An attack that was never tried can still work.
A red-team exercise explores selected ways an adversary could achieve an objective. In code security, that leaves a coverage problem: the accounts, routes, data, and attack sequences tested are only part of what an application can permit. A missed exploit does not establish that the code enforces the right boundary.
Traditional red teaming is bounded by the engagement.
Human testers choose scenarios within an agreed scope and time window. They may expose a serious attack chain while never exercising another endpoint, tenant role, or unusual sequence that contains a separate flaw. Showing one way in also does not identify every code path with the same underlying weakness.
AI automation increases attempts, not certainty.
AI-automated red teaming can generate tests, explore routes, and attempt attacks through tools. It still depends on the paths it discovers, the credentials it receives, the environment it can reach, and the tests it selects. More payloads do not establish that every relevant permission or data flow was examined.
Some automated systems also inspect source code or validate findings independently. Those capabilities matter. The limitation is treating the attacks attempted, or their absence of success, as a complete account of code security.
Galen examines the code that permits the attack.
Galen’s web application analysis starts from retained facts about entry points, calls, data flows, and controls. It uses those relationships to establish supported vulnerabilities and can compare access behavior with a reviewed policy baseline. Source-backed discovery does not depend on a live test selecting the right account, record, or request sequence.
Suppose one invoice route enforces tenant ownership while another route only checks sign-in. A live exercise that uses the first route can miss the second. Galen’s source analysis can examine the relevant paths and show where the ownership restriction is absent, with evidence tied to the affected operation.
The advantage for code review is direct: your team gets the cause, the affected path, and the protection that needs to change. The finding’s derivation is checked against its underlying facts, and AI can help explain or repair the issue without becoming the authority that declares it real.
Three approaches. Different foundations for a finding.
| Code-security question | Traditional red teaming | AI-automated red teaming | Galen |
|---|---|---|---|
| How are issues pursued? | Testers select and adapt attack scenarios within the engagement. | Agents select tests and use tools to attempt attacks within their configured scope. | Deterministic analysis relates code facts, paths, data flows, and relevant controls. |
| What can leave a flaw unseen? | An untested route, identity, or attack sequence. | An undiscovered path, missing test access, or an attack the agent never selects. | An unsupported stack, incomplete application model, or an evidence gap. |
| What supports the conclusion? | Observed behavior and artifacts from the tested scenario. | Tool results, reproduced attacks, and any independent validation the system performs. | Inspectable code evidence and a checked derivation of the emitted finding. |
| How does it guide a code fix? | A demonstrated attack establishes the behavior; tracing its source may require further work. | A system may propose a patch; the underlying cause and correction still need verification. | The affected path and control evidence focus the correction on its code-level cause. |
| How is the result checked again? | Re-run the agreed scenario after remediation. | Re-run the relevant automated tests and inspect the result. | Re-analyze the changed code and check the path against the original finding. |
Galen’s advantage is establishing and explaining supported code weaknesses without depending on a successful attack in a test environment. Language support and proof gaps still define what the analysis covers; runtime checks can establish additional facts about deployment behavior.
Find the cause. Correct it. Check what changed.
Use Galen Security to investigate application code and give developers evidence they can act on. Use Galen Policy for supported checks while building, including changes that diverge from approved access rules. When you want Telhawk to lead the review, choose a Managed Security Audit.
After a patch, use the retained path and control evidence to recheck the current code. An approved policy baseline also provides a reference for unintended access changes in later reviews. Where live confirmation is needed, use the source evidence to focus the retest on the right endpoint, identity, and boundary.
Red teaming remains useful for broader attack objectives, operational detection, and response exercises. Galen addresses a different code-security need: evidence of what the implementation permits, why the weakness exists, and whether the correction changes the affected path.
Background: NIST’s red-team definition and technical security testing guide.