The rise of modern digital transformations has been facilitated by the use of application programming interfaces (APIs), which have fundamentally changed how businesses and organizations operate. However, these technological advancements have also opened up new avenues for cybercriminals to exploit. As API threats increase, companies are realizing that traditional methods of API security are no longer effective. To combat these evolving threats, machine learning (ML) and artificial intelligence (AI) have emerged as valuable tools in preventing API attacks. The focus has shifted from whether to implement ML-driven API security to how to obtain the highest level of business protection.
API security has become a pressing concern for enterprises worldwide due to the escalating number of security breaches. The surge in API traffic has made API security a top priority, with many companies encountering API security events. According to the Google 2022 API Security Research Report, 50% of the firms surveyed experienced an API security incident, and of those, 77% delayed deploying new services or applications in response. Similarly, the Salt Security API Security Trends 2023 study revealed that API security breaches have become headline news and significant disruptors in the business world. These attacks, which are notoriously difficult to detect, target APIs connected to intellectual property, operational procedures, and sensitive data such as private information, proprietary data, and banking accounts.
Maintaining the availability of these APIs is crucial for business operations, but it also makes them attractive targets for attackers. The Salt Security report found that 17% of respondents had encountered a security breach related to APIs, while 31% had experienced sensitive data disclosure or privacy issues. These incidents can result in substantial financial losses and reputational damage.
According to a report published by Imperva titled “Quantifying the Cost of API Insecurity,” the absence of secure APIs could lead to an average global cyber loss of between $41 billion and $75 billion annually. Additionally, the average cost of a data breach is estimated to be $4.45 million, according to the IBM 2023 Cost of a Data Breach Report. Early detection and mitigation of API abuse issues are essential for enterprises to avoid long-term financial and reputational harm.
Many businesses have traditionally relied on static security measures, such as API gateways, log file analysis, and web application firewalls (WAFs), to address the increasing API threat landscape. However, these methods have proven to be ineffective in preventing API attacks. IT professionals in the Salt Security report admitted that their current tools are inadequate in this regard, with 77% of respondents stating that their existing tools do not effectively prevent API attacks.
One of the main challenges with static security measures is their limited ability to detect business logic attacks, allowing criminals to alter legitimate services without attracting attention. For example, a monitoring tool might fail to recognize a change in a server’s activity patterns if a malicious actor gains control and makes subtle modifications.
The sheer volume of warnings is another obstacle in identifying API abuse incidents. Many static criteria aimed at detecting less complex attacks tend to be overly sensitive, generating numerous notifications to minimize the risk of overlooking important security events. This makes it difficult for IT teams to identify significant events within the vast amount of API traffic, akin to “finding a needle in a haystack.”
In light of these challenges, ML-driven API security solutions have emerged as a viable approach. However, it is crucial for businesses to choose the right solution carefully. The effectiveness of an ML algorithm depends on the depth and breadth of the dataset used for training and the number of features utilized for detection. The ability to handle a large volume of diverse and sequential data while delivering valuable and accurate information on causality and attribution is a significant challenge in ML algorithms applied to cybersecurity. Businesses should select ML algorithms that strike a balance between these requirements, as attackers constantly change their tactics.
To be effective, ML solutions for API security should possess two critical traits. Firstly, the ML model should be trained on years’ worth of API data, incorporating best practices for threat identification. This ensures the best chance of distinguishing between legitimate and fraudulent traffic, allowing prompt action to be taken by key stakeholders to mitigate the severity of the issue. Secondly, the ML solution should include detection dashboards, enabling businesses to quickly identify critical API abuse problems such as business logic attacks and abnormalities. The dashboards should provide precise and succinct descriptions of the attack, including its origin, the number of API calls made, and its duration, to expedite incident resolution.
In addition to technical considerations, companies must also change their approach to handling API abuse incidents. Sarah Klein, a regulatory, privacy, and cybersecurity professional, argues in a LinkedIn blog post that API abuse should be considered a data breach. As more companies rely on APIs to provide services or automate data processes, the narrative around security needs to evolve accordingly. Many companies have started incorporating API abuse detection capabilities into their products, aligning with this perspective. By combining advanced ML capabilities with a comprehensive approach to API security, businesses can prevent API attacks and minimize their impact when abuse is detected.
In conclusion, as APIs continue to revolutionize the digital landscape, the importance of API security cannot be overstated. Machine learning and artificial intelligence are valuable allies in the fight against API attacks. With the increasing complexity and frequency of API threats, traditional static security measures are no longer effective. ML-driven API security solutions offer the best chance of detecting and mitigating these threats. However, businesses must carefully select ML algorithms that strike a balance between handling diverse data and providing accurate information. By implementing ML-driven API security solutions and redefining the approach to API abuse incidents, companies can protect their assets, avoid financial losses, and safeguard their reputation in an increasingly digital world.