AI-Native Offensive
Security for the
Modern Attack Surface

ThreatCanary is a continuous attack-path validation platform. It discovers exposed assets and APIs, connects them through graph intelligence, and validates which attack paths are realistically exploitable. It combines three layers — external attack surface management (EASM), API behavioural intelligence, and AI offensive security — so exposure is assessed the way an attacker would chain it rather than as isolated findings.

A vulnerability scanner identifies known conditions, usually by matching a version, banner or signature against a predefined check. ThreatCanary goes a step further and validates whether the weakness is actually reachable and exploitable in your environment, and what it chains into. The output is evidence-backed findings with reproduction detail rather than a list of theoretical issues that still need triage.

Traditional EASM answers what exists on your perimeter, and API security tools answer what APIs exist and how they behave. ThreatCanary uses both as inputs and then asks the harder question: how those exposed systems and APIs can actually be used against you. Exposure, API behaviour, identity context and vulnerability data are correlated in one graph so findings carry attack-path context instead of sitting in separate tools.

ThreatCanary constrains autonomous testing with explicit scope definitions, approval gates, tool-trust settings and execution limits, so activity stays inside authorised boundaries. Scope management defines exactly what the platform is permitted to discover, test and validate. Deterministic systems produce the evidence and validation trail, which means results are auditable and reproducible rather than opaque model output.

Customers get a living map of exposed assets, APIs and relationships as they change; validated findings backed by reproducible evidence rather than theoretical scanner output; attack-path context explaining what can be chained and why it matters; and role-specific outputs for CISOs, security teams, developers, GRC and platform owners.

ThreatCanary uses AI for reasoning, adaptation, hypothesis generation, reporting and advisory workflows — the parts that benefit from judgement. Deterministic systems handle discovery, validation, evidence capture and reproducibility. That split is deliberate: AI decides what is worth testing, while the evidence proving a finding is produced and reproducible by deterministic means.