Modern digital transformations have been fuelled by APIs, altering how many businesses and organizations run. However, the recent innovation and digital transformation wave have also opened up new attack surfaces for cybercriminals. Companies are forced to respond to an increase in API threats, but they quickly learn that traditional, static methods of API security are ineffective. Machine learning (ML) and artificial intelligence (AI) become helpful allies in stopping API attacks. The topic of whether to have ML-driven API security has now given way to how to obtain the best level of business protection.

The value of API security

Security issues with APIs are becoming more frequent and disruptive. Enterprises worldwide are seeing an increase in detrimental API incidents because of the surge in API traffic, making API security a high priority. According to the Google 2022 API Security Research Report, 50% of the firms questioned encountered an API security event; of those, 77% delayed deploying a new service or application.

According to the Salt Security API Security Trends 2023 study, the number of API security breaches generating headlines and significant business delays have elevated API security to a board priority. These attacks, which are famously difficult to recognise, target APIs connected to intellectual property, operational procedures, or sensitive data like private information, proprietary data, or banking accounts.

These APIs must be constantly available to offer business value, but they have also become targets for attackers. According to the same Salt Security report, 17% of respondents had encountered a security breach, and 31% had experienced a sensitive data disclosure or privacy problem. Such occurrences incur high expenses and harm a company’s reputation.

A paper titled Quantifying the Cost of API Insecurity, published by Imperva, predicts that the absence of secure APIs might result in an average yearly global cyber loss of between $41 billion and $75 billion. In addition, the average cost of a data breach is $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 to the company.

Traditional approaches are failing

Many businesses primarily rely on traditional security practices, such as API gateways, log file analysis, and alerts generated by web application firewalls (WAFs), to address the expanding API threat landscape. However, according to the Salt Security report, IT professionals admit these methods are ineffective. 77% of the survey respondents say their existing tools aren’t very effective in preventing API attacks.

Static security measures are less effective at detecting business logic attacks, giving criminals the freedom to alter lawful services to further their malevolent ends without drawing attention. For instance, most monitoring tools would likely not notice a change in a server’s activity patterns if a malicious actor took control of it and made modest changes.

The sheer number of warnings is another difficulty in identifying API abuse cases. Many static criteria that determine less complex attacks are extremely sensitive: They create a lot of notifications to lower the chance of missing important security occurrences. For many IT teams, this makes detecting the significant events within API traffic and taking action to remedy them similar to “finding a needle in a haystack.”

The quest for the best ML solution

ML-driven API security solutions seem to be the only viable way of addressing the complex nature of API abuse incidents. However, businesses should be cautious when selecting such a solution.

The depth and breadth of the dataset and the number of features utilised for detection purposes form the basis of every machine learning algorithm. The most significant issue for machine learning in cybersecurity is balancing the need to manage a large volume of diverse and sequential data while delivering valuable and precise information on causality and attribution. Businesses should choose machine learning algorithms that satisfy both needs successfully because attackers’ tactics constantly change.

Therefore, ML solutions for API security should have two critical traits:

1. The model needs to have been trained on years’ worth of API data and based on best practices for threat identification, providing the best chance to distinguish between legitimate and fraudulent traffic and warn key stakeholders to take prompt action and limit the severity of the issue.
2. Include detection dashboards so businesses can more quickly identify critical API abuse problems, such as business logic attacks and abnormalities. To resolve any incidents more quickly, critical threats must be highlighted with precise and succinct descriptions that capture the substance of the attack and highlight its essential elements, such as its origin, the number of API calls it made, and its duration.

Along with these technical characteristics, businesses must change how they handle API abuse incidents. Sarah Klein, a regulatory, privacy, and cybersecurity professional, wrote in a LinkedIn blog post:

“While many companies limit identifying “data breaches” to incidents defined by various laws or regulatory pronouncements they are obligated to comply with, it is inadequate for a maturing data industry. In addition, as companies rely more on APIs to provide services or products to their customers or use them internally to automate data processes, security experts must proactively change the narrative and treat API abuse as a data breach.”

In line with defining API abuse as a data breach, many companies have included API abuse detection capabilities in their products. The combination of advanced Machine Learning capabilities and a comprehensive approach to API security can help businesses prevent API attacks and reduce their impact should an abuse is detected.

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