84% of company codebases contained at least one vulnerability in the open-source software used, and 74% of them contains high-risk vulnerabilities, according to Synopsys’s ninth edition of the annual “Open Source Security and Risk Analysis” report. As organizations increasingly rely on AI models to identify and manage security risks in their systems, a critical question arises: how can we be sure these systems are making informed data-driven decisions, especially when the stakes are high? While AI excels at identifying potential risks, many machine learning models operate as ‘black boxes,’ flagging anomalies without clear explanations. When an AI flags a critical vulnerability, we need to understand why. What specific patterns triggered the alert? Are there more critical, overlooked issues? Thus, it’s vital to build transparency within systems for effective incident response, system optimization, and, ultimately, establishing confidence in AI-driven security. This talk will teach us how explainable AI techniques can illuminate these ‘black box’ decisions through real-world use cases within WordPress ecosystem. We’ll learn strategies to build transparent, trustworthy, and resilient AI security systems for WordPress! The key takeaways from this talk would be :
- Understand practical strategies to enhance explainability, ensuring AI-driven security decisions are actionable and trustworthy within the WordPress ecosystem via live-demo and sample code.
- Learn how explainable AI techniques can help security teams understand “why” behind the black-box models and anomalies detected within WordPress plugins, themes, and custom code.