How AI Can Transform Cybersecurity Strategies and Challenges?

AI is a fast-growing and exciting field that can help improve our overall cybersecurity

The application of Artificial Intelligence (AI) via a variety of technologies has shown to be both a blessing and a curse for the field of cyber security in many ways. Individually, we observe how deep-fake data is produced by AI in order to trick, rob, or scam people. Businesses are increasingly using AI technologies improperly to conduct sophisticated cyberattacks through social engineering, launch massive DOS (Denial of Service) attacks, or develop self-managed malware that can avoid detection.

The costs of malicious attack methods like these and others are starting to add up for businesses. The use of cutting-edge technologies in contemporary warfare to address geopolitical concerns is evident on the international scene. While AI-based technologies are on the one hand a source of worry, if used properly, they can aid in the battle against cybercrime. To enhance our overall cybersecurity plans, the usage of AI needs to be more deeply integrated in three areas.

1. AI-enhanced cybersecurity tools: Almost any security solution on the market today can incorporate artificial intelligence (AI), including firewalls, content filtering, intrusion prevention/detection systems, deception technologies, endpoint protection tools, SIEM, DLP, and a long list of other products. The use cases, built-in reactions to anomaly detection, and detection accuracy of these products have all significantly improved thanks to the branches of AI like Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP). Some of the more recent uses include finding uncommon anomalies that might also be zero-day vulnerabilities, analyzing intricate behavioral patterns to identify the smallest danger, and developing predictive intelligence through data scraping. To maximize the advantages of improving, additional improvements in this area require the correct expenditures in research and testing.

2. Automation powered by AI for security and legal compliance: While AI can be used to uncover new dangers, vulnerabilities, or behaviors in the unknown, these risks' mitigating measures eventually become well-known and have a standard fix. These mitigating responses can be carried out without or with minimal human participation with the aid of AI-driven automation tools or AI embedded into already-existing automation technologies. Automation driven by AI is already automating threat responses that require low-skilled triage via solutions like Security Orchestration, Automation, and Response (SOAR). Robotic Process Automation (RPA) platforms, which are an alternative to SOAR systems, can now assist in automating compliance procedures and IT operational chores that aid in preventive security. Low-code and no-code platforms have made it simpler to employ AI-powered automation software, which is proving to increase IT and business productivity.

3. AI-based security use cases integrated into company infrastructure or business software: In commercial applications, we observe the employment of AI components like ML, DL, NLP, speech recognition, and image processing. The integration of AI with IT infrastructure, such as SD-WAN, Edge Computing, etc., is also something we are seeing. Since many of these solutions depart from the conventional enterprise architecture, they won't profit from conventional security techniques. Finding potential misuse use cases and incorporating native protection features into the solutions is the key to safeguarding new technologies.