For purposes of scientific discovery, the field of insider-threat detection often lacks sufficient amounts of time-series training data. Moreover, the limited data that are available are quite noisy. For instance, Greitzer
Mitigating Insider Threats Using Bayesian Models
Trusted insiders can harm an enterprise in all kinds of ways, from intellectual property theft, financial fraud and data breaches to espionage, sabotage and even terrorism. Moreover, the root causes
Machine Learning: Expertise vs. Coverage
Critics of machine learning (ML) often point out that it can’t come close to emulating a subject-matter expert working at his or her maximum potential. Sure, ML powers our smart
Using AI to Extract High-Value Threat Intel from Data
Today’s security and risk analysts have access to oceans of raw data, thanks to a proliferation of information sources, drastically improved computing power and dirt-cheap storage. Paradoxically, though, they’re having
UBA Is Just Getting Warmed Up
Anyone who has worked long enough in the data analytics and high-tech industries will have a favorite story about some new technology that was subjected to a degree of hype
Beyond Machine Learning: Using Models in AI for Security
Some impressive people have said bearish things recently about the use of artificial intelligence (AI) in cybersecurity. A recent example is Heather Adkins, who for 15 years has been director
More Organizations Adopting UBA, Gartner Says
Enterprises are increasingly turning to user behavior analytics (UBA) for an array of security missions, as they confront ever-more sophisticated external threats and the possibility that even their most trusted
New SANS, Haystax Technology Insider Threat Survey Reveals Malicious Actors as the Most Damaging Threat Vector for Companies
Haystax Technology and SANS today released an industry survey titled “Defending Against the Wrong Enemy: 2017 SANS Insider Threat Survey” that illustrates the importance of managing internal threats to win
