Find the stories hidden in
your API data
Quickly identify and fix a misconfigured API. Automatically notify your development team if an API failed a risk audit. Swiftly, know if a partner’s API access has been compromised. Efficiently recognize when your payments API is scraped.
Neosec’s behavioral analytics detects misconfigurations, misuse, and business logic abuse–and shows the full story on a timeline.
Understand baseline behaviors
Data is the key ingredient to understanding API use. Inside your data lurks both normal and abnormal usage. Neosec profiles the normal baseline behavior for each entity in every API. But it doesn’t stop there. These profiles evolve as more data is analyzed. Anomalies are revealed from previous usage, and also compared with baselines from all other users.
Every entity has a timeline
Imagine having a behavior profile and timeline for every partner, merchant, user, API token, IP address, and all other actor entities in your APIs.
Better still, imagine having a profile for every business process entity such as inventory, payments, invoicing, or account access API. Every entity found in your APIs is profiled. Neosec is not a black box, it allows security teams to understand the timeline and exact behavior of every consumer and business entity.
Behavior is only understood over time. Only by accessing historical data is meaningful analysis possible. Be wary of solutions that don’t store and analyze past data. Neosec sanitizes, ingests, stores, and analyzes all your API data for a rolling 30 day period.
Our data enrichment helps security teams make sense of the mountain of data and reveals relationships and context across every API.
Every API is named and labeled according to your own taxonomy. This common sense language makes it easier for security and development teams to communicate and determine which threats to prioritize.
Efficacy improves as more data is analyzed. As each day passes, Neosec is designed to continuously improve its analytics based upon dynamic profiling.
These larger datasets create better input for threat researchers to improve machine learning detection models and algorithms.
Better efficacy creates highly accurate and actionable alerts that help security teams respond to threats faster with confidence.
Incident specific alerts
Behavioral alerts clearly explain the threat detected and this knowledge allows security and developers to work together to find solutions.
Alert categories include: Account Takeover, AuthN, AuthZ, Recon, Data Access, Data Leakage, Ops, Shadow API, Remote Code Execution (RCE), Injection, Local File Inclusion (LFI), and more.