Machine Learning Revolutionizes Cybersecurity; Detecting, Preventing Threats

By Sunil Sonkar
2 Min Read
Machine Learning Revolutionizes Cybersecurity; Detecting, Preventing Threats

Cybersecurity is highly critical and it is assumed the threats to continue evolving and growing. Organizations are turning to advanced technologies like artificial intelligence (AI) and machine learning (ML) to combat the threats. The technologies are revolutionizing how we detect as well as prevent cyber attacks. The technologies are offering innovative solutions and these can enhance our cybersecurity defenses.


AI and ML are powerful tools. These have the capabilities to fight against cyber threats due to their analyzing capabilities of vast amounts of data quickly as well as accurately. The two technologies can detect patterns and anomalies that might indicate a cyber attack. Behavioral analysis is one of the ways to serve the purpose. The tools learn the normal behavior patterns of users and devices within a network, the so-called User and Entity Behavior Analytics (UEBA).

One another way is through network traffic analysis. AI and ML monitor network traffic for unusual patterns like unexpected data transfers or communication. The method helps in identifying potential threats before significant damage takes place. Moreover, deception technology can trick attackers into revealing themselves.

AI and ML also automate defensive responses to detected threats. ML algorithms are trained on large datasets of malware and hence can identify as well as neutralize malware variants with high accuracy. AI systems can analyze emails and websites to detect phishing attempts. It can block the malware variants. Modern intrusion detection systems (IDS) use AI to analyze network traffic.

Security Orchestration, Automation and Response (SOAR) platforms integrate AI and ML to automate incident response workflows. These can automatically isolate infected systems, block malicious traffic and initiate other counter-measures. Hence, the response time is quick and reduces the burden on human analysts.

Automated patch management analyzes vulnerability data and prioritize patching efforts based on risk levels. AI ensures that critical vulnerabilities are addressed promptly and simultaneously reduces the opportunity for attackers.

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