What is API Threat Detection?

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API threat detection is the work of reading intent from live API traffic. A single request rarely tells the whole story. The useful signal appears when route inventory, schema expectations, auth context, payload inspection, failure patterns, behavioural cadence, and response actions are viewed together.

This is different from waiting for an exploit signature. Many API incidents use normal HTTP methods and valid JSON. A client can authenticate successfully and still scrape data, enumerate objects, test stolen credentials, or push an expensive route until the origin starts to struggle.

Detection Starts With Knowing the Surface

You cannot detect route-specific threats against routes you cannot see. API detection needs a current inventory of production endpoints, including legacy paths, partner routes, mobile app backends, GraphQL operations, and shadow APIs that no longer appear in the latest OpenAPI file.

The inventory gives each request a place to land. Is this route public or authenticated? Should it accept POST? Which schema applies? Does the route normally receive browser traffic, mobile app traffic, server-to-server calls, or partner API clients? Those details turn raw logs into security evidence.

Schema drift is often the first useful warning. A new field, missing auth header, unexpected content type, excessive payload size, or method change may be harmless deployment noise. It may also be the start of probing. Detection has to keep both possibilities visible until the team can decide.

Signals That Show Intent

API threat detection usually combines several weak signals rather than one perfect signal. Payload inspection can catch injection attempts, command strings, suspicious encodings, and malformed JSON. Auth signals show repeated 401s, scope failures, token replay, stale API keys, or a client trying routes outside its normal permission set.

Behavioural cadence matters as much as payload. A human-backed integration has pauses, retries, and business rhythms. Automation often works through object IDs, pages, or search queries in a steady sequence. AI agents and scraping tools may adapt their paths, but they still leave patterns in route sequence, response codes, fingerprint drift, and origin pressure.

Network context adds another layer. Residential proxy use, unusual ASN movement, first-seen TLS or HTTP/2 fingerprints, and header combinations can help group traffic that appears to come from many IP addresses. The goal is not to identify a person. It is to decide whether the requests belong to a trusted workflow, a risky client, or an active attack.

Cross-Route Correlation

The strongest API threats often cross routes. A credential stuffing run may start with login failures, move into successful sessions, then test account details, saved addresses, order history, password reset, and checkout. Looking at the login endpoint alone misses the account takeover pattern.

A concrete example: an API sees a burst of failed POST /login calls from rotating proxies, followed by a small number of successful logins from the same TLS fingerprint. Within minutes those sessions call GET /account, POST /account/email, and GET /orders. Each request is valid in isolation. Together they show likely credential abuse moving into account change attempts.

Detection should connect those events before the sensitive action completes. That means keeping route, identity, session, fingerprint, response code, and selected action in a common evidence trail.

Detection Must Lead to a Response

A detection page is incomplete if it stops at alerts. Operators need a way to choose the least risky action for the evidence in front of them. Some events should only be logged for review. Others should tighten a route-level rate limit, require step-up verification, block a payload, quarantine an API key, or protect the origin during a Layer 7 surge.

Response quality depends on feedback. If a policy blocks legitimate partner traffic, the logs should show which route, key, schema check, or fingerprint caused it. If a mitigation works, the team should see reduced failures, lower origin pressure, fewer schema violations, or a cleaner traffic mix.

Peakhour treats API threat detection as part of the request path, not as a separate reporting exercise. WAF, API schema, bot, proxy, rate, and log-forwarding signals are most useful when they explain the same decision. That is how teams move from "something looks strange" to a reviewable action on a specific route.