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As a first step towards a comprehensive approach, it is important to examine the most common attitudes towards application security testing today: static security testing and dynamic security testing.
Static security testing takes a white-box approach, creating tests based on the known functionality of the application by reviewing the design, architecture, or code, including the many complex paths that data can take as it passes through the application.
Dynamic security testing takes a black-box approach, creating tests based on the expected performance of the application given a particular set of inputs, disregarding internal processing or knowledge of the underlying code.
When it comes to APIs, developers and security teams frequently argue over which of the two methods is most appropriate, with the leading reasoning in favor of each being:
Sorry to ruin the party, but both of these points are only partially true. As a matter of fact, both approaches are necessary to ensure broad coverage and handle a variety of possible scenarios. Especially with the current rise of API-based attacks, you cannot take any chances when it comes to scalability, depth, and frequency.
'Grey-box' API security testing may offer an interesting alternative. Since there’s no user interface, having knowledge of the app’s internal workings (e.g., parameters, return types) can help you efficiently create functional tests that focus on the business logic.
Ideally, combining aspects of API security testing would get you closer to creating a grey-box solution that compensates for the weaknesses of each of these individual approaches. Such business logic approach would intelligently examine results of other test types and can adapt to apply improved tests, either automatically or manually.
There’s growing industry awareness surrounding the need to secure APIs across their lifecycle, placing APIs front and center in your security controls.
To do this, you must find ways to simplify and streamline your organization’s API security testing, integrating and enforcing API security testing standards within the development cycle. This way, along with runtime monitoring, the security team can gain visibility into all known vulnerabilities in one place. As a bonus, taking steps to shift-left API security testing will cut costs and accelerate time to remediation.
Moreover, once your testing workflows are automated, you'll also have built-in support for retesting: a cycle of test, remediate, retest, and deploy, keeping your pipeline running smoothly and avoiding bottlenecks altogether.
A business logic approach to API security testing can elevate the maturity of your Full Lifecycle API Security program, and improve your security posture.
However, this modern approach requires a tool that can learn as it goes, improving its performance over time by ingesting runtime data to gain insights into the application’s structure and logic.
This would involve creating an adaptive test engine that can learn as it goes, developing a deeper knowledge of the API’s behavior in order to intelligently reverse-engineer its hidden inner workings. Using runtime data and business logic information, you can enjoy the best of both worlds - the black and white box approach towards enhanced visibility and control with automation.
In addition to their increasing popularity, APIs also create greater vulnerability for web applications. A large number of organizations do not even know what the extent of their APIs and vulnerabilities are. Known and unknown weaknesses can easily be probed by hackers via available APIs.
However, API security testing is often overlooked and handled the same as web applications. Most testing approaches, such as black-box and white-box testing, are not conducive to API testing.
A combination of natural language processing and artificial intelligence (AI) offers a viable "grey box" option that automates, scales, and simplifies the complex process of API security testing.