Use cases

How teams use Mirror.

Buyer-shaped scenarios. Each one ladders to one of the three promises: data it can never see, rules it can never bend, proof you can never erase.

Grouped by buyer type

Different needs. One platform.

Every partner, industry, and role has its own page with the curated subset of scenarios that matter to that buyer.

By partner shape

What you're building, and how Mirror plugs in.

By industry

The regulatory and credibility lens.

By role

Same platform, different first question.

All scenarios

Three promises. 17 ways teams use them.

Grouped by which promise each scenario delivers. Use the product chips to narrow by Mirror module.

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Computes on data it can never see. Encryption · FHE 8

Computes Encryption · FHE

Bring AI to the data you can't move

Your most regulated records sit on the wrong side of the AI line because every model has to decrypt to compute.

Computes Encryption · FHE

Encrypted context, memory, and vector DB for every model and agent

Knowledge bases, vector stores, conversation history, agent memory. Every piece of context an AI reaches for is plaintext the moment it's loaded.

Computes Encryption · FHE

Joint analysis with partners who can't share their data

Reinsurers, banks, hospitals, and governments want shared intelligence but can't legally pool the underlying records.

Computes Encryption · FHE

Fine-tune on customer data without ever seeing it

Domain models need real customer data to be useful. Training in plaintext means you hold what you swore you wouldn't.

Computes Encryption · FHE

Pick the encryption posture per workload, not per gateway

Some routes need raw speed. Others need FHE all the way through. You shouldn't have to pick one for the whole org.

Computes Encryption · FHE

Let AI review code that never leaves your machine

To use an AI code tool you usually hand it your source, secrets, and proprietary logic. And trust a retention promise.

Computes Encryption · FHE

Run AI inside an air-gapped or sovereign boundary

Defence, government, and regulated workloads can't legally move data outside their boundary. So they're locked out of AI entirely.

Computes Encryption · FHE

Stop your model from being cloned out the back door

Distillation attacks turn your API into someone else's competing model in weeks.

Acts under rules it can never bend. Identity · Policy 5

Acts Identity · Policy

Find every agent before it finds your data

Copilots and automations are spreading inside the perimeter. The ones nobody approved have standing access and no owner.

Acts Identity · Policy

Stop the wrong agent action at runtime, not in a dashboard

Agents act with standing access. The most you get back today is a chart explaining what already broke.

Acts Identity · Policy

Govern what your agents reach for

MCP servers, plugins, and SaaS tools quietly extend agent blast radius. Almost nobody has a policy at that boundary.

Acts Identity · Policy

One endpoint for every model, with policy at the door

Each team hits its own provider its own way. Keys sprayed across services, prompts egressing in the clear, no single place that can say what was asked or whether it was allowed.

Acts Identity · Policy

Catch the risk before launch, block it in production

Pre-prod findings die in a Jira backlog. Runtime sees attacks the red team never imagined.

Leaves proof it can never erase. Proof · Evidence 4

Leaves Proof · Evidence

How teams use Mirror.

Different needs. One platform.

Three promises. 17 ways teams use them.

Bring AI to the data you can't move

Encrypted context, memory, and vector DB for every model and agent

Joint analysis with partners who can't share their data

Fine-tune on customer data without ever seeing it

Pick the encryption posture per workload, not per gateway

Let AI review code that never leaves your machine

Run AI inside an air-gapped or sovereign boundary

Stop your model from being cloned out the back door

Find every agent before it finds your data

Stop the wrong agent action at runtime, not in a dashboard

Govern what your agents reach for

One endpoint for every model, with policy at the door

Catch the risk before launch, block it in production

Know which attacks your AI would actually survive

Show the auditor what your AI did, and why

Answer security questionnaires with evidence, not adjectives

Explain in plain English what an agent did and why

See encrypted AI security in action.