Protegrity DAIS Platform
Policy-driven AI and data security enforcement.
Protect sensitive data at rest, in motion and in use — and control how AI agents see, share or act on it.
The Architecture
Transparent policy enforcement across all data, analytics and AI workflows.
Define policies once and enforce them across databases, files, SaaS endpoints, agent context, prompts, responses and tool results. Protected data stays usable, even for joins, aggregates and reasoning, so security stops being a tax on analytics and AI.
Swipe horizontally to explore the architecture diagram.
Platform pillars and shared foundation
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Pillar 01
Data Protection
Secures the data itself — at the source and wherever it flows.
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Pillar 02
Agent Security
Verify agent identities and tool permissions — what they can access, and for whom.
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Pillar 03
Inference Controls
Prevent sensitive data and knowledge exposure via model prompts and responses.
Foundation
Discovery & Policy
The governance layer beneath all three pillars: discover sensitive data, agents and tools, then define how data is protected, what agents can access and which tools they can use.
Shared Core
- Scanner & Classifier
- Policy Manager
- Session Vault
What you get
Finally put sensitive data and AI agents to work.
AI projects that ship.
Protection built into data and agent workflows gives security teams what they need to approve, so projects reach production sooner.
Policies set in one place.
Protection and access rules apply consistently across your data, applications and agents.
Privacy with full utility.
Semantic encryption keeps data meaningful, so analytics and AI can deliver insights without exposing anything sensitive.
Provable governance.
Every policy decision produces a record, so auditors see exactly what happened, how and why.
One platform for data and AI.
Data protection, agent security and inference controls from one vendor, with no gaps between tools.
Why it’s different
Proven data protection, extended to agents and AI.
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Protection that never drops.
Each data element is protected individually, so protection holds as data moves from databases to prompts to agents.
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Agents verified with users.
Dual-principal trust verifies both the agent and the person directing it before any data is accessed or action taken.
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Control over every tool call.
Policies decide which tools each agent can use and what data it can send to external services and models.
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Enforced where data lives.
Native protectors, gateways and an agent control plane plug into existing systems without moving data or rebuilding applications.
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Analytics on protected data.
The secure query gateway rewrites queries so joins and aggregates run on protected values without revealing the originals.
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Proven at the largest scale.
The same field-level protection trusted by the largest financial and healthcare institutions now extends to agents and inference.
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