Built for governed AI execution
AI agents shouldn't have unlimited freedom. Dezifi gives security and platform teams the controls to deploy AI safely across the enterprise.
Controlled Tool Access
Agents only get access to the tools and actions explicitly assigned to them.
Approval Workflows
High-risk actions can require human approval before execution.
Audit Trails
Every run, tool call, decision, approval, and output is logged for traceability.
Policy Enforcement
Define business rules and operational policies that agents must follow.
Environment Boundaries
Separate sandbox, staging, and production workflows.
Role-Based Access
Different users and teams can have different levels of access and control.
Explainability
Inspect why an agent took a certain action and what information it used.
Define what agents can — and can't — do.
Policies bind agents to specific tools, actions, data scopes, and approval requirements. Run them in sandbox, validate, then promote to production.
agent: sales_agent
environment: production
tools:
- salesforce.read
- salesforce.update:
requires_approval: true
approvers: [revops-leads]
- slack.send:
channels: ["#sales-ops"]
data:
pii: redact
scope: account_owner
limits:
max_steps: 12
max_cost_usd: 0.50Security is the platform, not a feature
Dezifi was designed for teams whose security review happens before the pilot, not after. Isolation, encryption, and auditability are structural — they hold whether or not anyone remembered to configure them.
Multi-tenant isolation
Every workspace is a fully isolated tenant. Agents, integrations, knowledge bases, credentials, users, and audit logs are scoped to one workspace — nothing leaks across boundaries, from the first session.
Encryption everywhere
Data is encrypted in transit and at rest. Integration credentials and API keys are stored encrypted and injected at runtime — agents never see raw secrets, and neither do their logs.
Runtime guardrails
Guardrails run inside the execution path, not beside it. PII redaction, prompt-injection checks, and policy violations block the action before it executes — a denied tool call never reaches the target system.
Audit-ready by default
Every run, tool call, model response, approval, and override is logged with actor, timestamp, and full context. Compliance reviews replay exactly what an agent did and why.
Frequently asked questions
What security and platform teams ask before deploying AI agents.
- How does Dezifi keep AI agents from taking dangerous actions?
- Three layers: policies scope which tools and actions an agent can use at all, approval gates require a human sign-off for high-risk actions, and runtime guardrails inspect every step and block violations before execution — not after.
- How is tenant data isolated?
- Each workspace is an isolated tenant with its own agents, integrations, credentials, knowledge bases, users, and logs. Cross-workspace access does not exist in the data model, so isolation is structural rather than policy-dependent.
- Where do integration credentials live?
- Credentials are stored encrypted at the workspace level and injected at runtime with the narrowest scope the policy allows. Agents, prompts, and traces never contain raw secrets.
- Can agents access PII?
- Only if you allow it. Guardrail profiles can redact PII from model inputs and outputs, and policies can restrict data scope per agent — for example, limiting a sales agent to accounts its requesting user owns.
- What audit evidence does Dezifi produce?
- A complete, immutable run history: every prompt, tool call, model response, policy decision, guardrail verdict, approval, and human override, each attributed and timestamped. Exportable for SOC 2, ISO 27001, and internal audit workflows.
- Does Dezifi support on-premise or private cloud deployment?
- Yes. For regulated environments, agents can execute inside your own infrastructure so data, credentials, and model traffic never leave your network. Talk to us about deployment options.