Webinar Description
Key Takeaways
- Group-IB presents Prevyn AI, a cognitive core designed to autonomously conduct multi-step threat investigations
- The webinar addresses limitations of single-step AI assistants in security operations
- Live demonstrations cover the Graph Agent and IMC Incident Analyzer capabilities
- Relevant for CTI analysts, SOC teams, vulnerability management professionals and security leaders
- Focus areas include Threat Intelligence, Managed XDR and Attack Surface Management
Introduction
Group-IB is hosting a technical webinar examining Prevyn AI, the cognitive core embedded within its Unified Risk Platform. The session targets cybersecurity professionals seeking to understand how autonomous AI systems differ from conventional AI assistants in threat investigation workflows. As security teams face mounting pressure to reduce response times while managing increasingly sophisticated threat landscapes, the distinction between AI tools that assist with individual tasks and those capable of conducting end-to-end investigations has become operationally significant.
About This Event
This virtual webinar provides an in-depth exploration of how Prevyn AI functions within Group-IB’s security ecosystem. The session combines technical explanation with live demonstrations, making it suitable for both hands-on analysts and security executives evaluating AI-driven investigation capabilities. Attendees will observe the platform’s autonomous research functions in action and learn how the system integrates with Threat Intelligence and Managed XDR workflows.
The presentation introduces several platform components, including the Graph Agent for mapping threat ecosystems and the IMC Incident Analyzer for structured incident examination. Group-IB positions these tools as mechanisms for closing the gap between threat detection and actionable response, a persistent challenge in security operations where alert fatigue and investigation backlogs can delay critical decisions.
Autonomous Investigation Versus Single-Step AI Assistance
A central theme of the webinar is the distinction between AI systems that handle discrete tasks and those designed to execute multi-step investigative processes. Many AI implementations in cybersecurity focus on accelerating specific analyst activities, such as query generation, log summarisation or alert triage. While these capabilities reduce manual effort, they still require human analysts to orchestrate the overall investigation, connecting findings across data sources and determining subsequent actions.
Prevyn AI is presented as a cognitive core that reasons across the Unified Risk Platform, autonomously progressing through investigation stages rather than waiting for analyst direction at each step. This approach aims to address what Group-IB describes as the execution gap, the delay between identifying a potential threat and taking decisive action. The system maintains analyst oversight by making each step reviewable, preserving human control while automating the investigative sequence.
The practical implications of this architecture extend beyond efficiency gains. When AI systems can independently traverse threat data, correlate indicators and surface relevant context, analysts can focus on validation and strategic decision-making rather than manual data gathering. This shift becomes particularly relevant as threat intelligence volumes grow and attack surfaces expand across cloud, endpoint and network environments.
Platform Components and Intelligence Infrastructure
The webinar covers several interconnected elements of Group-IB’s technology stack. The Unified Risk Platform serves as the foundation, integrating Threat Intelligence, Managed XDR and Attack Surface Management into a consolidated environment. Prevyn AI operates across these domains, drawing on what Group-IB describes as over twenty years of proprietary investigation data stored within its Intelligence Data Lake.
Access to proprietary intelligence distinguishes vendor-specific AI implementations from general-purpose tools trained on publicly available data. Threat intelligence platforms accumulate historical incident data, adversary tactics, infrastructure mappings and attribution research that public datasets cannot replicate. When AI systems can query this accumulated knowledge during investigations, they can surface connections and context that would otherwise require extensive manual research or remain undiscovered.
The Graph Agent, one of the features demonstrated during the session, focuses on mapping threat ecosystems. Graph-based analysis has become increasingly important in threat intelligence, as adversary infrastructure, malware families and campaign relationships form complex networks that linear investigation methods struggle to navigate efficiently. Automated graph traversal can reveal indirect connections between indicators, potentially exposing broader campaign infrastructure from limited initial data points.
Industry Context: AI Integration in Security Operations
The cybersecurity industry has seen substantial investment in AI-augmented security tools over recent years, with vendors across the market incorporating machine learning and natural language capabilities into their platforms. However, implementation approaches vary significantly. Some focus on detection enhancement, using AI to identify anomalies or classify threats. Others target analyst productivity, automating report generation or simplifying query construction.
The concept of autonomous investigation represents a more ambitious application, requiring AI systems to maintain context across multiple analytical steps, make reasoned decisions about investigation direction and synthesise findings into actionable conclusions. This capability depends not only on sophisticated AI models but also on deep integration with underlying security data and well-structured investigation frameworks.
Security teams evaluating these technologies must consider how autonomous capabilities align with their operational workflows and governance requirements. The emphasis on analyst-reviewable steps in Prevyn AI reflects broader industry discussions about appropriate human oversight in AI-driven security decisions, particularly for actions that may affect production systems or trigger incident response procedures.
Who Should Attend
The webinar is designed for security professionals across multiple roles and seniority levels. Cyber Threat Intelligence analysts and threat hunters will find the autonomous investigation demonstrations directly relevant to their daily workflows. SOC analysts and incident responders can evaluate how the platform might accelerate their triage and investigation processes.
Vulnerability management and exposure teams may benefit from understanding how Attack Surface Management integrates with threat intelligence within the Unified Risk Platform. Risk managers and CISOs considering AI investments in their security programmes can use the session to assess one vendor’s approach to autonomous investigation and compare it against alternative solutions in the market.
Organisations already using Group-IB’s Threat Intelligence or Managed XDR solutions will gain insight into new platform capabilities and how Prevyn AI enhances their existing deployments. Those evaluating Group-IB as a potential vendor can observe the technology in action before making procurement decisions.
Operational Challenges Addressed
The webinar content addresses several persistent challenges in security operations. The gap between detection and response remains a critical concern, as organisations often possess the tools to identify threats but lack the capacity to investigate and act on alerts quickly enough to prevent damage. Analyst workload and skill shortages compound this problem, with many security teams unable to thoroughly investigate every alert they receive.
By automating multi-step investigations, platforms like Prevyn AI aim to multiply analyst effectiveness, allowing smaller teams to handle larger alert volumes without sacrificing investigation depth. The integration of proprietary intelligence data further supports this goal by reducing the research burden on individual analysts, who might otherwise need to consult multiple external sources to build context around an indicator or threat actor.
For organisations maturing their threat intelligence programmes, the session offers perspective on how AI can enhance intelligence consumption and operationalisation, transforming raw threat data into actionable guidance more rapidly than manual processes allow.