Holistic Solution Combines Industry-Leading Security and Investigative Strategies with Artificial Intelligence Governance
Polaris Risk, a global corporate risk-management firm, announced a strategic partnership with Talisai—the creators of a cutting-edge, insider-threat prevention and monitoring platform—to deliver a holistic solution that combines industry-leading security and investigative strategies with a revolutionary machine-learning based artificial intelligence (AI) platform to identify and mitigate potential threats.
Together, Polaris Risk and Talisai will help organizations prevent, monitor and respond to the many forms of insider threats, offering comprehensive services that can be deployed fully or incrementally based on each organization’s specific needs. Catering to a wide variety of businesses, including healthcare, financial services and technology, the Polaris-Talisai offering includes background screenings, site inspections and assessments, cyber monitoring and analytics, dark web monitoring, data forensics, incident response and remediation as well as investigations.
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“The Polaris and Talisai partnership creates an optimal and dependable integration between advanced data analytics and human-driven process,” said Stephen Ward, CEO of Polaris Risk. “Our combined insider-threat services offer preventative and proactive risk management with an ability to continuously monitor threats. In the event of an incident, the platform enables seamless hand-off to investigators, presenting traceable rationales and evidence-driven recommendations.”
Powered by the Talisai platform, the advanced monitoring integrates and analyzes surveillance data from five key pillars of enterprise risk: communications, business transactions, corporate records, employees and customers. It then extrapolates data based on multiple analytic algorithms, providing a list of people at potential risk, along with a full-evidence chain, forensically explaining the rationales. This drastically improves productivity not only for cybersecurity analysts and investigators, but also compliance and audit personnel, all while being able to operate at scale.
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“Recent advancements in AI have exposed organizations to a new set of risks, particularly the issue of unknown algorithmic behavior with less human control,” said Joonho Lee, CEO of Talisai and former CIO of the Financial Institution Supervision, Federal Reserve Bank of New York. “Unlike traditional user-behavior analytics products, our solution enables advanced AI analysis while promoting more transparency and accountability in AI and the complex data environment.”
The parallel use of both machine-learning-based AI algorithms and analyst-designed deterministic algorithms provides an ability to continually train, self-monitor, cross-check, and enhance automated analysis. This approach ensures that the machine-learning algorithm is in fact learning the right things in its analysis while enabling the enterprise’s shift from point-in-time risk assessment to real-time risk management.
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