The prevalence of API Attacks increasing more and more, and most all go unnoticed until it is far too late. However, many have been very visible lately including recent attacks on Instagram, Verizon, and Facebook. Many of the Security and DevOps leaders we speak to will tell us they: 1. don’t know if they are under attack, 2. don't know how many APIs they have, and 3. don't have detailed visibility into API activity once authentication has occurred.
Machine Learning can be used to counter the exponential rise and evolution of API attacks. By leveraging machine learning, an organization can analyze normal API traffic and behaviors to detect anomalies and bad actors. Many identity based attacks (for example: stolen OAuth tokens) can go months before being discovered by conventional access policies. With machine learning, these nuanced attacks are stopped before customer data can be exfiltrated, edited, or deleted.
- What are the most common API attacks today
- An overview of how Machine Learning works
- How Machine Learning is the best first line of defense against API attacks
- How dynamic ML behavioral analysis can find attacks faster and with more precision than traditional access policies
