readers who wish to study much should sojourn Gurucul.