The increasing complexity of infrastructures, increased attack surfaces, and an abundance of data pose significant problems in protecting data and effectively identifying and addressing security risks. Cyber security firms have integrated the use of AI with conventional technologies like antivirus protection, data loss prevention, etc. ai in cyber security has given firms a more robust stand against cyber threats.
AI and machine learning (ML) enabled systems are becoming a primary focus of cybersecurity research development since they are becoming more and more necessary for cyber operations. According to a recent study, the market for cybersecurity solutions powered by artificial intelligence is expected to grow from $15 billion in 2021 to $135 billion by 2030.
How is AI in cyber security changing cyber security?
Intelligent algorithms can monitor irregularities in the network, identify new threats without clear indicators, and take appropriate action. It can also be used to link data from different verticals to assess network risks, and vulnerabilities, and identify the kind of security threats that are occurring.
AI algorithms can help make decisions more effectively by ranking and responding to data, especially in larger networks with more users. The ability to locate, classify, and combine data is a very valuable ability for lowering cyber security threats.
AI can track network activities in real time by analyzing data to detect unauthorized connections, unwanted communication attempts, illegal credential use, and brute force attempts at authentication and logging in. AI solutions can recognize shadow data and alert cybersecurity experts about the potential dangers of unauthorized access to sensitive information. This enables cyber-security providers to draw real-time statistical conclusions and prevent problems priorly.
Automated and flexible network-based tasks can be made possible with the use of AI and ML. Automation enables network analysis that delivers real-time insights on anomalies. It automatically updates the network, firewall, and anti-virus software layers of the security system in addition to performing cybersecurity diagnostics and forensics analysis.
AI-powered risk assessment may speed up alert inspections and filtering by an average of 55%. Using behavioral data for evaluating the potential risk of individual login attempts and authenticating users, AI in cyber security can assist in striking a balance between security and UX by streamlining access for confirmed users and cutting the overall cost of fraudulent activities by up to 90%.
One of the main causes of cybersecurity flaws is human error. As network systems are upgraded, replaced, or redesigned, responsive technologies may be able to assist teams in identifying and resolving problems. Teams that manually evaluate configuration security must juggle countless updates with routine daily support chores. Organizations could obtain immediate guidance on recently identified difficulties using intelligent, adaptive automation in cybersecurity. AI in cyber security has also enabled organizations with predictive defense systems.
