Vendors are overusing the term Artificial Intelligence (AI). Chief Information Security Officers (CISOs) are interested in Machine Learning (ML) but are skeptical of AI. Security teams in companies receive too many alerts, and most of the signals go uninvestigated. Whereas ML uses augmented analytics to assist the security staff to decide which alert to investigate, find out the attacks that the defenses have missed and dig deep to the root of the severe problem.
ML can recognize the pattern of attacks from a collection of loosely related data faster than an analyst. It also tackles one of the most common problems in a company which is a shortage of security analysts. ML allows building smart systems which can decide on its own without being programmed. Humans could observe incoming client requests and flag malicious activities. ML can achieve the same action based on the data labeled by humans and then classify incoming requests on its own. The differentiating factor, however, humans can detect anomalies among thousands of data points, but an algorithm is capable of performing the same task for millions of requests.
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MLs main function in security is to flag anything unusual and re-route to humans for review. Algorithms predict malicious programs based on millions of feature sets after being trained on large datasets. These algorithms have evolved based on what they observe and training. On the other hand, AI lets the machine suggest or take action based on its models and observations. This practice is better in theory than in practice. Machine learning can stop malware before infecting the system. Whereas, conventional antivirus systems employ preventive measures after the attack takes place.
Automation has provided value in DevOps and cybersecurity leaders are embracing automation for cybersecurity. The same sluggish model will be used for AI in cybersecurity. As of now, AI in security has not evolved enough. ML has plenty of scope without AI’s pitfalls.