Where a control layer earns its place.
The problems security and platform teams are handed the moment AI reaches production — and how DSX Guard resolves each one.
Shadow AI, brought into the light
Employees already use ChatGPT, Claude and Gemini — usually without a policy behind it. Guard shows which assistants are in use, what kinds of data reach them, and where policy is being breached, without banning the tools people depend on.
Regulated data, public models
Teams need frontier models but cannot send customer, patient or account data outside. Reversible masking lets the work happen against a public model while the sensitive values never leave your tenant.
Prompt injection defence
A document or web page can carry instructions aimed at your assistant. Guard inspects prompts and attachments for injection, jailbreak and exfiltration patterns, and stops the request before the model acts on them.
One LLM endpoint for engineering
Every service integrating its own provider SDK means scattered keys, no shared limits and no central view. The AI gateway gives engineering one endpoint, and gives the platform team routing, quotas, cost tracking and policy in one place.
Agents over enterprise systems
Business teams want answers that live in Oracle, Salesforce or the warehouse. Governed agents reach those systems under existing permissions, inside the assistant the employee already has open.
Evidence for audit and regulators
"How do you control AI use?" needs a better answer than a policy document. Guard produces the record — who used what, what was masked or blocked, and under which rule — streamed into your SIEM.
Three teams, one platform.
Security
Gets enforceable policy, threat inspection and a complete event stream instead of a blocked-domain list.
Platform & engineering
Gets one endpoint, central key management and model routing that changes without a redeploy.
The business
Keeps the AI tools it already uses, plus governed access to enterprise data from inside them.