Instead of replacing humans, machine learning will free up overworked IT analysts to focus on other tasks. However, the rising adoption of AI could expose companies to a new breed of insider threats.
As the threat landscape evolves to target connected devices, artificial intelligence (AI) and machine learning will become increasingly crucial parts of any organization's endpoint security strategy.
When the machine learning technology cyber defenders use to stop DDoS attacks inevitably falls into the hands of malicious actors, which side will win?
A recent study conducted by NIST researchers found that face identification performance is optimal when insights from a human expert are combined with a top machine learning algorithm.
While studies reveal the majority of CTI adopters are dissatisfied with threat intelligence machine learning adoption, there's evidence the adversary is already using algorithms to their advantage.
Artificial intelligence (AI) tools enable security teams to identify behavioral patterns that could point to insider threats more quickly.
IBM's new Intelligent Orchestration offering enables analysts to streamline their investigations via integrations and incident response playbooks.
At RSAC 2018, many speakers urged organizations to take the human element out of the security equation as much as possible by investing in automated tools and focusing on professional development.
As AI progresses, security professionals must prepare for the inevitability of machines writing their own malware to infect other machines in the not-so-distant future.
While fraudsters have yet to master adversarial AI, the only way for the security community to get ahead of the emerging threat is through collaborative defense.