AI Use Cases · Security & Governance

Where Security Teams Put Governed AI Under Policy

Your teams are already running AI on regulated data — often through tools no one reviewed. Here's how security and compliance use OptScale AI to discover that usage, redact sensitive data, log every interaction, and enforce policy — without banning the tools people rely on.

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Up to 60%

Lower AI spend, workload-dependent

<10ms

Routing overhead

100%

Of gateway traffic traced

On-prem

Or cloud-hosted, your terms

Shadow-AI Discovery Data Loss Prevention Audit-Ready Logging Policy Enforcement Agent Governance On-Prem & Open Source

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Use case 01

Shadow-AI Discovery

Teams and apps across the org call whichever model is easiest — through providers and accounts security never reviewed. There's no inventory of what's actually in use.

RiskYou can't govern, or report on, AI you can't see; unknown providers quietly process regulated data on your behalf.

GovernedEvery model, provider, and prompt routed through the gateway is surfaced as a live inventory — who's using what, where, and how much.

OutcomeShadow AI becomes an observed, measurable surface you can act on — not a periodic audit that's stale the day it ships.

AI traffic · discovered

Unapproved SaaS & modelsdiscovered

Provider · team · volumemapped

Consumer chatbot accountsflagged

Full usage inventorylogged

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Use case 02

Data Loss Prevention

Staff paste PII, source code, and customer records into public models inside ordinary prompts — the single most common way sensitive data leaves the building.

RiskFile-based DLP rarely sees a prompt, so it never fires; sensitive fields reach an external model before anyone notices.

GovernedDetection built for prompts redacts PII, secrets, and regulated fields before anything leaves your perimeter, with deny-by-default access to approved models only.

OutcomeProductivity stays, exposure doesn't — sensitive data never leaves, and every block is on the record.

Prompt · field filtering

Question + task contextallowed

PII · secrets · source coderedacted

Interaction recordlogged

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Use case 03

Audit-Ready Logging

Compliance needs to show what AI did, with which data, by which model — for any interaction, going back months, whenever a regulator or examiner asks.

RiskWithout a per-interaction record, evidence gets reconstructed under pressure — or simply doesn't exist when it's needed.

GovernedEvery prompt, model, version, and response written to a tamper-evident trail, mapped to the framework you report against — EU AI Act, NIST AI RMF, ISO 42001, SOC 2.

OutcomeReproducible evidence on demand — the same record whether it's your first query or your millionth.

Interaction · audit trail

Inputs + data sourceslogged

Model + version + promptlogged

Tamper-evident recordretained

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Use case 04

Policy Enforcement

Your acceptable-use policy exists on paper, but nothing checks it at the moment a prompt is sent. Enforcement depends on people remembering the rules.

RiskA policy no runtime reads doesn't stop a single call; the gap between what's written and what's enforced is where incidents happen.

GovernedApproved-models-only, deny-by-default, and per-team and per-workflow rules applied to every call at the gateway — automatically.

OutcomeYour policy becomes a control that executes on every request, not a document nobody reads.

Model request · policy

Approved model + roleallowed

Unapproved modelblocked

Decision + rule appliedlogged

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Use case 05

Agent Governance

Teams build and run agents — LangChain, CrewAI, or custom — that act on their own across your systems, tools, and data stores.

RiskAgents drift off-task, burn budget, loop, or connect to MCP servers and vector stores no one approved.

GovernedOptScale AI doesn't run your agents — it governs them: register each one, set cost, time, and recursion limits, detect loops and drift live, and block unauthorized MCP servers and vector stores.

OutcomeAutonomy you can trust to stay in bounds — no runaway spend, no unapproved data. See AI Agent Control for the full detail.

Registered agent · live controls

Cost / time / recursion capsenforced

Loop & drift detectionalerting

Unauthorized MCP / vector storeblocked

Every action + accesslogged

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Use case 06

On-Prem & Sovereign Deployment

Regulated and sovereignty-conscious orgs need AI governance without shipping sensitive prompts to someone else's cloud — and without adopting a control layer they can't inspect.

RiskA closed control plane becomes its own dependency and data-residency question — the thing meant to reduce risk adds some.

GovernedDeploy SaaS or fully on-premises on an open-source core; keep sensitive prompts inside your network and route only approved traffic outward.

OutcomeGovernance that fits your data-residency and sovereignty requirements — with no lock-in on the control plane itself.

Deployment · your perimeter

Sensitive prompts in-networkkept

Approved traffic outboundallowed

Open-source coreno lock-in

Want the feature detail behind these controls — how the guardrails, DLP, and logging actually work?
‍See AI Security & Guardrails →

Bring your AI usage under policy this quarter

See how OptScale AI discovers shadow AI, redacts sensitive data, logs every interaction, and enforces your policy — on your own traffic, SaaS or on-prem.

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