Applications Programming Interfaces (APIs) have continued to grow in recent years and have now become the driving force in businesses’ digital transformations. They empower all aspects of business transactions and create innovative ways through which customers can access services. They also make it easy for businesses to meet customer demands and predict future changes before they can even happen.
However, this has encouraged hackers to find ways through which they can attack these businesses and disrupt their operations. To counter this, businesses have implemented API security measures that offer solutions such as authorization, authentication, and throttling and help them control access to their APIs.
Furthermore, when building APIs, businesses are subjecting them to thorough tests to see how strong they are in case of attacks. To do this, they use some of the best API testing tools that are designed to test the functionality, security, and performance of APIs and foresee any problems that might happen in the future.
Even though each one has its own pros and cons, these tools give businesses visibility into any vulnerabilities that their APIs might have.
To further enhance the security of their APIs, smart businesses are implementing more dynamic measures such as machine learning-driven security measures to address new sophisticated attacks on their APIs.
Adding a Machine Learning Security Layer
To ensure API security, most businesses are using policy and rule-based security measures. These include;
- Dynamic security checks: These are security measures that check the API aspects that change with time. They involve the use of existing data to validate request data. Examples may include throttling and validating access tokens.
- Static security checks: These checks validate data using a set of policies and rules and are normally done at gateways to block any attacks that might affect the APIs.
- Authentication: These are checks used to identify users that use the API. The most commonly used authentication measures include the OAuth2, basic authentication, and JSON Web Token (JWT).
However, with hackers taking advantage of technology improvements and finding new ways to bypass the above security measures, businesses are employing machine learning algorithms to ensure that their APIs are secure.
These algorithms are able to easily detect any dynamic attacks and check the vulnerabilities that might be available on each API in use. Businesses that are using machine learning algorithms are able to check activities on all APIs and respond to any threats and unauthorized activity that the above measures cannot.
Some developers might argue that the above policy and rule-based measures are able to detect the same threats that machine learning algorithms do. Well, even though that is true to some extent, it takes them (policy and rule-based measures) a very long time before detecting the threats.
If a hacker who wants to gain access to your information finds their way in, chances are that they might get the information they want without being detected. However, having machine learning algorithms that use models based on the access patterns of your users will make it possible for you to detect such a hacker in real-time, and therefore prevent them from gaining access to your APIs.
Conclusion
Adding a machine learning security layer to your APIs does not affect the performance of the APIs in any way. It only works in making sure that the APIs are secure and prevent any attacks that might derail their performance.
The layer runs outside API gateways but will communicate with the gateway when preventing attacks. This leaves enterprises that need to protect their APIs with no excuse but to implement artificial intelligence in the cybersecurity of their businesses to protect themselves and their customers.
