Repello AI Alternatives: An Exit and Coexistence Matrix

Repello AI spans several security jobs: AI asset discovery, ARTEMIS red teaming, ARGUS runtime security, coding-agent visibility and agent threat modeling. An alternatives review should split those jobs before naming a replacement. The exit and coexistence matrix below preserves attack evidence and retest history while keeping runtime request authorization at its actual HTTP boundary.

TL;DR

• Treat Repello AI as a portfolio of discovery, red-team, runtime and specialist agent-security modules.
• Preserve assets, attack objectives, conversations, findings, mitigations and retest history before replacing ARTEMIS.
• Compare browser tests and connected-API tests separately because they exercise different reachable surfaces.
• Keep ARGUS guard policies distinct from DeepInspect authorization on authenticated user or agent-to-LLM HTTP calls.
• Use coexistence when red-team evidence should improve production policy without turning either record into the other.

Repello AI covers several control surfaces

The Repello product page presents AI Asset Discovery, ARTEMIS AI Red Teaming, ARGUS AI Runtime Security, Workstation Lens for coding-agent security, Agent Wiz for threat modeling and Whistleblower for system-prompt exposure. That portfolio reaches assets, user interfaces, APIs, agent connections and production interactions. A single "replacement" column would hide which job the candidate actually performs.

Start the matrix with one row per module. Add columns for protected surface, deployment route, input, action, evidence, owner, retention, export, candidate, coexistence path and acceptance test. Mark every row direct replacement, partial replacement, adjacent control or retained module. My opinion is blunt: any shortlist that gives one vendor a green check for "AI security" has already failed the architecture review.

The physical test artifact should fit on one wide monitor. A reviewer needs to see the asset ID, attack objective, observed path, finding, mitigation, retest and production-control link without opening six browser tabs.

ARTEMIS alternatives need workflow-level tests

Repello's AI red-teaming page says ARTEMIS tests supported chatbots, AI agents and retrieval-based applications through a browser interface or connected API. It challenges multi-turn behavior, untrusted content, data boundaries and tool use. Findings include the attack path, observed behavior and mitigation guidance, and teams can rerun the objective after changing the application.

Evaluate alternatives against that workflow rather than the phrase "automated red teaming." Build a fixed corpus with a prohibited disclosure objective, a prompt-injection path through retrieved content, an unsafe tool action and a cross-account data test using controlled identities. Record the starting state, prompts, uploads, model and application versions, tool calls, outputs, finding severity, evidence and mitigation. Rerun the same objective after remediation.

Browser and API modes deserve separate rows. Browser testing reaches the interface a user sees, including supported uploads and multi-turn interaction. Connected API testing reaches the endpoint exposed for assessment. A candidate that supports one route should receive credit for that route. It should not inherit the other by implication.

Preserve attack evidence before an exit

Export the AI asset inventory first. Keep the Repello identifier, name, owner, environment, interface, endpoint, model, retrieval sources, connected tools, data boundaries and test identities. Then export every campaign and attack objective with its configuration, run time, application version and execution route.

A complete finding preserves the full attack conversation, uploaded artifacts, tool sequence, observed behavior, screenshots or response captures, risk score, mapped framework references, mitigation guidance, assignee, status and reviewer history. Link every retest to the original finding and keep both outcomes. A successful retest cannot erase the evidence that opened the issue.

Use checksums for exported artifacts and retain a manifest. Import a representative campaign into the candidate, rebuild one finding packet and rerun its objective. Acceptance requires evidence continuity and repeatable behavior. The AI security vendor evaluation guide provides a broader procurement frame for these tests.

Runtime defense and red teaming are different jobs

Repello states directly that ARTEMIS testing is separate from runtime protection. Its FAQ assigns weakness testing to ARTEMIS and configured guard-policy enforcement in connected production workflows to ARGUS, alongside application authorization and security controls.

Create two rows in the matrix. ARTEMIS produces adversarial test evidence. ARGUS evaluates configured guard policies on connected production traffic. For each ARGUS deployment, record the route it sees, supported interaction types, policy source, action, failure behavior, latency budget, logging fields and rollback method. Replay one allowed interaction and one configured violation, then remove a required dependency to observe the failure posture.

A red-team finding can inform a runtime rule, but the records remain different. Link the finding ID to the policy change, approval and production test. Keep the original attack evidence with ARTEMIS or its replacement. Keep the runtime decision with the enforcement component that made it. The AI policy enforcement guide explains why custody matters.

Authenticated HTTP request authorization is narrower

DeepInspect evaluates authenticated HTTP model calls deliberately routed through its stateless proxy. On that route, it can use caller identity, delegated role, destination, prompt classification and policy to permit, redact or block a request, then write a per-decision record.

That boundary is narrower than the Repello portfolio. DeepInspect does not discover every AI asset, operate a browser to run multi-turn red-team campaigns, inspect local coding-agent actions, authorize MCP tools or build application threat models. A browser test conducted by ARTEMIS also remains outside DeepInspect unless the resulting model call crosses a proxy route controlled by the customer.

For coexistence, pass stable identifiers across the systems. Link the Repello asset and finding IDs to the application route and DeepInspect policy version. Use a confirmed attack path to create a regression test, then verify that the relevant authenticated model request receives the intended policy decision. The AI gateway guide helps teams place that route without confusing it with a full application-security test.

Exit and coexistence acceptance matrix

Run one representative agent workflow through discovery, threat modeling, ARTEMIS testing, remediation, retest and any connected ARGUS policy. Add a routed model call with a controlled identity and a policy result. Compare these artifacts:

• Asset identifiers, owners, interfaces, endpoints, models, retrieval sources and connected tools.
• Attack objectives, execution route, complete conversation, uploads, tool sequence and observed outcome.
• Finding severity, evidence, framework mapping, mitigation, assignment and status history.
• Retest relationship, application version, changed control and final result.
• Runtime policy version, route, caller identity, action, timestamp and failure posture.
• Export format, checksums, retention, access roles and restoration steps.

Keep rollback until the candidate reproduces the chosen attacks and restores the evidence packet without administrator edits. When coexistence is the outcome, test correlation after upgrades on both sides. Revoke old connector credentials only after the final export and residual-write check.

DeepInspect

DeepInspect adds a request-level control to the coexistence matrix. For authenticated user or agent-to-LLM HTTP calls deliberately routed through its proxy, it evaluates identity, route, destination, prompt data and policy before forwarding allowed traffic, then writes a per-decision record.

Link Repello findings to DeepInspect policy only where a real routed model call exists. Book a technical deep dive at deepinspect.ai.