By augmenting the skills of their human security analysts with machine learning capabilities, organizations can boost the efficiency of their SOCs and stay ahead of evolving cyberthreats.
It's January 2019 and cybercriminals are stealing your customer data. How will you use AI to execute your incident response plan and master the basics to avoid future incidents?
Researchers have shown how generative adversarial networks (GANs) can be applied to cybersecurity tasks such as cracking passwords and identifying hidden data in high-quality images.
While some observers fear a Skynet-esque future of malicious, self-aware machines, Dudu Mimran envisions a world in which AI and cybersecurity work together to keep emerging threats in check.
Cisco's "2018 Annual Cybersecurity Report" found that burst attacks, cryptoworms and mobile threats are among security professionals' top concerns in 2018.
Generative adversarial networks are neural networks that compete in a game in which a generator attempts to fool a discriminator with examples that look similar to a training set.
Most organizations around the world lack a consistent incident response plan and thus are unprepared to manage the repercussions of a cyberattack, according to a recent Ponemon report.
By implementing orchestration and automation (O&A), security leaders can deliver the real-time threat intelligence their understaffed analyst teams need to punch above their weight.
A UBA solution powered by machine learning enables security teams to model normal behavior and track subtle changes in user activity to identify malicious insiders.
The traditional mission of security is evolving under the influence of several key trends regarding the functions, staffing, processes and core capabilities of the security operations center.