Cogility's AI Aims to Predict Insider Threats Before They Happen

IRVINE, CA – March 17, 2026 – As government agencies and corporations grapple with an increasingly complex and insidious threat landscape, Irvine-based Cogility Software is positioning its advanced artificial intelligence platform as a way to see into the future. The company is set to showcase its newly rebranded Cogynt.ai solution at the Insider Risk Summit West this week, promising a fundamental shift from reacting to security breaches to proactively preventing them.

The summit, held in Monterey, California, provides a timely backdrop for Cogility's presentation. The challenge of the "insider threat"—a trusted employee, contractor, or partner who exploits their legitimate access for malicious or unintentional harm—has become a paramount concern for security leaders. Traditional security tools, often focused on external attacks and network perimeters, frequently fail to detect the subtle behavioral precursors to data theft, sabotage, or espionage.

The Behavioral Blind Spot in Security

For years, the cybersecurity industry has been locked in a reactive cycle. A breach occurs, an investigation follows, and new rules are written to prevent that specific attack from happening again. However, this approach leaves organizations perpetually one step behind. A recent 2025 survey on insider risk underscored this vulnerability, revealing that a staggering 77% of cybersecurity leaders lack confidence in their ability to proactively detect insider threats before damage is done.

The core of the problem, experts argue, is a failure to understand human context. Security systems that only monitor for technical red flags—like a large file download—miss the broader patterns of behavior that can signal growing risk. This is where the industry is seeing a push towards a "Whole Person" methodology, an approach that integrates both technical data and behavioral indicators, such as HR information, performance reviews, or even external financial stressors, to build a more complete risk profile.

"Organizations are drowning in data but starving for intelligence," noted a senior cybersecurity analyst who covers the sector. "The challenge isn't a lack of alerts; it's the inability to connect disparate events over time into a coherent narrative that reveals intent. That's the holy grail for insider risk management."

From Reactive Alerts to Predictive Intelligence

Cogility asserts that its Cogynt.ai platform is designed to find that grail. The solution moves beyond simple event detection to what the company calls "decision intelligence." At its heart is a patented technology named Hierarchical Complex Event Processing (HCEP). This Expert AI engine is engineered to continuously monitor and analyze massive, diverse data streams in near real-time.

Unlike traditional systems that might flag a single suspicious action, HCEP is designed to recognize and track multifaceted patterns of behavior as they evolve, even over long periods. It fuses data from technical sources (network logs, file access) with behavioral sources to understand not just what a user did, but to build a model that helps predict why. This allows the system to assess risk and, crucially, how that risk decays or escalates over time. For instance, it can differentiate between an employee innocently gathering files for a new project and one methodically siphoning intellectual property ahead of a resignation.

A key feature highlighted by the company is its no-code authoring environment. This allows an organization's own subject matter experts—the analysts and investigators who intimately understand their specific risks—to build and refine the AI models directly, without needing to write a single line of code. This dramatically accelerates the ability to adapt the system to new and emerging threats.

The Business Case for Proactive Prevention

Beyond the technological sophistication, Cogility is emphasizing the tangible business benefits of its platform. The company claims that customers have realized a 72% reduction in engineering resources and costs associated with developing and deploying decision intelligence applications. This significant return on investment is attributed directly to the no-code platform, which reduces the reliance on specialized and expensive data science and software engineering teams to translate security logic into functional code.

By empowering existing security analysts to become model builders, organizations can become more agile and self-sufficient. This not only saves money but also shortens the time between identifying a new risk pattern and deploying a countermeasure. In high-consequence environments, this speed can be the difference between a minor policy intervention and a catastrophic breach.

The shift to a proactive model also has profound implications for organizational resilience and compliance. By identifying at-risk individuals early, organizations can employ positive mitigation strategies—such as training, counseling, or access adjustments—rather than waiting for a fireable offense to occur. This approach not only protects assets but also fosters a more supportive and secure corporate culture, reducing the risk of costly litigation and reputational damage that inevitably follow a major insider-caused incident.

Securing the Nation's Most Critical Assets

The importance of this advanced approach is amplified when applied to critical infrastructure. Cogility's Chief Behavior Scientist, Dr. Frank L. Greitzer, is scheduled to speak on a panel at the summit focusing specifically on securing the energy and utilities sector. Dr. Greitzer, whose background includes two decades as a Chief Scientist at the U.S. Department of Energy's Pacific Northwest National Laboratory (PNNL), is a leading voice on applying behavioral science to cybersecurity.

His work has long focused on modeling human behavior to anticipate malicious attacks, making him uniquely qualified to discuss the nuanced threats facing sectors where an insider incident could trigger cascading failures across the economy and impact national security. His research into "risk decay"—understanding when a potential threat is no longer a threat—is particularly relevant, as it helps security teams focus their limited resources on the most pressing risks.

The participation of experts like Dr. Greitzer underscores a broader trend: the convergence of cognitive science and artificial intelligence to solve complex security challenges. As adversaries, both internal and external, become more sophisticated, the tools used to defend against them must evolve beyond simple rules and alerts. They must begin to understand the most unpredictable element in any system: the human being. By aiming to decode human intent from digital footprints, Cogility and others in this space are not just building better security tools; they are forging a new paradigm for how organizations manage risk in the digital age.