K
Kathleen Martin
Guest
Artificial intelligence, along with machine learning and other deep analytics strategies, is seeing a resurgence among enterprise technology practitioners. Companies are using AI to do everything from improving customer experience to optimizing supply chains, but one of the most widespread use cases for AI is improving an organization's cybersecurity stance. That said, CIOs and other enterprise technology practitioners need to clearly understand where AI and ML can and can't assist cybersecurity initiatives. To clarify this, it makes sense to look at a range of use cases or scenarios in which AI and ML can be good fits. For a full list of potential cybersecurity scenarios, CIOs should have their teams assess against a standard framework; one of the best is the Mitre ATT&CK framework. A good way to get started assessing cybersecurity vulnerabilities is to use the Mitre ATT&CK framework to provide guidance around the types of attacks to which an enterprise may be vulnerable.
Cybersecurity's AI use cases
There are some scenarios where AI and ML stand out as highly effective cybersecurity techniques: Log analysis. AI is ideal for problems that require automated correlation and assessment of large volumes of data. The challenge for cybersecurity professionals is often to translate information (the output of device, network and system logs) into knowledge (security alerts). Human security analysts don't have the mental or physical bandwidth to process these high-volume data streams and determine which combinations of data points equate to security alerts or events.
AI tools can find commonalities across disparate data feeds and convert data points into actionable events for analysts, thereby reducing the time required to uncover and respond to attacks. Log analysis tools that rely on AI and ML include products from Splunk, SolarWinds and LogRhythm.
Continue reading: https://searchenterpriseai.techtarget.com/feature/Is-AI-a-boon-or-bane-for-cybersecurity
Cybersecurity's AI use cases
There are some scenarios where AI and ML stand out as highly effective cybersecurity techniques: Log analysis. AI is ideal for problems that require automated correlation and assessment of large volumes of data. The challenge for cybersecurity professionals is often to translate information (the output of device, network and system logs) into knowledge (security alerts). Human security analysts don't have the mental or physical bandwidth to process these high-volume data streams and determine which combinations of data points equate to security alerts or events.
AI tools can find commonalities across disparate data feeds and convert data points into actionable events for analysts, thereby reducing the time required to uncover and respond to attacks. Log analysis tools that rely on AI and ML include products from Splunk, SolarWinds and LogRhythm.
Continue reading: https://searchenterpriseai.techtarget.com/feature/Is-AI-a-boon-or-bane-for-cybersecurity
