CISOs point out that machine learning and AI tools are not exactly the silver bullet for fighting cybercrimes as they were initially considered to be
Security leaders believe that it is high time that security teams adopt AI steps to protect sensitive data as hostile actors and cybercriminals increasingly try to implement technology for nefarious activities. Machine learning and AI has made it easier for internet users and threat actors to use AI to launch multiple cyber-attacks simultaneously.
The above scenario is yet another battleground for the continuing war for increased control over digital architecture. Luckily, it is a battle that AI defenders have prepared long for. The first known cyber-attack incident was the Morris Worm malware that infected devices in 1988. In the present world, cyber-attacks are ranked alongside climate change and natural disasters by the World Economic Forum in the annual list of gravest threats for the global society.
When ML and AI came into security measures, cybersecurity strategies became more powerful and effective. However, it has become easier for threat actors to break into corporate networks and computer systems by employing AI and ML.
As technology continues to grow, the use of tech to orchestrate AI-based hacking will become commonplace. AI-based tools like facial recognition, ML platforms, text analytics has helped transform major aspects of the business right from customer engagement to security profiles.
Offensive AI as a consistent pattern of a shift in cyberattacks
CISOs acknowledge that cyberattacks have become commonplace, and it is obvious that AI will help change the very nature of it. Enterprises are aware that all industries are susceptible to cyber-attacks, specifically now, as the sophistication of the attacks has drastically increased.
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The above situation is boosted because computer systems capable of acting with reasoning, and learning, are still in the initial stages of development. Moreover, ML requires enormous data sets, and for real-world systems, a complex blend of physical sensors, complicated programming needed for decision-making, and robotics is needed.
Deployment might seem easier for enterprises that adopt AI, providing access to AI for data, and authorizing any level of autonomy brings forward a huge array of critical risks that must be seriously considered.
Risks possessed by AI
Security leaders say AI tools launching attacks will mimic human behavior. It can automate intrusive and disruptive activities by launching attacks at an unimaginable speed. After accurate and automatic profiling of various targets’ interaction patterns, the tool can launch artificially based phishing attacks that mimic the original one.
In addition to this, AI-based malware can travel easily through the enterprise by implementing ML to analyze internal systems without getting caught. Network traffic analysis can help such attacks blend their interactions easily with random communications existing on the network, hiding in plain sight.
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Security leaders believe that most AI cyberattacks in the future will use model theft, training data poisoning, and other methods to breach ML-based systems. Despite overwhelming evidence, industry practitioners are yet to fully accept that machine learning can be used adversely.
