You can finish an AI red-teaming evaluation with a long report and one question still unanswered: does this application enforce its rules?
For a model comparison, responses may be enough. For an agent that changes customer records, you also need to know whether the application allowed a change. That difference should shape the shortlist before you compare features.
PyRIT, garak, Inspect, and DeepTeam are useful starting points for teams building their own evaluations. General Analysis, Check Point's Lakera products, Mindgard, HiddenLayer, Zscaler's SPLX, and Enkrypt AI offer commercial assessment capabilities. They differ in integration work, what they observe, and how much of the evaluation your team owns.
What this comparison establishes#
This is a documentation-based buying guide, reviewed on September 4, 2026. We opened the official repositories, documentation, and product pages linked below. We did not run a new head-to-head benchmark or test every commercial product.
General Analysis publishes this guide and sells AI red-teaming services and software. Our own product descriptions receive the same treatment as competitors' descriptions: they establish advertised capabilities, not independently measured superiority.
The recommendations are our interpretation. Where public documentation is incomplete, we identify what to ask rather than assume a feature is absent.
See how your AI systems hold up under real attacks
General Analysis maps AI applications and agents, red teams prompts, retrieval, tools, MCP servers, browser actions, permissions, and business workflows, then turns findings into evidence your team can reproduce and retest.
Quick comparison#
| Tool | What the primary source describes | When to consider it | What you still need to establish |
|---|---|---|---|
| PyRIT | Framework for generative AI security evaluations | Your team wants to customize evaluation logic | Integration effort, scoring quality, and maintenance ownership |
| garak | Model scanner built around generators, probes, and detectors | You need a repeatable model-level baseline | Whether the integration observes the application behavior you care about |
| Inspect | Evaluation framework with tasks, tools, scoring, and logs | You need shared infrastructure for security and capability evaluations | The security-specific cases and acceptance criteria your team must supply |
| DeepTeam | Framework for assessing LLMs and agents | You want an application-oriented evaluation framework | How its categories and scoring map to your application's policy |
| General Analysis | Automated assessment of AI applications and agents | You want assessment support around a deployed application | Exact integration, deliverables, and the work retained by your engineers |
| Check Point / Lakera Red | AI security assessment and red-teaming capabilities | You are comparing assessment and runtime protection together | Which capabilities are included in the proposed service or subscription |
| Mindgard | Application and model testing, CLI, SDK, and remediation guidance | Your team wants programmatic access to a commercial testing product | Application visibility, result export, and integration maintenance |
| HiddenLayer | AI Attack Simulation within a platform spanning discovery, supply chain, and runtime security | Your evaluation is part of a broader AI security purchase | What each module covers in your environment |
| SPLX / Zscaler | AI assessment technology brought into Zscaler | You are considering AI testing alongside an existing security platform | Current packaging, support, and integration commitments |
| Enkrypt AI | Agent and multimodal assessment, coverage reports, and regression suites | You need to compare coverage across agents, tools, and modalities | Which claimed surfaces are demonstrable in your application |
These are different operating models, not ten interchangeable entries in a league table.
PyRIT vs garak vs Inspect vs DeepTeam#
PyRIT: own the evaluation logic#
Microsoft's PyRIT repository describes a framework for security professionals assessing generative AI systems. That makes it a reasonable candidate when an engineering team wants to assemble and maintain its own evaluation.
The procurement question is staffing. Who writes the application integration, checks scoring errors, and updates the suite when the product changes? A framework can give that person useful components. It cannot take responsibility for their judgment.
garak: start with a model-scanning baseline#
NVIDIA's garak repository separates model access, test probes, and detection. That structure is useful when you want a consistent model-level assessment across supported interfaces.
A scanner result describes what its detector observed. If the business risk depends on a downstream permission check or database change, establish whether the integration can observe that outcome before treating a response-level result as an application finding.
Inspect: share evaluation infrastructure#
Inspect is an evaluation framework from the UK AI Security Institute. It supports evaluation tasks, scoring, tools, and logs; it is not a ready-made security certification.
Consider it when security evaluations should share infrastructure with your existing model or agent evaluations. The remaining work is deciding what constitutes a failure and supplying appropriate cases. Keeping that decision explicit is more useful than importing a score with no agreed meaning.
DeepTeam: map application policies to evaluations#
DeepTeam's repository describes assessments for LLMs and AI agents. It is another option for teams that want a framework rather than a managed engagement.
Compare its supported categories with your policy. For example, an application may be allowed to discuss a sensitive subject while being prohibited from disclosing a customer's records. A category label alone will not resolve that distinction.
Open-source tools are not limited to prototypes. Their production suitability depends on the integration, case quality, and maintenance you provide.
Commercial platforms: what to ask beyond the demo#
General Analysis#
Our automated red-teaming product page describes assessments for AI applications and agents. Consider us when you want to discuss security evaluation of the deployed application, including its connected tools.
Ask us for a scoped deliverable using your architecture, the evidence an engineer will receive, and what happens after a finding is fixed. Do not substitute our description of the product for that evaluation.
Check Point / Lakera Red#
The current Lakera documentation is branded Check Point AI Security and describes red teaming as well as expert assessment. It should not be reduced to “a prompt filter.”
Clarify whether a quote covers a managed assessment, platform access, or both. If you are also buying guardrails, establish which findings can inform those controls and which require an application change.
Mindgard#
Mindgard's documentation exposes a CLI and Python SDK alongside testing and remediation material. That makes programmatic integration a concrete part of the comparison.
Ask for a sample result in the format your engineers will use. Confirm who maintains the connection to your application and how you can export results if you stop using the service.
HiddenLayer#
HiddenLayer's documentation describes four modules: discovery, attack simulation, supply-chain security, and runtime security. Its assessment product includes security policy evaluation and red-team evaluation.
A broad purchase may fit an organization responsible for both model artifacts and deployed applications. Ask which modules are necessary for your first use case; breadth on a product page does not establish coverage in your deployment.
SPLX / Zscaler#
SPLX announced its acquisition by Zscaler. Older standalone-product comparisons can therefore miss the current buying and support arrangement.
Validate the current offering directly. Ask which assessment capabilities are available now, how they connect to existing controls, and what is contractual rather than planned.
Enkrypt AI#
Enkrypt AI's agent red-teaming page describes coverage across agents, RAG, tools, MCP, and multiple modalities. It also describes coverage maps and regression suites.
The useful next question is whether the report distinguishes tested surfaces from advertised ones. Request an example of each deliverable before assuming a dashboard entry means your integration has been assessed.
AI red-teaming evaluation worksheet#
Use this original General Analysis worksheet before a vendor call or an internal build decision.
Download the evaluation worksheet as CSV. The complete checklist is also below; no form is required.
For each row, record an owner, a link to the relevant evidence, and one status: documented, demonstrated, not supported, or unknown. “Documented” means a source makes the claim. “Demonstrated” means your team has seen it work in the agreed setting. Neither is a blanket statement that the system is secure.
| Decision | Evidence to request | What remains to decide |
|---|---|---|
| Application boundary | Diagram of the components actually included in the assessment | Which relevant components are excluded? |
| Authorization and data handling | Agreed scope, data-retention terms, and approved environment | Can the assessment proceed without exposing live customer data? |
| Policy definition | Written allowed and disallowed outcomes for the application | Who resolves ambiguous cases? |
| Observable outcomes | Sample report distinguishing model responses from application actions | Can it verify the outcome that matters to the business? |
| Coverage | Report of assessed surfaces and untested areas | Are critical requirements missing? |
| Scoring quality | Scoring criteria and examples of reviewed false positives | Who checks disputed findings? |
| Benign behavior | Results for ordinary allowed tasks alongside security results | Does the proposed control make legitimate work fail? |
| Finding handoff | Sample engineering ticket with affected component and recommended control | Can an engineer act on it without another sales call? |
| Change management | Versioned configuration and a way to compare later assessments | Who maintains cases when the application changes? |
| Operating cost | Quote plus model usage, integration, and maintenance estimates | What does the first year cost, not just the first run? |
| Ownership and export | Named owner, support commitments, and result-export format | Can the team keep useful findings if it changes tools? |
Do not add the rows into an arbitrary “security score.” A missing required integration can rule out a candidate regardless of how many other boxes it checks. Conversely, you do not need to buy supply-chain scanning to solve an output-validation problem.
Cost and coverage: compare the same job#
A free repository does not mean a free evaluation. Budget for integration, inference, result review, and maintenance. A commercial quote may cover some of those costs, but ask which ones.
Avoid comparing raw failure counts across tools. Counts can change with the tested scope, number of cases, scoring rules, and treatment of duplicates. Ask for the denominator and review process before interpreting a percentage.
Keep the assessment environment and application version consistent where practical. Record differences that cannot be held constant. If two products assessed different systems, say so instead of manufacturing a ranking.
Choose a starting point#
If you have engineers who will own the evaluation, compare the four frameworks against a small set of written requirements. If you need additional assessment support, take the same requirements and worksheet to the commercial candidates.
For a broader purchase that includes discovery or runtime protection, use the AI security platform comparison. If you already know which behavior needs a runtime control, go to the guardrails comparison. For the underlying concepts, start with What is AI red teaming?.
AI red teaming tools FAQ
Choosing a framework, comparing evidence, and budgeting for an AI security evaluation.
- What is the best AI red teaming tool in 2026?
There is no single winner across model scanning, custom evaluations, and managed application assessments. PyRIT, garak, Inspect, and DeepTeam offer different starting points for engineering teams. Compare commercial services when you need additional integration, reporting, or assessment support, and ask every candidate for evidence from your own use case.
- How do PyRIT and garak differ?
PyRIT is a framework for assembling custom generative AI security evaluations. garak is organized as a model vulnerability scanner with generators, probes, and detectors. The useful distinction is how much evaluation logic you want to own, not that one is universally more secure.
- Can open-source tools support production evaluations?
Yes, with suitable integrations, maintained test cases, validated scoring, and an owner. Open source versus commercial does not determine whether the application boundary is covered. Check what the chosen setup can observe and what engineering work remains.
- What should an evaluation report contain?
The tested application and configuration, scope and exclusions, observed versus expected behavior, scoring criteria, reviewed findings, and limitations. Record untested features as unknown rather than treating them as passed.
