AI augmentation in security software

March 2024 Security Services & Risk Management, AI & Data Analytics

Paul Meyer

If you missed it, the first article in this series can be found at

Static Application Security Testing (SAST) is defined as the products and services that analyse application source byte, or binary code for security vulnerabilities. These tools are one of the last lines of defence to eliminate software security vulnerabilities during application development or after deployment.

These software security tools and services report weaknesses in source code that can lead to vulnerabilities that can be exploited, which can, of course, lead to a breach and subsequent damage to your organisation. SAST enables companies to assess and transform their security posture.

This paradigm shift in static analysis dramatically increases return on investment as the time and cost of audit results decrease substantially. Rather than reducing the breadth of security issues, scan analytics platforms can distinguish non-issues from the real thing, and they can do this automatically. This innovative approach utilises big data analytics to scale secure software assurance to the enterprise without sacrificing scan depth or integrity.

Automated application security must be built into their processes, especially as businesses transition to DevSecOps environments. Automation reduces the repetitive, time-consuming work of issue review through the scan analytics platform.

Human intervention is still needed

SAST reports categorise issues in terms of criticality, but they must then be manually confirmed by expert application security auditors as either exploitable or not a problem. These specialists are required to validate findings using details specific to the enterprise, such as the context of an application and its deployment. Time-consuming audits have traditionally come at a significant cost to businesses and expose fundamental challenges attached to delivering secure applications.

The much-reported cybersecurity skills gap adds to the challenge of software security assurance. Static analysis tools make the impossible job of securing code possible, and a skilled auditor’s software security expertise verifies actionable findings. Even the best security teams are ultimately limited by the human experience available. However, this often pales into insignificance compared to the innumerable software flaws companies can be exposed to.

This is where machine learning (ML) comes to the fore with the next evolution of applications making the process of securing developing ones quick and efficient. These techniques extend the reach and better scale the expertise of security professionals through the entire development lifecycle.

ML coupled with predictive analytics - the next generation of SAST - provides actionable intelligence with problems being assessed by scan analytics platforms.

AI-driven static analysis and ML vastly increase speed and accuracy in application security by running thousands of static, dynamic, and mobile scans per week, scanning billions of lines of code. The scan results are then passed to a team of expert auditors who identify and prioritise the findings.

In this way, it is possible to confidently assess the vulnerability and complexity of threats and determine how issues must be categorised, for example, labelling them as exploitable, indeterminable, or not a problem.

Through ML technologies, a company’s application security program can become more efficient and effective without expanding headcount or allocating additional budget. This shift in SAST from scarce human expertise to the limitless scalability of AI can reduce non-issue findings by up to 90 percent. 


Enterprises no longer need to accept noisy scan results, nor do they need to make trade-offs between scan comprehensiveness and time-to-audit. It is also not necessary to negatively impact product delivery dates with scan review time. Classifiers trained on anonymous issue metrics can reduce the expense of software security assurance programs without the risk of identifiable data being transmitted to the cloud. Today, it is possible for businesses to reduce their overall security workload through vulnerability prediction software.

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