Optimize the Entire Security Operations Lifecycle Experience Using AI

The use of Artificial intelligence (AI) is an absolute game-changer in cybersecurity. Going beyond the hype and silver bullets offered by XDR and SOAR, AI is a major step forward. It will revolutionize the way SecOps teams can use security platforms, like SIEMs, to leverage massive amounts of data scale, reduce false positives, and detect and mitigate cyber threats in real-time.

Optimized Detection using Gurucul Sme AI

Traditional SIEMs rely on rule-based systems and have limited capabilities to detect known threats in real-time and rapidly identify new and emerging threats. Gurucul Generative AI, using the latest innovations in artificial intelligence and trained machine learning algorithms, analyzes large volumes of telemetry across logs, endpoints, networks, cloud infrastructure, and identity in real-time.

Gurucul’s AI, called Subject Matter Expert (SME) AI is trained to detect patterns and anomalies that may indicate a security breach, even in the absence of a known threat model, and build a set of threat content to establish the active threat. This reduces manual efforts and combines machine and human efforts to quickly identify and respond to potential security incidents.

While Gurucul is already a leader in delivering insider threat solutions to Fortune 100 organizations, we can enhance our capabilities using AI-based analytics. With our combination of award-winning UEBA and mature identity analytics capabilities, Gurucul’s AI algorithms can improve the detection of anomalous behavior that may indicate insider threats. This would include privileged access misuse or unusual data transfers to more accurately validate the activity as malicious.

Optimized Investigations using Gurucul Sme AI

Gurucul has already gone beyond traditional correlation and siloed analytics indicative of some solutions. Our model-chaining with link chain analysis can correlate across any type of telemetry and associated analytics models (i.e., endpoint, network, cloud, behavioral and identity analytics). This advanced capability improves investigations through the cross-validation and unification of relevant context to provide confirmation, while eliminating the ambiguity of whether an event is truly part of an attack campaign. This is a major step in reducing manual efforts during threat hunting and investigations.

With Sme AI, we leverage natural language requests to rapidly search and generate more relevant context. These capabilities extend across even multi-cloud and geographically distributed environments that are critical for incident response, investigations, and reporting for security operations teams.

As part of investigations, Gurucul Sme AI enables users to query external threat intelligence to get information about malicious entities and threats across specific industries. Gurucul Sme AI continuously monitors and analyzes global threat intelligence feeds in real-time to identify emerging threats and vulnerabilities that are being actively exploited. This improved set of contextual information is leveraged by the Gurucul Risk Engine to prioritize incidents more actively and build risk-prioritized responses based on a detailed understanding of the nature of the threat, its potential impact, and how various threats come together to execute an attack campaign.

Optimized Response using Gurucul Sme AI

Gurucul Sme AI can automate the response process for highly critical events that are the least disruptive to normal business operations, such as isolating affected systems, or blocking access from a compromised account. The precise response avoids broad-stroke actions like shutting off a network segment or access to a sensitive application. Organizations can drastically minimize Mean-time-to-Respond (MTTR), the time between threat detection and response, reducing the window of opportunity for attackers to damage or disrupt IT operations or steal sensitive data.


When done correctly, AI can accelerate detection, investigation and response time and remove manual efforts that slow down security teams. Instead, they are empowered to handle the volume and sophistication of advanced attacks and professional threat actors, even as they themselves use AI. Gurucul Sme AI can help less experienced security analysts to more easily gather varied and relevant context that is used for confirming the events and actions leading up to an attack. This lowers the burden on more senior analysts within security operations.

To Learn more contact us to understand how this technology can optimize your threat detection, investigation, and response programs. It’s an exciting new capability that will change the way your SOC analysts operate!

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About The Author

Sanjay RajaSanjay Raja, VP Product Marketing and Solutions, Gurucul

Sanjay brings over 20 years of experience in building, marketing and selling cyber security and networking solutions to enterprises, medium-to-small business, and managed service providers. Previously, Sanjay was VP of Marketing at Prevailion, a cyber intelligence startup. Sanjay has also several successful leadership roles in Marketing, Product Strategy, Alliances and Engineering at Digital Defense (acquired by Help Systems), Lumeta (acquired by Firemon), RSA (Netwitness), Cisco Systems, HP Enterprise Security, Crossbeam Systems, Arbor Networks, Top Layer Networks, Caw Networks (acquired by Spirent Communications), Nexsi Systems, 3Com, and Cabletron Systems. Sanjay holds a B.S.EE and an MBA from Worcester Polytechnic Institute. Sanjay is also a CISSP as well as Pragmatic Marketing certified.

Frequently Asked Questions

What is generative AI?

Generative AI refers to a subset of artificial intelligence techniques that involve the creation of new content, such as images, text, music, and more, by modeling patterns and structures from existing data. Unlike traditional AI systems that rely on explicit programming and predefined rules, generative AI utilizes machine learning algorithms, particularly deep learning models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to learn and replicate the underlying distribution of the input data. This enables the AI to generate novel and coherent outputs that often exhibit a high degree of realism and creativity, making it a powerful tool in various creative, artistic, and problem-solving domains.

Why is generative AI important to cyber security?

Generative AI holds significant importance in the realm of cybersecurity due to its capacity to model and predict potential threats, simulate attack scenarios, and devise effective defense strategies. By analyzing historical data and patterns of cyberattacks, generative AI can anticipate new attack vectors and vulnerabilities, aiding in proactive threat mitigation. Moreover, it can generate realistic synthetic data that mimics real network traffic, assisting in training more robust intrusion detection and prevention systems. Furthermore, generative AI can be used to create more sophisticated and diverse datasets for training machine learning models, enhancing the accuracy of anomaly detection and classification of malicious activities. Ultimately, generative AI empowers cybersecurity professionals to stay ahead of evolving threats and bolster the resilience of digital systems and networks.

How do malicious actors employ generative AI?

Malicious actors can harness generative AI to enhance the sophistication and potency of their cyberattacks. They can use this technology to automate the creation of convincing phishing emails, malware, and social engineering tactics that are tailored to specific targets. Generative AI enables the production of highly personalized and realistic content that increases the likelihood of successful infiltration or deception. Moreover, attackers can leverage generative algorithms to generate variations of malware that evade signature-based detection systems, making their malicious software harder to detect and mitigate. By constantly adapting and evolving their attack strategies using generative AI, malicious actors can effectively bypass traditional security measures and pose more significant challenges to defenders in the ever-evolving landscape of cyber threats.