AI usage has skyrocketed in past few years, and so is AI driven API security threats. To prevent such unique threats, security teams must implement a reliable AI Powered API security platform that offer advanced security for API’s and has capability to monitor countless interactions simultaneously.

A recent “State of security” report by Darktrace found that 74% of 1800 cybersecurity leaders have experienced AI driven threats, while 96% think AI-Powered security as essential solution to prevent such threats. AI’s breakthrough capabilities can help prevent evolving security threats, that would be difficult to achieve through manual security measures alone.

Read on to know what does AI in API Security mean and why it is essential in the modern API security sphere.

What is AI-Powered API Security?

AI-Powered API Security is an application of AI (Artificial Intelligence) technology in API Security measures to protect APIs from evolving security threats and vulnerabilities. AI helps identify anomalies, analyze API traffic patterns, and spot potential attacks in real time. It has the ability to learn from historical data and adapt to unique and new threat patterns and make smart decisions without the constant need of human supervision.

Why is AI important for API Security?

API security requires the integration of Artificial Intelligence to address evolving threats and operational challenges. Here’s a breakdown of reasons to state why it is important.

Increase in AI driven Security Threats

The advancement of AI technologies at lightening speed has also been a major cause for rising AI related threats and vulnerabilities which includes automated OSINT (Open Source Intelligence) gathering, probing network, and credential harvesting. A recent report indicated a shocking 1025% surge in AI based vulnerabilities and exposures where most of them were linked to APIs. This surge highlights the evolving sophistication of security threats that target APIs, which makes it necessary to implement advanced and efficient security measures.

Expansion of API Attack Surface

The rapid usage of APIs has resulted in expanded attack surface. Unfortunately, the traditional measures mostly fall short in handling the new and unique type of vulnerabilities that are specific to APIs. A report reveals that only 21% of business organizations have facility to detect the security attacks at the API layer and only 13% are able to effectively prevent most of the API attacks. This difference highlights the need for AI powered API security platforms that are capable of tracking and securing complex API scenarios.

Rise in Unique API Attack Techniques

Cyber attacks are increasing at an alarming rate and most of them exploit business logic vulnerabilities within APIs, conduct high level attacks that traditional security tools fail to detect. Such attacks include manipulation of API parameters misuse pricing errors, abuse authentication workflows to gain unauthorized access, bypass rate limits to scrape the data. These type of complex threats can be mitigated with the help of AI through its advanced capabilities of analyzing patterns and detecting anomalies.

Shortage of Cybersecurity Workforce

Cybersecurity industry is struggling with immense talent shortage. As per report, there is a shortage of over 3 million eligible professionals in cybersecurity roles. This shortage could hinder the organizations ability to effectively manage and secure their API infrastructure. AI could minimize this issue by automating threat identification and response to compensate for the lack of human resources.

Considerations to Implement AI in API Security

AI plays an important role in improving API security, however its implementation by API security vendors mostly lacks transparency. Security teams must have clear insights into how AI powered security platform function to make right decisions. Here’s a closer look at key considerations for security teams before implementing AI in API Security.

# Traditional Scanner: Rule-Based Detection
def scan_api_request(request):
    blocked_patterns = ["../../", "DROP TABLE", "admin' --"]
    for pattern in blocked_patterns:
        if pattern in request:
            return "Blocked: Suspicious activity detected."
    return "Request allowed."

# AI-Powered API Security: Behavior-Based Detection
from ai_security_module import AnomalyDetector

anomaly_detector = AnomalyDetector()

def ai_scan_api_request(request, user_profile):
    risk_score = anomaly_detector.assess(request, user_profile)
    if risk_score > 0.8:  # AI dynamically evaluates risk
        return "Blocked: High-risk request detected."
    return "Request allowed."

# Example Usage
user_profile = {"normal_behavior": ["GET /profile", "POST /order"]}

request_1 = "DELETE /user/123"  # Not in the blocklist but unusual for this user
print(scan_api_request(request_1))  # Traditional scanner may allow it
print(ai_scan_api_request(request_1, user_profile))  # AI may flag it based on behavior

Transparency in AI Security Platforms

A big challenge in AI powered security is the lack of clarity and transparency from API Security vendors. Most of the times, security professionals are unclear about the types of AI used in their security tools. This uncertainty is quiet challenging for security teams to assess whether the AI powered security platforms align with API Security needs. With clear explanations, security teams can trust and fully be able to take advantage of AI powered security platforms.

Clear Communication from API Security Vendors

To bridge the communication gap, API Security Vendors must provide detailed and clear explanation about the AI implementations. They should address the following.

What type of AI will be used?

Get clear insights into what type of AI will be implemented, whether the API security platform relies on deep learning, behavioral analysis or other AI technologies.

How does it Work?

Understand the mechanisms which is used in AI powered API security platform, such as automated threat intelligence, anomaly detection and policy enforcement.

Why to choose the specific AI based API Security platform?

Consider what type of benefits the AI powered API security platform provides. For instance, zero day vulnerability identification, real time threat detection or automated response.

These considerations help security teams to make better decisions that align with their security strategies when deploying AI powered API security platform.

How AI Works in API Security

AI can improve API across various domains and effectively assist in preventing security threats. Let us look at how AI works in API security

Secure API Coding

AI assists developers in detecting and rectifying security vulnerabilities during the coding stage. By integrating AI tools into the development settings, potential security issues like improper authentication methods or insecure data handling can be detected at the earliest. This proactive approach ensure that APIs are developed with utmost security measures from the outset, and minimize the risk of future security breaches.

Discovery of Zero Day API Vulnerability

By utilizing machine learning, AI analyzes various datasets to find patterns which are indicative of unknown vulnerabilities. This feature lets AI to predict and identify zero-day vulnerabilities, flaws that are not publicly patched or disclosed before they could be exploited. By closely keeping tabs on emerging threats in advance, security teams can implement security measures proactively.

API Threat Detection and Response

AI can constantly monitor API traffic to establish a baseline normal behavior. When deviations happen like unusual data requests or access patterns, AI systems can instantly identify these anomalies. This real time analysis help in quick response to potential security threats and can highly reduce the window of opportunity for attackers and safeguard sensitive information.

API Security Posture Management

AI allows extensive management of security teams API ecosystem. It has the capability to automatically discover all the active API’s, which includes undocumented or shadow APIs and ensure that they are all accounted and secured. Apart from this, AI can also classify APIs based on the sensitivity of the data they handle. This also enables specific security protocols that meets risk profile of each API.

API Security Testing

AI can automate the workflow of testing APIs for weaknesses. It helps simulate various security attack scenarios, like attempts of authentication bypass, injection attacks to evaluate API’s resilience. This exhaustive testing ensures that vulnerabilities are identified and addressed before the deployment and improves the overall security posture of APIs.

Final Thoughts

By Integrating AI powered API Security platforms, security teams can improve threat detection, reduce response times and stay ahead of emerging API security threats and make API security more effective, efficient and scalable.

Akto has integrated next generation AI technology in innovative ways to provide advanced API security such as:

GenAI Security Testing

Akto has recently introduced “GenAI Security Testing” to improve the security of AI and LLM’s Large Language Models. This feature makes use of AI based testing techniques and algorithms to effectively detect and mitigate vulnerabilities in AI and LLM API’s. It helps address risks such as insecure output handling and prompt injection.

AktoGPT

AktoGPT utilizes OpenAI’s GPT to strengthen the API security and provide unbeatable API protection. This integration facilitates automated response strategies, predictive threat analysis, and optimization of API security protocols that makes them more resilient and adaptive.

To experience more such exciting and ground breaking API security features, contact Akto.io experts and get your hands on the Akto API security platform by booking a free demo right away!