Elite AI hackers aren’t born. They’re trained.
Elite AI hackers aren’t born. They’re trained.
Terra requires a human pentester in the loop, which means it can’t scale to match the demand of continuous Offensive Security. Novee is built for fully autonomous AI penetration testing – see why the distinction matters.
Terra excels at amplifying the human pentester, putting them in command of a suite of AI agents. But that means Terra requires a human in the loop for key stages of pentesting, limiting speed and scalability.
Requires a pentester to operate.
Doesn’t scale.
No proprietary offensive model.
Findings and fixes routed through people.
Continuous offensive testing across a portfolio demands a flexible platform that both scales human pentesters and allows them to generate valuable, actionable findings without direct intervention.
Autonomous execution at portfolio scale.
A proprietary offensive AI stack.
Autonomous validation, runnable evidence.
Closed-loop, stack-specific remediation.
Built for continuous workflows.
Predictable per-asset pricing.
| Capability | Novee AI Pentesting | Terra |
|---|---|---|
| Operating model | Autonomous and continuous. Novee runs the engagement itself and scales across the portfolio, both maximizing the effectiveness of existing pentesters and running without their direct intervention. |
Human-operated by design. Terra gives pentesters direct command over AI agents, with key decisioning kept with the operator. |
| Offensive capability | Proprietary offensive reasoning model, post-trained on real attacker tradecraft and orchestrated via proprietary harness with frontier models selected per task (multi-model). |
Orchestrates AI agents under pentester direction; no proprietary offensive model described. |
| Validation & evidence | Three independent agents (a finder, validator, and a blind re-validator) plus deterministic checks, run without a human in the loop. Every finding includes a working exploit, replication steps, and a PoC script. |
Agentic AI plus human oversight, with the pentester in the validation loop; delivers pentester-signed, audit-ready reports. No independent multi-agent blind validation or runnable PoC scripts described. |
| Remediation | Generated tailored to your WAF, backend, and codebase, and automatically retested to confirm the fix held and catch regressions. |
Guidance designed to be interpreted and applied by the directing pentester; auto-retest supported. |
| Pricing | Predictable per-asset pricing. Depth and frequency don’t increase cost, and coverage doesn’t require more operators. |
No fixed price; coverage scales with operator effort, so more applications means more pentester time. |
| Workflows and integrations | Native CI/CD and change-triggered workflows; connected to CI/CD, fixes drop to the code level. |
No CI/CD integration described; AWS partnership confirmed, no named ticketing or CI/CD integrations on the site. |
| Continuous, change-triggered testing | Runs autonomously the moment code ships, or at regularly-scheduled intervals. |
Continuous and change-based. Tests and validates on meaningful production changes, operated through the pentester-in-command model. |
| Application context | Asset Intelligence Model. A persistent, per-asset model of purpose, roles, permissions, workflows, APIs, and business logic that compounds every cycle and never resets. |
Agents learn in-step with your context and code, onboarded once to avoid constant rescoping; persistence within an engagement, with no cross-assessment compounding. |
| Coverage | Web apps, APIs, mobile apps, and AI agents/LLMs across the external attack surface. |
Web applications, internal applications, network infrastructure, and AI red teaming. |