Multi-stage attacks use diverse and distributed methods to circumvent existing defenses and evade detection - spanning endpoints, networks, email and other vectors in an attempt to land and expand. Meanwhile, individual tools including DLP, EDR, CASBs, email security and advanced threat protection are only designed to...
Organizations may have great cybersecurity intentions, but translating those desires into a robust security reality is often challenging, says Ratinder Ahuja, CEO of ShieldX Networks. That's why he advocates automation to ensure intention equals reality.
Machine learning systems adapt their behavior on the basis of a feedback loop, so they can overlearn and develop blind spots, which if not understood by practitioners can lead to dangerous situations, says Sam Curry of Cybereason.
Automation is the first step toward full-blown machine learning and artificial intelligence. But unfortunately, automation already is being weaponized for malicious purposes, says Fortinet's Derek Manky.
In a keynote address at the RSA Conference 2019, RSA President Rohit Ghai encouraged attendees to work in the coming years to "implement a security program with machines and humans working together. Humans asking questions; machines hunting answers."
As the use of artificial intelligence tools and robotics continues to grow, it's crucial for organizations to assess the potential security risks posed, says attorney Stephen Wu, who reviews key issues in an interview.
Analytics, artificial intelligence and machine learning are increasingly playing promising roles in healthcare data security, say Ron Mehring, CISO at Texas Health Resources, a large delivery system, and Axel Wirth of Symantec, a technology vendor. They were featured speakers at the HIMSS19 conference.