AWS Bedrock + Areebi

Areebi integrates with AWS Bedrock to deliver enterprise governance across Amazon's managed foundation model service. Bedrock provides access to leading models - Claude, Llama, Amazon Titan, and more - through a unified API within your AWS environment. Areebi adds the governance layer that AWS-native tools lack: DLP scanning at the prompt level, user-identity-aware audit logging, and granular policy enforcement through a centralised policy builder.

For organisations already invested in AWS, Bedrock offers the advantage of keeping AI workloads within their existing cloud footprint, leveraging IAM for authentication, VPC for network isolation, and CloudTrail for infrastructure logging. Areebi builds on this foundation by adding AI-specific governance: content-level DLP that inspects what users actually send to models, audit trails that capture the full context of AI interactions (not just API call metadata), and policies that control which users can access which models.

The integration authenticates using AWS IAM roles or access keys, supporting cross-account access patterns and AWS Organizations. Governance policies apply uniformly across all Bedrock models - whether a user is interacting with Claude, Llama, or Titan, the same DLP rules, audit logging, and access controls are enforced.

Bedrock's multi-model architecture means organisations may use different foundation models for different tasks. Areebi ensures consistent governance regardless of which model is selected. The DLP engine scans every prompt for PII, PHI, financial identifiers, and custom data patterns before the request reaches any Bedrock model. This is critical because AWS CloudTrail logs API metadata but does not inspect or govern prompt content.

Audit logging in Areebi captures the complete interaction: user identity, workspace, model ID, token consumption, prompt content (or redacted version per policy), and response. These logs complement CloudTrail by providing the content-level visibility that compliance teams need for SOC 2 and HIPAA audits. Logs can be exported to CloudWatch, S3, or any third-party SIEM for unified security monitoring.

Policy enforcement allows administrators to control model access per user group - restricting Claude to the legal team, Llama to engineering, or Titan to customer support. Token budgets and rate limits prevent runaway costs, and cost allocation tags map every API call to a user, workspace, and department for accurate chargeback. All policies are managed through Areebi's policy builder and take effect immediately without redeployment.

AWS provides a comprehensive compliance foundation with certifications including SOC 2, HIPAA, FedRAMP, and PCI DSS. Areebi extends this with AI-specific governance controls that AWS's general-purpose security services do not address. While GuardRails and CloudTrail provide infrastructure-level monitoring, they do not perform content-level DLP on AI prompts or provide user-aware audit trails for AI interactions.

For organisations using multiple Bedrock models alongside other LLM providers, Areebi provides a unified governance experience. The same DLP rules, audit log format, and policy framework apply across Bedrock, Azure OpenAI, direct API integrations, and local models. Workspace isolation ensures business units operate independently with tailored governance configurations.

Visit the trust centre for security architecture documentation, review pricing for AWS-aligned enterprise plans, or request a demo to see Bedrock governance in your AWS environment.