Threat Detection
With the help of AI, threat detection has become faster and more accurate, enabling responses to millions of attempts instead of just tens or hundreds. AI can detect and respond to threats on a massive scale, making it possible to identify and address risks on a much larger level.
A great example of this is the 2017 Wimbledon. An IBM Security report revealed 200 million breach and threat attempts during just two weeks of the tennis tournament. Without AI, detecting and responding to such massive attacks would have been impossible.
Behavioral Biometrics
The advancement of cybersecurity can sometimes become an inconvenience for system users and limit their access. Fortunately, AI-powered cybersecurity measures can help detect abnormal user behavior and minimize such occurrences.
Using behavioral biometrics, AI can determine whether a single authorization is enough or if more steps are needed for additional security. It can even flag specific logins or actions for security team monitoring or block them altogether. Without this technology, users would have to go through multiple authorization methods every time they log in.
Code and Library Security
One of the most promising areas of utilizing AI is cybersecurity during the initial phases of application development. By leveraging AI, developers can improve the quality and security of code and third-party libraries used in software development. This goes beyond traditional methods like static code analysis, unit tests, and automated end-to-end or penetration tests, resulting in higher overall security.
Fixing security issues early on in the development process can help avoid costly and time-consuming fixes later on. Additionally, it ensures that the application meets industry-standard security requirements, providing peace of mind.
Potential Risks and Challenges
AI is undoubtedly a powerful tool in the fight against cyber threats, but it has its challenges and risks.
One major issue is the possibility of false positives or negatives – errors that can occur in automated threat detection systems. False positives happen when a system incorrectly identifies a harmless event as a security threat, while false negatives happen when a system misses a genuine security threat. Both cases can lead to a loss of trust in the security system and may result in missed or wasted resources.
Moreover, if not properly secured, AI-based systems can be attacked by cybercriminals. And there’s a danger that they could use AI to launch more advanced attacks, making it harder to detect and thwart threats.
Conclusion
As AI becomes more prevalent in cybersecurity, it’s essential for us humans to remain vigilant and partner with it to boost our security efforts. We should carefully review any code AI enhances to ensure accuracy and reliability. Relying solely on AI’s coding without human supervision is risky, as both humans and AI can make mistakes. It’s called AI hallucination, and it’s a risk we can’t ignore. Let’s work together to protect our online world and ensure that it is secure for all of us.