Hawk AI MCP Server™ · Security & Audit
MCP Server Security & Audit for AI-Agent Database Access
When AI agents can query production data, governance can’t be optional. The Hawk AI MCP Server enforces scoped tokens, access control, masking, read-only limits, and a per-agent audit trail on every call — so automation never becomes an open door.
Scoped tokens Read-only enforcement Per-agent audit 
The risk
An AI agent with a raw connection string is a standing breach
Wiring an agent straight into a database with full rights means no least-privilege, no masking, and no record of what it did. The Hawk AI MCP Server removes that risk by making every agent a governed identity behind DBHawk's zero-trust control plane.
Scoped tokens
Each agent authenticates with a token bound to a role and a defined scope — not a shared, all-powerful credential.
Read-only by default
Limit agents to read-only or specific operations; writes and DDL are blocked unless you explicitly allow them.
Masking & ACL
Object- and column-level access control and dynamic masking apply to agent queries, exactly as they do for people.
Controls on every call
The same gauntlet, every request
Authenticate the agent’s scoped token and resolve its role.
Authorize against object, column and row policy before the query runs.
Enforce read-only limits and block disallowed operations.
Mask sensitive fields in the results returned to the agent.
Audit the tool call, query and result — per agent, per token.
The audit trail
Every agent action, attributable and reviewable
Each MCP call is logged against the agent’s identity and token, with the tool invoked, the SQL executed, and the outcome — ready to stream to Datadog, Splunk, or your own database.
# MCP audit event
agent: “reporting-bot” token: tok_9f2…c1 role: analyst-ro
tool: run_query datasource: sales-prod mode: read-only
policy: ACL✓ mask(ssn,card)✓ rows(region=EU)✓
result: 142 rows · masked · 38ms status: allowed
ts: 2026-09-03T14:22:07Z user: svc:reporting-bot
agent: “reporting-bot” token: tok_9f2…c1 role: analyst-ro
tool: run_query datasource: sales-prod mode: read-only
policy: ACL✓ mask(ssn,card)✓ rows(region=EU)✓
result: 142 rows · masked · 38ms status: allowed
ts: 2026-09-03T14:22:07Z user: svc:reporting-bot
FAQ
MCP security & audit — questions
How do you stop an AI agent from over-accessing data?
Every MCP call is authorized against DBHawk's object-, column- and row-level policy before it runs, scoped to the agent's token and role. Dynamic masking redacts sensitive fields in the results, and read-only limits block writes and DDL unless explicitly permitted — so an agent can only ever reach the data its role allows.
Is every agent action logged?
Yes. Each tool call — the agent identity, token, datasource, query, policy applied, and outcome — is recorded in the audit trail and can be streamed to your SIEM (Datadog, Splunk) or stored in your own database.
Can I revoke or rotate an agent's access?
Yes. Because each agent uses a scoped token tied to a role, you can rotate or revoke that token and adjust its scope centrally, without touching the underlying database credentials.
Does this support our compliance requirements?
The combination of least-privilege authorization, masking, read-only enforcement, and a complete per-agent audit trail supports the same SOC 2, GDPR, HIPAA, SOX and PCI-DSS controls you apply to human access.
Bring AI agents inside your zero-trust perimeter
See how the Hawk AI MCP Server governs and audits every agent query under the controls you already trust.