While AI in cybersecurity is still in its infancy, the inaugural Ai4 Cybersecurity conference was a great first step in advancing the discussion, especially as it relates to application security.
Cybersecurity AI is approaching maturity. Are you prepared to defend against a new wave adversarial machine learning risks?
As facial recognition technology advances, bad actors will have more biometric data with which to create deepfake images, videos, sounds and more. Is the security community prepared for this threat?
A recent Ponemon study revealed a set of habits and practices that the most cyber resilient organizations undertake, such as adopting automation.
A fusion center uses a wider set of data sources, collects data from both inside and outside the organization, and delivers it to the right people to help them respond and recover more efficiently.
The fourth annual "The Cyber Resilience Organization" study found that 23 percent of respondents are now using security automation.
Smartphones, tablets and cloud computing have been leading the way in the workplace until now, but the growing popularity of the internet of things could totally change the look of IT infrastructure.
Security researchers have discovered that the new SLUB backdoor is receiving attack commands from GitHub and relying on Slack for communicating with its attackers.
Deepfake videos that are indistinguishable from authentic videos are coming, and we can scarcely imagine what they'll be used for. We should start preparing for the worst.
Now that you've identified an AI solution and selected a suitable algorithm for your machine learning model, you're ready to measure the effectiveness of your security classifier.