AI-based security solutions not only crack complex problems, they also positively contribute to solving human resources issues in SecOps environments, application development and security testing. As most organizations will have experienced, cybersecurity is suffering from a huge resource scarcity, with millions more professionals needed. Therefore, people’s time must be used more effectively, with a focus on the most demanding and interesting analysis.
Using AI, machines can be trained to identify unnecessary events, such as false positives, and free up SOC analysts to concentrate on real incidents. This has a twofold impact: first, clearly, a solution embedding AI should be more productive; and second, AI helps to improve job satisfaction. How? An analyst concentrating on more interesting tasks without wasting energy looking for a needle in a haystack is generally more motivated to complete the “mission” at hand.
In many cases, the quickly evolving cybersecurity threat landscape leaves organizations floundering about what to do. None want to be blamed (publicly) for failing to take the right measures, so they keep adding layers of defensive software, encumbering their software stack, and slowing down their products. This problem worsens as the attack surface is extended with software and applications that are not securely coded, with the result that it is not uncommon for dozens of security bugs to be introduced per 1,000 lines of source code.
Considering the existing hundreds of millions of source code lines that are likely to host security “anomalies”, this is a big challenge for security professionals and we would not expect them to review every single line of code. Nor do we believe all coders will become 100% secure coders. Here again, we see a case for machines learning models. They can now detect over 90% of security bugs as well as analyze the quality of the code produced by developers and guide them to correct the coding errors. They can also teach them how to avoid these errors. This is particularly interesting in a DevOps cycle where, for example, an AI-powered API security testing tool can be integrated within the CI/CD pipeline tools, its workflows and processes, minimizing the impact on the key stakeholders’ velocity, from developers to security experts.