Enterprise · Zero-trust AI
Contain, observe, and control autonomous AI agents before they ever touch your data. Agents that only see what they should - with full audit trails and enterprise controls.
The problem
Enterprises are racing to deploy AI for undeniable productivity gains - but security teams are simultaneously firefighting AI-related incidents and struggling with governance gaps.
of enterprises have experienced at least one AI-related security incident
of IT leaders lack confidence managing Copilot's security and access risks
barrier to AI agent adoption: data security concerns - not model quality or UX
Architecture
Not generic "we care about security" marketing. Real containment, least privilege, observable agents, and lifecycle governance - built into every layer.
Each agent runs in its own container with network boundaries and access policies defining which external systems it can reach.
Permissions are defined per agent: which databases, apps, folders, or APIs it can access. Multiple agents for different departments with strictly separated access.
Tools are registered with allowed operations. High-risk actions (wire transfers, data exports, policy changes) require human approval.
Every decision and tool call is logged with correlation IDs. Logs stream into SIEM/SOC tools (Splunk, Datadog, etc.) for monitoring.
Business and Enterprise customer data is excluded from AI model training by default. Your prompts, documents, and agent conversations are used to run your workloads - not to improve ours.
Examples
Each agent is scoped to its role. Compromise in one never exposes the rest.
Can do
Cannot do
Blast radius
Limited to AP/AR data and read-only ERP access. Worst case: delayed report, never unauthorized transfer.
Can do
Cannot do
Blast radius
Scoped to ticketing system and KB. Worst case: wrong ticket update - never data exfiltration.
Can do
Cannot do
Blast radius
PII access is constrained and fully logged. Worst case: draft error - never unauthorized disclosure.
Governance
Built to meet the requirements of CISOs, compliance teams, and regulators.
Admins, agent owners, and observers each get precisely scoped permissions across agents and the admin console.
High-risk actions - payments, PII exports, policy changes - require human sign-off before execution.
Replay any agent's decisions for forensics, compliance checks, or internal audit. Every action timestamped with rationale.
Architecture aligns with Zero Trust, NIST, ISO 27001, GDPR, and SOC 2 principles. Deploy in your VPC if required.
Stream agent logs into Splunk, Datadog, or your existing monitoring stack. Custom log enrichment available.
We collaborate with your security team: shared architecture docs, threat modeling, penetration test results, and custom controls.
Integration
Donely reduces the perceived risk of "yet another platform" by fitting into your existing identity, governance, and monitoring infrastructure.
Respect your SSO providers and SCIM provisioning. No separate identity silos.
Agents use scoped service accounts - never broad user impersonation across your org.
Donely is for custom, workflow-heavy agents where micro-segmentation matters most.
Pilot path
Start small, prove value, and expand with confidence.
Work with your security and business teams to pick 1-2 low-risk but valuable workflows for the pilot.
Scope data and tools, define policies, set approval flows, and align on logs and monitoring requirements.
Run for 30-90 days with full audit logs, then review results, adjust policies, and expand scope.
Questions
Get started
Book a call with our team. We'll map your use cases, define agent policies, and start a governed pilot.