We are excited to share that Cyclops now supports natural language search, to further empower SecOps professionals and enable a quicker, more effective way to get actionable insights.
The ability to use natural language search enables SecOps professionals to swiftly and intelligently navigate through the vast volumes of data, alerts, and notifications that were gathered, normalized and correlated by the Cyclops platform. The addition of AI-powered natural language search not only accelerates the investigative process but also provides our users with a powerful tool to extract actionable insights promptly. This marks a pivotal moment in our commitment to delivering cutting-edge solutions, and we look forward to the positive impact it will have on strengthening cyber defenses.
Manual Data Analysis Is A Burdensome Challenge for SecOps
SecOps professionals often find themselves grappling with the daunting task of manually sifting through colossal amounts of data, a process that is undeniably painstaking and filled with challenges. The sheer volume of data generated by various cybersecurity tools and sources can overwhelm even the most seasoned security analysts. Manually combing through this extensive dataset not only consumes significant time but is also prone to human oversight, introducing the risk of missing critical indicators and potential threats. The labor-intensive nature of this task leads to delays in identifying and responding to security incidents, leaving organizations vulnerable to evolving cyber threats. As the complexity of the threat landscape continues to grow, the need for more efficient and intelligent approaches to data analysis becomes increasingly evident in order to bolster the effectiveness of SecOps teams.
The Uphill Battle of Uncovering True Risk Indicators
The sheer volume and diversity of data generated by various security tools create a complex and time-consuming task. SecOps teams need to navigate through disparate interfaces, execute multiple queries, and correlate information from different sources, often leading to a fragmented and labor-intensive process.
The difficulty intensifies as SecOps professionals attempt to reconcile data formats, structures, and contextual nuances across various platforms. Each security solution may present data in a unique way, making it challenging to establish a cohesive view of the overall security posture. The prevalence of false positives adds another layer of complexity. SecOps professionals must sift through a barrage of alerts, many of which may not represent actual risks, leading to wasted time and resources. The lack of automated contextualization further hampers the ability to discern the true severity of potential threats.
In essence, the manual gathering and analysis of data from diverse security sources demand significant time, expertise, and attention to detail from SecOps professionals. The current landscape brings up the need for advanced tools and technologies to sift through the noise, contextualize data, and unveil genuine risk indications amid the vast and intricate sea of security data.
Transforming SecOps with AI-Powered Natural Language Search
The ability to harness AI to search security data, and enabling SecOps professionals to use free text, revolutionizes the way we extract answers from vast datasets, alerts, and notifications, dramatically reducing the time-intensive process that typically spans hours or even days.
The act of searching for data is fundamental to acquiring knowledge and making informed decisions in various facets of life. The ability to freely search for data enables individuals to access diverse perspectives, get contexts, and review empirical evidence, fostering a comprehensive understanding of subjects. Informed decision-making relies on the availability and accessibility of relevant data, empowering individuals to weigh options, anticipate consequences, and choose paths aligned with their goals. Thus, the act of searching for data becomes a pivotal driver in the continuous pursuit of knowledge and the ability to make enlightened choices in the myriad aspects of life.
Cyclops gathers, normalizes and correlates a vast amount of security data, alerts, and notifications from your existing security tech stack. With natural language search powered by advanced AI, professionals can swiftly query and retrieve precise information. By understanding the context of queries and recognizing patterns in unstructured data, the technology allows for near-instantaneous access to critical insights. This efficiency not only accelerates the investigative process but also empowers cybersecurity teams to make informed decisions rapidly, enhancing overall responsiveness to emerging threats.
Watch this short video example of Cyclops Security responding to a user query in natural language:
The Advantages of Using LLM to Understand Natural Language
The Cyclops platform incorporates Large Language Models (LLM) to understand natural language. Using LLM to understand user input in free text, versus the use of query languages, brings forward the ability to bridge the gap between human language and machine understanding.
While query languages require users to formulate precise commands using predefined syntax, LLMs excel in interpreting natural language expressions with context and nuance. LLMs can comprehend the subtleties of natural language, including synonyms, variations, and linguistic intricacies, allowing for more flexible and intuitive interactions. Unlike query languages, which often necessitate a specific structure, LLMs enable users to express their queries in a more conversational manner, facilitating a more user-friendly and accessible experience. This advantage becomes particularly evident when dealing with unstructured data, where LLMs' adaptability enhances the efficiency and effectiveness of information retrieval and analysis in diverse applications.
By providing a robust platform that allows SecOps professionals to efficiently search and analyze security data, we not only facilitate quicker responses to evolving threats but also empower SecOps to gain critical insights and prioritize risk
Cyclops Three–Tiered Utilization of AI
In addition to the use of LLM for understanding natural language entered by users in the search window, the Cyclops Platform utilizes sophisticated AI algorithms in two other key functionalities:
- Automated Data Correlation: Cyclops leverages AI algorithms to automatically correlate data from various sources. By identifying patterns, relationships, and anomalies within datasets, automated data correlation enhances the efficiency of recognizing complex connections and dependencies that might be challenging for human analysis. This capability is particularly valuable for SecOps, where quick identification of correlated events can strengthen threat detection and prioritization of mitigation efforts.
- QueryIQ: Cyclops uses AI to provide suggestive queries. This functionality allows users to interact with systems in a more intuitive manner, receiving useful suggestions for queries based on their role and past queries. This not only enhances user experience and accelerates the querying process, but also broadens users' perspectives by bringing attention to aspects they might not have considered. Guided by the system's intelligent suggestions, users can discover new dimensions, uncover hidden correlations, and gain insights that may have otherwise gone unnoticed.
The combinations of automated data correlation capabilities, Cyclops QueryIQ and the use of LLM for understanding natural language entered by users in the search window makes the Cyclops platform a powerful tool that SecOps teams can easily adopt to improve their performance.
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