APIs have powered modern-day digital transformations, revolutionizing how many businesses and organizations are now run. However, the fresh wave of innovation and digital transformation is also responsible for carving new avenues for hackers to exploit.

According to various reports, API communication makes up 83% of internet traffic. Therefore, as APIs become exceedingly frequent in use, the number of API data breaches also reaches sky-high. The problem prevails as traditional API security is no longer enough to match the sophistication of modern-day cyber-attacks.

As API security has become the epicenter for ensuring customer data privacy and security, a new method of enabling API security has started appearing that uses AI-driven API security measures.

Artificial intelligence (AI) can help enable robust security measures to protect unsecured APIs. AI is trained by utilizing a significant amount of data artifacts from both structured and unstructured resources, allowing it to build the ability to defend against attacks. Moreover, with the help of machine learning and deep learning, AI improves its knowledge and understanding of cybercrime, helping to significantly improve an API’s security posture.

What Is API Security and Why Is It Important?

As API integration enters the foreground at many organizations, API security has quickly become one of the primary concerns for GDPR, PSD2, CDR, PCI, and HIPAA compliance. This is underscored by the rising number of data breaches due to insecure APIs. In 2018, IBM claimed that 50% of global security decision-makers had at least one breach. The year 2021 alone faced an alarming number of data breaches, amongst which some of the most significant ones are:

  • Parler data breach: The social media platform had insecure APIs causing a privacy breach leaking user messages and other private information. The platform was soon after shut down.
  • Microsoft Exchange Server Attack: The Chinese state-sponsored hacking group targeted various Microsoft Exchange Server deployments targeting its web component and APIs.
  • LinkedIn Scraping: The attack occurred twice in April 2021 and then in June 2021. As a result, a large chunk of LinkedIn profile data was sold at an underground marketplace. According to Microsoft, such data scraping occurs due to leaky APIs.

With such attacks prevailing, API security has become a critical part of any business strategy in the digital age. With so much sensitive data being transmitted and stored through APIs, it’s essential to have a robust security strategy in place.

Any organization enabling API security can ensure protection against several threat actors and their malicious attacks. However, considering the modern-day threat landscape, the only way to guarantee high-grade API security is by integrating AI-based security measures.

Problems With Traditional API Security

The traditional model for API security relies heavily on authentication, authorization rate-limiting, and throttling. While these tools are practical, they still fail to provide the security APIs needed against threat attacks. Although netizens nowadays rely on the use of strong security tools such as VPN to enable data protection and online security, API attacks continue to rise steadily.

It is crucial to realize that API gateways front several web services, and often the APIs they manage are loaded with a high number of sessions. Therefore, even if anyone works to analyze all those sessions through policies and processes, it will still be a complex task for an API gateway to inspect each request without computerized support.

Moreover, each API works through its access patterns. Therefore, a legitimate access pattern for one API could often indicate malicious activity for another API. Amidst this, the API gateway might have to separately analyze each API access pattern to determine the correct response.

Another crucial factor that cracks existing API security methods is the frequent occurrence of insider threats. Since these insider threats occur through users with valid credentials and access to systems, it is hard to prevent them through policy-based authentication and authorization.

While utilizing the traditional API security method, the best way to mitigate insider threats is to implement more rules and policies within an API gateway. However, this method would entail additional load on API gateways, leading to processing delays. The delays could ultimately result in frustrated users.

Advancements in API Security With The Advent Of AI

As the traditional API security method continues to fail in ensuring robust API security, modern security methods view AI as an ultimate solution. The advent of AI has remarkably changed API security since it can detect and respond to dynamic attacks and unique vulnerabilities faced by each API individually. AI models can allow enterprises to discover anomalous API activities and threats through continued inspection and detection.

There is no doubt that the previous security measures could detect the anomalies and the risks faced by API. But these methods took months, and often by the time discoveries were made, it was too late. In contrast, integrating AI-based security models on user access patterns can help detect various threats in real-time.

The most crucial aspect of integrating AI security is that these models usually run outside API gateways, establishing their communication and decisions externally. Since these models don’t require API gateways to expand their resources, the addition of AI security doesn’t impact API gateway runtime performance. Some of the key benefits of implementing AI-powered API security are as follows:

  • Real-time protection against cyberattacks
  • Identification of malicious actors and patterns
  • Improved security for API data
  • Increased safety for businesses in the digital age

Since API security is more important than ever in the age of cyberattacks, businesses can now better protect their API data and keep their systems safe from harm with the advent of AI. By implementing API security measures, organizations can help ensure that their business is safe from harm in the digital age.

Conclusion

The modern threat landscape demands the use of robust API security. As the traditional security methods cannot keep up with the modern, actively thriving digital landscape, integrating AI seems to be the best solution in enabling robust API security.

With the advent of AI, organizations can now better protect their API data and keep their systems safe from harm. An AI-based API security approach offers several benefits that allow organizations to run their matters smoothly without the hassles and interference of various API threats and vulnerabilities.