Looking to integrate AI into your organization’s governance framework?
While there are no universally-lauded systems as of yet, there are several commercial AI-based threat detection and analysis systems available in the market today that might help you out. Here are just a few examples. Make sure to leave a comment if you want to share an opinion.
- IBM Watson for Cyber Security: IBM Watson leverages AI and machine learning to analyze vast amounts of structured and unstructured data from various sources, including security blogs, research papers, and news articles. It helps security analysts identify potential threats, understand their context, and prioritize incidents for investigation. IBM publishes annual cybersecurity reports and is a recognized leader in this sector.
- Darktrace: Darktrace utilizes unsupervised machine learning algorithms to detect and respond to cyber threats in real-time. Its AI technology learns the normal behavior of a network and identifies anomalies that could indicate cyber attacks, including insider threats, zero-day exploits, and advanced persistent threats (APTs). This produce promises to help from detection to repair and everything in between.
- CylancePROTECT: CylancePROTECT uses AI and machine learning to prevent malware and advanced threats. It employs a predictive model that analyzes file attributes and behavior to determine whether a file is malicious or safe, even if it has never been seen before. CylancePROTECT promises to be a complete endpoint security solution.
- Splunk Enterprise Security: Splunk Enterprise Security combines machine learning and analytics to provide a comprehensive security intelligence platform. It analyzes data from various sources, including logs, network traffic, and security events, to detect and respond to threats. It also offers features such as anomaly detection, correlation of events, and incident response automation. Splunk offers a free trial for those who might be interested in exploring it.
- Symantec Endpoint Protection: Symantec Endpoint Protection (SEP) utilizes machine learning algorithms to protect endpoints from a wide range of threats. It can detect and block known and unknown malware, including fileless attacks, using behavioral analysis and heuristics. SEP continuously learns and adapts to new threats, improving its detection capabilities over time. As a long-time industry leader in security, Symantec’s exploration of AI in cybersecurity looks to be promising.
These are just a few examples of commercial AI-based threat detection and analysis systems in cybersecurity. It’s important to note that the landscape is continually evolving, and new solutions are being developed as the field advances. Currently, there is no one-size-fits-all solution that will work for all businesses.
Organizations should evaluate different offerings based on their specific needs, industry requirements, and budgetary considerations. This is especially true for small and medium businesses, as they need cost-effective solutions that could make or break how they handle security incidents.