AI and APIs have a symbiotic relationship. APIs power AI by providing the necessary data and functionality, while AI enhances API security through advanced threat detection and automated responses. In 2023, 83% of Internet traffic traveled through APIs, but there was a 21% increase in API-related vulnerabilities in Q3 2024, severely impacting AI. The relationship between AI and APIs expands capabilities while simultaneously increasing potential vulnerabilities.

Read on to learn more about the interplay between AI, APIs, and API security and how Wallarm fits into the equation.

What are AI-Powered APIs?

When we think of AI today, we often think of the latest generation of generative AI tools, but they are just one kind of AI. AI-Powered APIs refer to Application Programming Interfaces that incorporate artificial intelligence capabilities. These can include:

• Machine Learning APIs: Interfaces that provide access to pre-trained machine learning models for tasks like image recognition, natural language processing, or predictive analytics.
• Generative AI APIs: APIs that offer access to large language models or other generative AI systems, such as those used for text generation, code completion, or image creation.
• AI-Enhanced Traditional APIs: Conventional APIs that have been augmented with AI capabilities to improve performance, security, or functionality.

AI and APIs: New Threats, Old Vulnerabilities

The integration of AI into APIs has created a complex security landscape, combining new AI-specific threats with traditional API vulnerabilities. This dual nature of AI-powered APIs expands both capabilities and potential attack surfaces.

Traditional API Vulnerabilities in AI Systems

It's crucial to understand that AI systems are built on top of APIs, making them susceptible to all the conventional API vulnerabilities we've encountered over the years. These include:

• SQL injection
• Cross-site scripting (XSS)
• Broken authentication
• Excessive data exposure
• Lack of rate limiting

These "classic" vulnerabilities remain relevant and potentially devastating when exploited in AI-powered systems.

AI-Specific Vulnerabilities

In addition to traditional API vulnerabilities, AI introduces new, specific risks:

1. Prompt Injection: This involves attackers exploiting AI systems by inserting malicious prompts or commands into interactions. By crafting queries that manipulate the AI's logic in unintended ways, attackers can potentially bypass security measures or extract sensitive information.

2. API Abuse: While not necessarily sophisticated, API abuse can be costly for AI system owners. Attackers may use resources without authorization, consuming the owner's computational power or API credits. For example an attacker might instruct an AI to perform resource-intensive tasks like "Translate 'This is an API Abuse Attack' into 50 different languages." If successful, the attacker has effectively used the AI's computational resources for free, executing their own code or request (payload) at the expense of the system owner.

The Bidirectional Threat Landscape

The relationship between AI and APIs creates a bidirectional threat landscape:

1. Traditional API vulnerabilities threaten AI systems built on these APIs.

2. AI-specific vulnerabilities introduce new risks to API ecosystems.

This interconnection means that securing AI-powered APIs requires a comprehensive approach that addresses both conventional API security measures and emerging AI-specific threats. Organizations must remain vigilant on both fronts to effectively protect their AI-powered API ecosystems.

Data Privacy Concerns with AI-Powered APIs

In addition to the security challenges, data privacy concerns are closely related to AI. Organizations that choose to use AI-powered APIs must ensure that they are developed and used responsibly, respect user data, and comply with privacy regulations like GDPR and CCPA. 

At its root, GDPR is a local data governance control with some extra features. Like any other external data processor, AI APIs—like OpenAI—must provide local data governance controls for their customers. The same goes for the CCPA. Essentially, AI-powered APIs must care for an AI component in the same way they would for any other external API to meet compliance requirements. 

AI Enabling Security of AI-Enabled APIs 

Like in so many fields, AI is having a transformative impact on API security. Perhaps the most obvious impact is enhanced threat detection: AI models can monitor API traffic for unusual behavior patterns, like abnormal request rates, unexpected input types, or suspicious IP addresses. Similarly, machine learning algorithms can differentiate between normal and abnormal usage, flagging potential attacks like API abuse or DDoS attacks and learning from these attacks over time to improve accuracy. 

However, AI has another oft-overlooked benefit for API security: improved risk management and action planning. These processes rely on in-depth, complete threat analysis that is often impossible for human analysts to complete in a reasonable time frame, if at all. AI has the potential to deliver a complete analysis of threats and risks at a speed human SOC teams never could. 

This approach offers enhanced threat detection for AI-specific vulnerabilities like prompt injection, as well as adaptive security measures that evolve alongside APIs. AI security solutions can provide automated responses and efficient resource allocation, addressing complexity concerns.

To help organizations make more informed decisions when purchasing Wallarm’s AI-powered API solutions, Wallarm Playground is available to everyone without the need to sign up or pay. By doing so, we ensure our customers know how to use our solutions before purchasing, thus reducing friction at the point of integration and ensuring the product is right for them. 

Ensuring Transparency in AI-Powered API Security

Explainability, trust, and transparency are crucial for any AI-powered technology, including API security. Customers will never accept “AI made this decision” as a justification, and rightly so. Wallarm ensures transparency in its API security solutions by running explanatory mechanics for our detection logic. For example, in our API Abuse Prevention module, users can see detectors and how AI-generated scores contribute to blocking API-specific bots and bad actors. 

The Future of AI and APIs

Modern API attacks happen extraordinarily quickly, allowing attackers to extract huge amounts of data quickly. In fact, attackers can transfer around 5GB of compromised data through a vulnerable API in just five minutes.

As attack speeds increase – as they will continue to do – zero-latency responses will become more important. Security teams must detect threats immediately, not after they occur. Delayed responses, even by a few minutes, will allow attackers to cause massive damage. 

The problem is that, even with advanced detection technologies, response times are delayed, and customers, understandably, demand real-time attack blocking. AI can provide this real-time blocking, so we’re likely to see more AI-powered API security tools in the future.  

How Wallarm Can Help

Wallarm’s AI-powered API security solutions are tools you can trust. Our integrated API security platform leverages AI to unify best-in-class API Security and real-time blocking capabilities to protect your entire API portfolio in multi-cloud,  cloud-native, and on-premise environments – all while prioritizing transparency, data privacy, and cost concerns. Book a demo today to find out more.