Governing Amazon Bedrock Agents with ArgusAI

Amazon Bedrock and the Data Governance Challenge

Amazon Bedrock provides enterprise-grade access to foundation models and its agent orchestration capabilities are accelerating the adoption of complex AI workflows. As teams deploy Amazon Bedrock Agents to automate tasks, these agents gain the ability to query, retrieve and act on data across the organization. This introduces new data protection challenges, as agents operating without data-aware context can create pathways for unintentional data exposure or compliance breaches.

Bedrock Data ArgusAI addresses this governance challenge by providing a framework to connect agent configurations to underlying data sensitivity. The platform moves beyond simple discovery to build the interconnected context required to apply and automate data security policies for AI workflows.

Step 1: Detecting and Mapping Amazon Bedrock Agents

Upon connection to an AWS account, Bedrock Data ArgusAI automatically discovers Amazon Bedrock Agents and Knowledge Bases and maps their full configurations.

This mapping identifies:

• The knowledge bases each agent is configured to access and their respective data bill of materials (DBOM)
• The Guardrails linked to those agents.
• The foundation models powering each agent (e.g., Claude, Titan, Mistral).
• The invocation context, including prompt templates, Lambda functions and data connectors.

This process builds a topology of agent-to-data-source connections, mapping the potential flow of data through the AI ecosystem.

The screenshot below displays agents currently configured in an AWS environment.

Step 2: Classifying Data and Aggregating Risk to Agents

Once agents and their DBOM are mapped, Bedrock Data classifies the data within those connected datastores. The platform’s analysis engine identifies and categorizes sensitive data types, including:

• Personally Identifiable Information (PII)
• Financial data
• Healthcare identifiers
• Proprietary business data

Each datastore is assigned an Impact Score that reflects the type and volume of sensitive data it contains. This analysis is then used to create aggregated Impact Scores for the Amazon Bedrock Agents that access those datastores. This provides a risk profile for each agent, allowing teams to identify which agents interact with the most sensitive information and prioritize them for governance.

The accompanying screenshot illustrates the specific categories of sensitive data identified within a datastore.

Step 3: Assessing Agent Configurations for Safe Content Handling

After data sensitivity is mapped, Bedrock Data ArgusAI assesses each agent’s configuration to determine whether appropriate controls are in place to mitigate sensitive data leakage risk. The platform analyzes each agent’s Guardrail setup and evaluates its effectiveness in protecting against sensitive data exposure.

This automated analysis examines:

• Whether data masking or redaction filters are enabled for model inputs.
• Whether output filters or moderation policies are applied before responses are returned.
• Whether context filters or PII detectors are active for retrieval-augmented generation (RAG) workflows.

This Guardrail Gap analysis provides a data-driven understanding of how well each Amazon Bedrock Agent is governed and where remediation may be needed to ensure compliant AI operations.

The configuration illustrated in the following screenshot demonstrates how a Guardrail is applied to an agent, controlling the flow of data to or from the model.

Step 4: Applying Remediation

When ArgusAI’s Guardrail Gap Analysis detects that an Amazon Bedrock Agent may be exposing sensitive information, its remediation workflows allow teams to take precise action. The interface allows for inspection of the agent + Guardrail configuration to pinpoint where data exposure risks exist and provide steps needed to update the Guardrails configuration.

The remediation workflow, illustrated in the following screenshot, details the steps for correcting a Guardrail that is inadvertently exposing sensitive data.

Guardrails can be configured to block, mask, or redact sensitive content before it reaches a foundation model or end user. Connecting detection directly to remediation capabilities helps convert potential data leaks into controlled, auditable workflows, ensuring AI-driven processes align with organizational security standards.

Step 5: Continuous Monitoring and Proactive Alerts

Amazon Bedrock environments are dynamic; new agents are deployed, data sources evolve and sensitivity levels shift. Bedrock Data’s continuous monitoring engine keeps pace with these changes.

Whenever a developer updates an agent’s data source or new sensitive data appears in a datastore, Bedrock Data ArgusAI automatically:

• Re-scores the agent’s risk level based on the latest configuration and data profile.
• Notifies compliance or security teams with actionable alerts.
• Recommends targeted remediation steps, such as applying a PII redaction filter or tightening Guardrail policies.

This active monitoring process ensures the AI governance posture remains adaptive as the environment evolves.

The Result: Safe AI Adoption Without Slowing Innovation

By bridging the gap between AI development and data security, Bedrock Data ArgusAI helps teams confidently scale Amazon Bedrock Agents. This approach provides:

• A persistent map of AI agent data connections
• Risk-based prioritization based on correlated data sensitivity
• Reduced risk of sensitive data leaks via AI or policy violations

Enterprise AI requires a data-aware governance model. By correlating agent behavior with data sensitivity, Bedrock Data ArgusAI provides the necessary context to secure and scale Amazon Bedrock automations responsibly.