What are the steps to determine the appropriate CAL for a component?

To determine a CAL, assess the component's risk profile based on its functionality, exposure to threats, and potential impact of a breach. Then, choose a CAL that aligns with this risk level and the manufacturer's overall cybersecurity strategy.

How often should CALs be reviewed or updated?

CALs should be reviewed regularly, especially when there are significant changes in the threat landscape, technology, or operational environment. An annual review is a common practice, but some situations may warrant more frequent assessments.

What is the risk of code generated by AI?

While AI-generated code can accelerate development, it also introduces new risks. This is because AI tools currently rely on the LLMs, which use general data and don't know the specifics of the application under test. Moreover, they are being used to produce much higher quantities of code which in turn will lead to more bugs and vulnerabilities. The reliance on AI can also lead to a reduced understanding of the codebase by human programmers and a false sense of security, as found in a study by Stanford University.

What is the issue with traditional static analysis and black-box testing?

Traditional methods of software security testing, like static analysis (SAST) and dynamic black-box testing (DAST), suffer from significant limitations. Starting with SAST, it lacks runtime context, meaning it cannot fully assess the actual behavior of the software during execution. Consequently, SAST often generates numerous false positives, leading to potential vulnerabilities being overlooked or ignored. On the other hand, DAST faces its own set of challenges. While it evaluates the external behavior of a software application, it fails to assess its internal workings, leaving potential security flaws within the code undetected. Furthermore, DAST does not achieve complete code coverage, meaning certain parts of the application might remain untested and susceptible to vulnerabilities.

What are the benefits of AI-powered white-box testing?

AI-powered white-box testing offers several advantages: It can integrate easily with existing unit tests for automated testing at each code change It leverages the internal design of the application to find hidden issues It eliminates false positives and duplicates, providing actionable results It helps assess untested parts of the system by refining test inputs based on coverage information

What is AI-Powered white-box testing?

Dynamic white-box testing with self-learning AI is a testing strategy that leverages the internal design of the software under test to generate myriads of intelligent test cases. Genetic algorithms learn from previous test data and create new test cases to dig deeper into the software under test. This approach enables dev teams to identify bugs and vulnerabilities that traditional testing methods often miss.

How does AI-powered white-box testing compare to traditional black-box testing?

AI-powered white-box testing differs from black-box testing as it can leverage the internal design of software to provide a more comprehensive testing approach. It can also allow dev teams to gather information from previous test cases and use this knowledge to generate new, more intelligent ones automatically.

How can Large Language Models (LLMs) and self-learning AI work together in software testing?

LLMs can be used to analyze code and automatically identify the potential attack surface. They can also generate the corresponding test harnesses needed for self-learning AI. Self-learning AI can then “attack” these entry points while continuously refining test cases based on coverage feedback. When combined, these two forms of AI provide a more automated and scalable testing model, enabling development teams to secure self-written code, third-party components, and AI-generated code at scale.