The use of Artificial Intelligence (AI) in a number of technologies has proven to have positive and negative effects on the field of cyber security in various ways. We each see how deep-fake data is generated by AI to deceive, rob, or swindle people. Businesses are increasingly abusing AI technologies to launch large-scale DOS (Denial of Service) assaults, carry out complex cyberattacks through social engineering, and create self-managed malware that may evade detection.
These and other harmful assault techniques are starting to cost corporations money. On the global stage, it is clear that modern warfare uses cutting-edge technologies to address geopolitical issues. While AI-based technologies are on the one hand a cause for concern, they can help in the fight against cybercrime if applied properly. The use of AI needs to be more thoroughly incorporated in three areas if we want to improve our overall cybersecurity objectives.
1. AI-enhanced cybersecurity tools:
Virtually every security product available today, such as firewalls, content filtering, intrusion prevention/detection systems, deception technologies, endpoint protection tools, SIEM, DLP, and a lengthy list of other items, can incorporate artificial intelligence (AI). Thanks to branches of AI including machine learning (ML), deep learning (DL), and natural language processing (NLP), the use cases, built-in responses to anomaly detection, and detection accuracy of these products have all considerably increased. Finding rare anomalies that could also be zero-day vulnerabilities, examining subtle behavioral patterns to spot the smallest hazard, and creating predictive intelligence through data scraping are some of the more recent uses. Additional advancements in this field need to be made with the appropriate investment in research and testing to fully realize the benefits of progress.
2. Automation powered by AI for security and legal compliance:
While AI can be used to identify novel threats, weaknesses, or behaviors that are not yet known, these risks eventually become well-known and have a standard curve. With the use of AI-driven automation tools or AI built into already-existing automation technologies, these mitigating responses can be carried out without or with limited human involvement. Through the use of tools like Security Orchestration, Automation, and Response (SOAR), automation powered by AI is already automating threat responses that call for low-skilled triage. Now, compliance procedures and IT operational tasks that support preventive security can be automated with the use of robotic process automation (RPA) platforms, an alternative to SOAR systems. AI-powered automation software is becoming easier to use with the help of low-code and no-code platforms, which is increasing IT and business productivity.
3. AI-based security use cases integrated into company infrastructure or business software:
We see the use of AI technologies like ML, DL, NLP, speech recognition, and image processing in commercial applications. We are also witnessing the fusion of AI with IT infrastructure, including SD-WAN, Edge Computing, etc. These solutions won’t benefit from traditional security methods because many of them diverge from the traditional enterprise architecture. The secret to securing new technologies is identifying potential misuse use cases and integrating native safety measures into the solutions.
