Saryu Nayyar, CEO and Founder of Gurucul – Interview Series

Saryu Nayyar is the internationally recognized cybersecurity expert, author, speaker and member of the Forbes Technology. It has more than 15 years of information security experience, identity and access management, information technology risks and compliance, and security risk management sectors.

She won the title Ey Enreprenirail Ravertinales Wandents in 2017. She has played leadership roles in the Oracle, Simeio, Sun Microsystems and Vau (which was Sun) and Disney. Sarayio also spent several years in the highest positions in technological security and risk management in Ernst & Young.

Gurucul is a cybersecurity company specialized in security and behavior -based security analyzes. The statute enhances machine learning, artificial intelligence, huge data to detect internal threats, settlement levels, and advanced attacks across mixed environments. Gurucul is famous for the platform for safety analyzes and standard risks, which combine SIEM and UEBA (user behavior analyzes, entity), XDR and identity analyzes to provide detection of the actual threat and response to it. The company serves institutions, governments and MSSPS, aimed at reducing the wrong positives and speeding up the threat treatment through smart automation.

What inspired you to start GUURCUL in 2010, and what is the problem that you were aiming to solve in the scene of cybersecurity?

Gurucul was founded to help security operations and risk management teams from the inside in obtaining clarity in the most important electronic risks that affect their business. Since 2010, we have taken behavioral analysis approach and predicts, instead of the rules, which have generated more than 4000 machine learning models that put the user homosexuals and the entity in a context through a variety of different risk scenarios. We have built this as our basis, and moved from the assistance of large companies 50 to solve the challenges of risk from the inside, to help companies gain fundamental clarity in all cyber risks. This is the promise to reveal, the platform of our unified and preserved analyzes on AI-Haaaa-AIVE. We are now dependent on our artificial intelligence task with a vision to present a self -driving security analysis platform, using automatic learning as our basis but now put on the AI’s obstetric capabilities and the agent through the entire life cycle of threat. The goal is for analysts and engineers to spend less time in countless complexity and more time that focuses on meaningful work. Allow the machines to amplify the definition of their daily activities.

After working in leadership roles in Oracle, Sun Microsystems and Ernst & Young, what are the main lessons that you brought from those experiments to the institution GuruCul?

My leadership experience in Oracle, Sun Microsystems and Ernst & Young enhanced my ability to solve complex security challenges and provided me with an understanding of the challenges facing Fortune 100 executives and CISO. Group, I allowed me to gain a seat in the front row of technological and commercial challenges faced by most security leaders and inspired me to build solutions to fill these gaps.

How does the Gurucul itself distinguish itself from the SIEM solutions (safety information and event management)?

Siem Legacy solutions depend on fixed methods based on the rules that lead to excessive positive positives, increase costs, delay and response. Our detection platform is a fully protected cloud with AI, and uses advanced machine learning, behavioral analyzes, and record dynamic risks to detect and respond threats in actual time. Unlike traditional platforms, they are constantly revealing with advanced threats and complementarity across local environments, clouds and hybrid environments for comprehensive security coverage. Confeated as the “most visible” solution in the magic quarter in Gartner for a period of three consecutive years, reveals the SIEM redefinition by AI with unparalleled accuracy, speed and vision. Moreover, SIEMS struggles with the problem of excessive data. It is very expensive to color all that is required to see a complete, and even if they do it, it only adds to the wrong positive problem. Gurucul understands this problem and for this reason we have a solution to managing the original and stimulating data pipelines, which filter the non -critical data to low -cost storage and saving money, while maintaining the ability to run uniforms through all data. Analysis systems are the position of “garbage in garbage”. If the next data is swollen, unnecessary or incomplete, the output will not be accurate, executed or trusted in the end.

Can you explain how to use machine learning and behavioral analyzes to detect threats in real time?

Our platform benefits from more than 4000 automated learning models to analyze all relevant data groups and identify abnormal cases and suspicious behaviors in actual time. Unlike the old security systems that depend on fixed rules, they reveal threats with their appearance. The basic system also uses user and entity behavior analyzes (UEBA) to create basis lines for normal user behavior and entity, and discover deviations that can indicate internal threats, at risk or malignant activity. This behavior is placed additionally through a large data engine that connects and enrichs and connects security, network, information technology, the Internet of Things, identity, work application data and the intelligence of both internal and external threats. This teaches the dynamic risk risk engine that helps in real time risk that helps to give priority to the critical threats. Together, these capabilities provide a comprehensive approach driven by discovery and response to the actual time that distinguishes traditional security solutions.

How does AI’s Gurucul’s GUURCUL Reducing the positive positives compared to traditional cyber security systems?

It reduces the disclosure of wrong positives by taking advantage of the analysis of the context that AI, behavioral visions, and machine learning to distinguish the legitimate user activity from actual threats. Unlike traditional solutions, it reveals its detection capabilities over time, which improves accuracy while reducing noise. UEBA discovers deviations about the basic activity with high accuracy, allowing security teams to focus on legitimate security risks instead of being overwhelmed by wrong warnings. While machine learning is an essential aspect, the artificial intelligence and the agent plays an important role in the context of attaching to the natural language to help analysts understand exactly what is happening about an alert and even automation of response to the alerts mentioned.

What role does artificial intelligence play in modern cybersecurity threats, and how does Gurucul fight these advanced risks?

First, all that we see already is the aggressive AI is applied to the lowest suspended fruit, human veil and identity -based threats. That is why behavior analyzes, and identity analysis are two decisive things in the ability to identify abnormal behaviors, put them in the context and predict harmful behavior before they multiply more. Moreover, artificial intelligence infection is the nail in the sarcophagus for signing detection methods. Amnesty International’s opponents are used to evade these TTP detection rules, but again they cannot evade behavior -based discoveries in the same way. SOC teams are not obtained adequately to continue writing the rules to keep up with and will require a modern approach to discovering threats, investigation and response. Behavior and context are the main components. Finally, platforms such as detection depend on the continuous feedback ring, and we are constantly applying Amnesty International to help us improve our detection models, recommend new models and inform the intelligence of the new threat that our full ecosystem can benefit from.

How does the GUURCUL -based scoring system improve the ability of security teams to give priority to threats?

Our dynamic risk risk registration system is appointed in actual time for users, entities, procedures based on monitored behaviors and contextual visions. This enables security teams to give priority to critical threats, reduce response times and improve resources. By identifying the risk on a scale from 0 to 100, it ensures that the institutions focus on the most urgent accidents instead of being overwhelmed by low priority alerts. With a unified risk degree that extends over all sources of institutions data, security teams acquire a greater vision and monitoring, which leads to faster and more enlightened decisions.

In the era of increasing data violations, how can AI’s safety solutions help prevent internal threats?

Internal threats are especially difficult security risks due to their exact nature and access to employees. UEBA discovers for “Detection of deviations about fixed behavioral foundation lines, identifying risky activities such as accessing unauthorized data, extraordinary login times, and misusement. From discovering the internal threats and alleviating them prematurely before they are escalating to violations. Another link that gives these ammunition teams to build quickly and defend the state of evidence so that business can respond and deal with it before the data nomination occurs.

How to enhance the solution of the security identity analysis compared to the traditional IAM tools (identity and access)?

Traditional IAM solutions focus on control and ratification, but it lacks intelligence and vision to detect exposed accounts or abuse of concession in actual time. The detection of these restrictions by taking advantage of behavioral analyzes to evaluate the user risks continuously exceeds, adjusting risk degrees dynamically, imposing adaptive access benefits, and reducing misuse and illegal privileges. By integrating with the current IAM frameworks and accessing the minimum return, our solution enhances the safety of identity and reduces the surface of the attack. The IAM governance problem is the extension of the identity system and the lack of interconnection between different identity systems. Gurucul gives the 360 ​​-degree difference to the risk of identity through all the identity infrastructure. Now they can stop accessing the rubber seal, but they follow a approach directed towards risk to reach policies. Moreover, they can accelerate as well as compliance with IAM, show continuous monitoring and completely total approach to access to controls throughout the organization.

What are the main threats of cybersecurity that you expect in the next five years, and how can artificial intelligence help reduce it?

Identity -based threats will continue to relapse, as they have worked. The litigants will return to the bottom upon access to access by logging in either by bargaining on the informed or attacking the identity infrastructure. The internal threats will remain naturally heading to the risks of many companies, especially as Shadow continues. Whether it is harmful or negligent, companies will grow increasingly to see risks from the inside. Moreover, artificial intelligence will accelerate traditional TTPS differences, because opponents know that this is what they will be able to evade discoveries by doing this and the low cost for them will be for them to tactics, technologies and creative protocols. Thus again, why will the focus on behavior in the context and the presence of detection systems will be able to adapt at the same speed is extremely important in the foreseeable future.

Thank you for the wonderful interview, readers who want to know more, visit Gurucul.