Gurucul, a global leader in security analytics and risk management, has officially announced the launch of its AI Insider Risk Management (AI-IRM) product, which focuses on expanding company’s award-winning insider threat solution to cover the industry’s first native AI Insider Analyst.
According to certain reports, the stated product extends autonomous triage, bias-free risk scoring, context-rich investigation, and human and AI collaboration, while simultaneously integrating response workflows directly into insider risk management operations.
To understand the significance of such a development, we must take into account the Cybersecurity Insiders’ 2024 Insider Threat Report, where it was revealed that 83% of organizations reported at least one insider attack in the past year. This happens to be the case, until now, teams have largely struggled with siloed tools, noisy alerts, resource constraints, and process bottlenecks that slowed remediation.
Against the given conundrum, AI-IRM brings forth a specialized mechanism which effectively combines advanced User and Entity Behavior Analytics (UEBA), Identity and Access Analytics (IdA), Intelligent Data Loss Prevention (DLP), and Native Automated Response (SOAR).
“Much like humans, AI can develop biases over time. Creating a system that is transparent and can be trusted is non-negotiable,” said Nilesh Dherange, CTO, Gurucul. “The benefit of a native AI-Insider Analyst is the cohesion it has, with 10+ years in developing insider risk machine learning detections. It’s trained on contextualized data and is continuously trained from historical cases, feedback and keeping a human-in-the-loop to validation process.”
Talk about the whole value proposition on a slightly deeper level, we begin from its promise to provide comprehensive insider threat content. This content is likely to come from pre-built pipelines, dashboards, detection models, watchlists, playbooks, risk indicators, and reports, each source seemingly expected to accelerate deployment and threat coverage.
Next up, we have an expansive use case coverage coming into play, coverage which spans across human users, non-human identities, AI agents, and state-sponsored threats with hundreds of out-of-the-box use cases and thousands of ML-driven detections.
Another detail worth a mention is rooted in the availability of an AI analyst & sme AI copilot. Here, users can cone expecting AI-powered alert triage and response trained on insider threat investigations that, on their part, empower faster, more accurate responses with automated incident narrative.
Joining that would be the prospect of flexible data ingestion You see, Gurucul’s AI-IRM arrives on the scene bearing an ability to ingest non-standard and enrichment data sources with ease, including federated search for data not ingested in the platform.
The technology in question further brings to your disposal an assortment of identity and access analytics. These analytics propose a rather proactive approach to reducing the identity threat surface, and at the same time, enforcing zero-trust principles.
Then, there is a facility committed to advanced UEBA and contextual UAM. In essence, self-learning behavioral analytics are effectively combined with user activity monitoring and a 360° timeline view to unlock deep context.
Users of Gurucul’s latest brainchild can also come expecting powerful contextual natural language search and threat hunting, something which spans AI-powered natural language search, federated queries, as well as retrospective analysis and replay. Alongside that, there are behavior-based templates and a built-in STUDIO that empowers analysts to build detections tailored to organizational risk.
Hold on, we still have a few bits left to unpack, considering we haven’t yet touched upon the prospect of risk data detection and prioritization. Thanks to a flexibility underlying infrastructure, the solution in question supports human risk scoring, patented risk aggregation, and early warning models.
We also haven’t touched upon a facility committed to comprehensive DLP coverage, as intelligent, DLP across all egress channels is available with cross-channel exfiltration detection and protection, leveraging bidirectional integrations with other enterprise security solutions.
“Gurucul empowers Insider Risk Management teams to move beyond fragmented point products with natively converged behavioral and identity-centric analytics, smart data protection, policy enforcement, and agentic AI that proactively surfaces and prevents real insider threats,” said Saryu Nayyar, CEO, Gurucul. “Our AI-Insider Analyst transforms the insider threat detection and response workflows by automating alert triage and response with human collaboration. This enables organizations to leverage our expansive use case library, enabling Day 0 coverage so the existing analyst team can focus on the high-risk investigations.”
