High-Fidelity Alerts: The Key to Effective API Security

APIs are essential for modern applications as they enable seamless communication and data exchange, serving as the foundation of these applications. However, their interconnected nature makes them vulnerable to various threats. The high volume of API calls - ranging from millions to billions every month - poses a challenge in distinguishing between genuine attacks and harmless irregularities. Many API security solutions struggle to differentiate between actual threats and false alarms, leading to an overload of unnecessary alerts and resource drain for security teams.

The False Positive Flood: A Challenge in API Security

False positives, the misidentification of harmless or anomalous activity as malicious, are a common problem in API security. The large amount and variety of API traffic often result in a flood of alerts, most of which do not indicate actual attacks. This continuous stream of false alarms leads to alert fatigue, desensitizing security teams and potentially causing them to overlook real threats.

Investigating every false positive is a laborious and resource-intensive task. It directs attention away from crucial security responsibilities, delays incident response, and ultimately undermines an organization's security position. With the rise of advanced API attacks, the need for accurate alerts that pinpoint real threats has never been more vital.

Salt Security: A Beacon of Clarity in the Storm

Salt Security is a pioneer in API security, providing a unique approach that significantly reduces false positives and delivers the high-fidelity alerts that security teams desire.

1. Big Data Power: Salt's foundation is built on a scalable big data infrastructure capable of processing large volumes of API traffic over extended periods. Unlike other tools that analyze API calls in isolation or over short timeframes, Salt is able to gain a comprehensive understanding of user behavior and intent. This enables it to differentiate between normal variations and malicious patterns.
2. Salt's AI and ML models are trained on an industry-leading dataset comprising trillions of API calls and millions of real-world attacks. This extensive and diverse training data allows Salt to fine-tune our algorithms, accurately identifying subtle indicators of malicious activity that other solutions might miss.
3. Looking Beyond Anomalies: Understanding Intent: Salt doesn't only identify anomalies; it goes further to comprehend the intention behind each API call. This contextual understanding enables Salt to differentiate between a user encountering a problem due to using an outdated app version and an attacker methodically searching for vulnerabilities for example.  This capability allows us to highlight actual malicious intent quickly.