Comparison · AI security platforms
Vincosha vs Prompt Security
A GenAI security platform protecting both employee use of AI tools and organisations' own AI applications, with inspection and policy applied at the prompt and response layer.
What Prompt Security is genuinely good at
Prompt Security covers both directions — employee-facing AI use and the applications a company builds — and prompt-layer inspection catches a real class of problem: sensitive data heading into a model, and obvious injection attempts arriving in a prompt. For an organisation whose AI risk is concentrated in chat interfaces and homegrown apps, that is well-matched.
choose Prompt Security if
- You need inspection and policy at the prompt and response layer, in real time.
- Your risk is concentrated in employee chat usage and applications you built yourself.
- You want content-level controls: redaction, blocking, sensitive-data detection.
choose Vincosha if
- Your risk is what agent tools load before any prompt is constructed.
- You need supply-chain controls — signing, pinning, scanning, revocation — for skills, hooks and MCP servers.
- You need to reconstruct which artifact versions were active in a session after the fact.
The structural difference
Both products take prompt injection seriously and approach it from opposite ends, which makes this a genuinely useful comparison rather than a positioning exercise.
Prompt-layer inspection works on the content of a request and a response. It can detect sensitive data heading out, flag recognisable injection patterns, and enforce content policy in real time. That is worth having, and for chat-shaped usage it addresses much of the exposure.
Our position is that for *agents* the prompt layer is too late to be the primary control, and the reason is structural rather than a criticism of any implementation. Injected instructions can be arbitrarily rephrased, encoded, or split, so content detection is a probabilistic filter rather than a boundary — a point the OWASP guidance on prompt injection makes directly. More importantly, the highest-consequence agent attacks do not present as a suspicious prompt at all: a hook that reads credentials and posts them somewhere constructs no prompt, and a poisoned tool description looks like ordinary tool metadata.
So we work earlier. Assay scans skills, hooks, rule files and MCP manifests before they reach a laptop and quarantines what fails. Registry pins exact versions so an approved artifact cannot be silently swapped. Ledger records which versions were active in which session, so an injection incident is reconstructible instead of theoretical. Content inspection and supply-chain vetting are complements — but if a team can only build one, we would argue the artifact layer removes more risk per unit of effort for agent-heavy organisations.
Where we actually overlap
Both target prompt injection. Prompt Security filters content at request time; we constrain which artifacts can shape a request and record what was active. The strongest posture runs both, and neither claim requires the other to be weak.
Frequently asked
- Can prompt-layer filtering stop prompt injection?
- It raises the cost of unsophisticated attempts and is worth having. It cannot be a boundary, because payloads can be rephrased, encoded or hidden in content the filter never sees — and the worst agent attacks involve no suspicious prompt at all.
- Why is the artifact layer the higher-leverage control for agents?
- Because it is deterministic. A hook either matched the reviewed hash or it did not. A skill either came from an approved publisher at a pinned version or it did not. Content detection is a judgement call at request time; artifact vetting is a verification before anything runs.
Verify this yourself
This page describes Prompt Security at the level of what its category is architecturally, reviewed on 2026-07-25. It asserts no specific feature, price or version, because those change and we cannot verify them from here. Check their site — and challenge ours.