Problem

Most enterprises ship AI on hope.

They run a few prompts, the answers look fine, and the model goes live. Then a customer finds the edge case. Or a journalist does. Or a regulator does.

The failures are already in there. Prompt injection that rewrites your instructions. Outputs that leak data. Biased responses that hold up in a courtroom but not in a complaint. Models that pass every demo and break on the thousandth real query.

You cannot fix what you have not tested for. And testing AI by hand does not scale past the first ten prompts.

Test & Detect is the first layer of AI Assurance. It is how you know what your AI actually does, not what you hoped it would do.

The three layers of AI testing

Real AI testing has three layers. Most tools stop at the first.

Layer 1: Validators. 65 ML-based checks that score every output for bias, toxicity, data leakage, drift, and policy alignment.

Layer 2: Jailbreaks. 84 adversarial techniques, single- and multi-turn, that actively try to break your model.

Layer 3: Live Vulnerability Database. A continuous feed of newly published LLM vulnerabilities, fed straight back into your testing.

Plenty of tools do layer 1. Few do layer 2 well. Almost nobody runs layer 3 with a live feed. Disseqt does all three, in one place.

Capabilities

Validators

Sixty-Seven ML-based validators across four families.

• Base: bias, toxicity, data leakage, insecure output.

• RAG: faithfulness and grounding for retrieval systems.

• Agentic: topic-adherence and drift for systems that take actions.

• MCP: validation for tool-using and connected agents.

They run automatically, at scale, against the full range of inputs your system will face in production.

This is not a spot check. It is a continuous read on whether your AI is behaving the way you need it to, measured against criteria you can defend to a regulator.

Jailbreaks

Eighty-four adversarial attack patterns. Single-turn and multi-turn.

Single-turn attacks try to break your model in one message. Multi-turn attacks are harder and more realistic: they build pressure across a conversation, the way a determined user or a bad actor actually would.

Disseqt runs both against your system before launch, so the people probing your AI for weaknesses are on your side. You find it in private. You fix it before anyone else gets the chance.

Live Vulnerability Database

A continuously updated feed of emerging AI threats.

The attack surface for AI does not hold still. New jailbreaks, new injection techniques, and new failure modes appear constantly. A test that passed last quarter does not mean your system is safe today.

The Live Vulnerability Database keeps your testing current. New techniques surface constantly, from reversal of alignment decisions under pressure to poetry-framed jailbreaks. As they appear across the AI field, they flow into your testing coverage. Your AI gets retested against the threats that exist now, not the ones that mattered when you launched.

Built for scale

Three guided testing agents, reusable prompt packs, and cross-LLM benchmarking round out the layer.

The Responsible AI agent, Advanced Jailbreaking agent, and Vulnerability DB testing agent run campaigns for you, so you are not writing tests by hand. Pull from ready-made prompt packs for sales and CRM, HR, manufacturing, life insurance, or build custom packs for your own regulated workflows.

Benchmark the same pack across different LLMs side by side, so you choose the safer model with evidence, not vendor claims.

Differentiation

Test theirs, not ours

Disseqt is model-agnostic. We test your AI, on any model.

That means any commercial LLM, any open model, and your own custom or on-prem deployments. We do not push you toward a house model and then mark its homework. We test what you actually run.

Run the same prompt pack across several models at once and see which one holds up. You get a fact-based answer to the question every AI team is asking: which model is safe enough to ship?

ML validators, not LLM-as-judge

Most AI testing tools use one large language model to grade another. It is slow, it is expensive, and it burns a lot of resource to do it.

Disseqt uses ML-based validators instead. The result is measurable.

• Around 99% less water

• Around 98% less CO2

• Sub-50ms latency

You get faster, cheaper, more sustainable testing that runs at the scale enterprise AI actually demands. The same coverage, without the cost of running a judge model behind every check.

HOW IT WORKS

Four steps from connected model to defensible test report.

1. Connect your model. Point Disseqt at any LLM you run, including custom and on-prem. No need to migrate or rebuild.

2. Choose your coverage. Pick a prompt pack for your domain, or let a guided testing agent build the campaign. Validators and jailbreaks apply automatically.

3. Run the attacks in private. 65 validators and 84 jailbreak techniques probe your system, refreshed by the Live Vulnerability Database so you test against today's threats.

4. Get the evidence. Every result is scored, ranked by severity, and benchmarked across models, so you know what to fix and which model to ship. That same evidence flows straight into Protect & Enforce and Prove & Comply.

OBJECTIONS

"We already run our own evals." In-house evals cover the cases you thought of. Test & Detect adds 65 validators, 84 adversarial techniques, and a live feed of new vulnerabilities, at a scale manual testing cannot reach. It finds the failures you did not think to look for.

"Isn't this just monitoring?" Monitoring watches production after the fact. Test & Detect probes your system before launch, on purpose, with attacks you control. You find the failure in private, not in a post-incident review.

"Our model is custom, so off-the-shelf testing won't fit." Disseqt is model-agnostic. It tests any LLM, including custom and on-prem builds, and benchmarks them side by side. We test what you run, not a model we would prefer you used.

WHO THIS IS FOR

Built for the teams accountable for AI that ships.

• Enterprise IT and engineering teams shipping AI into production who need proof it holds up under pressure.

• FCA and SEC-regulated financial services carrying real consequences if an AI decision goes wrong.

• Global systems integrators and IT consulting partners assuring AI deployments for the enterprises they serve.

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THE CATEGORY

This is AI Assurance, a new category.

It is not GRC. It is not eval tooling. It is not monitoring on its own. AI Assurance sits between the application layer and your enterprise governance function, and it is where testing, protection, and proof come together.

Legacy GRC platforms and point tools were not built for this. Disseqt was. One platform, the full assurance lifecycle, with testing as the first move.

ONE PLATFORM, THREE PILLARS

Test & Detect is the first pillar in the AI Assurance Lifecycle. It finds the problems.

The other two pillars close the loop. Protect & Enforce applies guardrails and policy in production, stopping bad outputs in real time. Prove & Comply turns everything into audit-ready evidence regulators accept.

Disseqt is the only unified AI assurance platform covering testing, monitoring, policy, audit, and compliance in one place. You do not have to choose between observability and governance. You get both.