Security Evolution from Legacy to Advanced to ML or AI
Security Evolution from Legacy to Advanced to Machine Learning and Artificial Intelligence
The cybersecurity industry is seeing a new dawn with AI and ML. AI is not a new concept in computing. AI was first defined as computers being able to perform tasks similar to human intelligence in 1956. These tasks included learning, solving problems, making decisions and understanding and recognising speech. ML is a broad term that refers to computers’ ability to acquire new knowledge without human intervention. ML is a subset AI. It can take many forms such as reinforcement learning, deep learning, and Bayesian network. AI is poised for disruption in the cybersecurity space in many different ways. This could be the ultimate win against cyber criminals.
AI/ML is used in cybersecurity to deploy self-sufficient tools that can detect and stop or prevent threats without human intervention. The algorithm used to detect threats is trained by the tool and the data provided by the developers. An AI-powered security tool that detects threats will improve over its lifetime. Developers will provide a reference base for the security tool that can be used to determine what is normal and malicious. Before final deployment, the security tool will be exposed to insecure environments.
The system will continue to learn from the threats it encounters in the environment. It will also be targeted by hackers. These hacking attempts include trying to overload its processing power with malicious traffic or hacking. The tool will detect the most common hacking techniques used to breach systems or networks. It will detect password-cracking tools like Aircrack-ng on wireless network networks. It will also detect brute force attacks on login interfaces. Humans will play the main role in cybersecurity by updating the algorithms of the AI tools to increase their capabilities.
All threats can be eliminated by AI security systems. Conventional security systems are often unable to detect threats exploiting zero-day vulnerabilities. AI will ensure that malware cannot penetrate the AI system, even if it evolves and adapts new attack patterns. The system will inspect the code that is being executed by malware and predict its outcome. The AI system will stop the program’s execution if it is found to be harmful. Even if malware hides its code, the AI will monitor the execution pattern. It will be able stop the program’s execution if it attempts to perform malicious functions, such as altering sensitive data or the operating systems.
Already, it is predicted that AI will surpass human intelligence. It is possible that all cybersecurity roles will be moved from humans to AI systems in the near future. This is both a good and bad thing. Fortunately, AI systems that fail today are usually tolerable. Because AI systems are still limited in their operations, this is normal. A failure in the system might lead to AI surpassing human intelligence. It is possible that security systems will be able to reject input from humans because they are better than human intelligence.
An unreliable system may continue to operate without intervention. Both the perfectionist nature and good sides of AI will be beneficial. Current security systems aim to reduce the number of attacks that can be successful against a system. AI systems, however, work towards eliminating all threats. False-positive detection may not be taken seriously. They might be treated as positive detection, and cause disruptions in the harmless systems that are affected.