The AWS MCP servers let AI agents query your AWS account — IAM, Secrets Manager, S3, and data services. Here's the setup, the real secrets and infrastructure risks, and how Strac governs every tool call with remediation and a full audit trail.
AWS publishes a family of MCP servers (and community servers exist) that expose AWS capabilities to AI agents over the Model Context Protocol — from documentation and CDK to cost analysis and, increasingly, direct data and resource access. Connected, an agent can read account configuration, query data services, and operate AWS on your behalf.
That's powerful for cloud engineering. It also means an external agent now reaches some of the most sensitive surfaces you run.
The same reach that makes an agent a capable cloud engineer is why every call needs governance.
1. Access keys and Secrets Manager values. An agent with AWS MCP access can read the secrets store directly — the highest-value target in your account.
2. IAM and infrastructure config. Roles, policies, and architecture detail an attacker would love, returned in plain text.
3. Data services. S3 objects, RDS rows, and Redshift query results — PII, PCI, and PHI pulled straight into a model.
4. CloudWatch logs. Logs leak credentials and request bodies, now reachable by agents.
Traditional DLP doesn't sit in the MCP path — the response goes straight into the model's context. See the ingress shift.
Strac is the governance gateway between AI agents and the AWS MCP servers. You see every query each agent makes. You control what it can reach and block high-risk actions (reading Secrets Manager, exporting data). You protect the response — secrets, keys, and regulated data are remediated inline. And you prove it with a full audit log.
Access-only tools answer "who called what." They do not see the access key returned from Secrets Manager or the PII in an RDS query result. Strac sits inline on every AWS tool call: it detects and remediates the secrets and data inside — redact, mask, block, or revoke — approves or blocks risky actions per agent, and keeps the audit trail. The call and its contents.
One inline pass over each response — five actions, your policy:
One control plane across the full MCP connector directory.
AWS MCP servers expose AWS capabilities — docs, CDK, cost analysis, and increasingly direct resource and data access — to AI agents over the Model Context Protocol, so agents can build and operate AWS.
Not without governance. An agent can reach IAM, Secrets Manager, and your data services, returning secrets and regulated data to the model. An MCP-layer control like Strac remediates and blocks before that happens.
Secrets — an agent reading Secrets Manager or access keys — plus IAM/infra config and the data in S3/RDS. Strac blocks high-risk reads and remediates the rest.
Yes — Strac exposes a standard MCP gateway endpoint, so any MCP-aware client routes AWS tool calls through it with one config change.
AWS DLP for S3 protects data at rest in AWS stores. AWS MCP DLP governs AI agents accessing AWS over MCP — the ingress path. Most teams need both.
Related reading: MCP DLP · AWS DLP for S3 · Snowflake MCP Server · MCP connector directory · AI Agent Governance · Cloud DLP
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