data-ai MCP Server
PRISM D1 Velocity — A framework for measuring and scaling AI-assisted development. Workshop modules, Strands agents, MCP integration, enhanced DORA metrics, and AWS-native observability for engineering teams adopting AI-DLC.
Discovered via github-topic:mcp and last synced 5mo ago.
Install instructions not detected yet
Check the source repository for the latest setup steps.
[Prerequisites](workshop/00-prerequisites/)
Source
Daily+
[Dashboards & Visibility](workshop/06-dashboards-visibility/)
Incident → resolution
Name
Duration
Experimental
[AI-SDLC Foundations](workshop/01-ai-sdlc-foundations/)
Integrated
[Agent Development](workshop/02-agent-development/)
Orchestrated
[Spec-Driven Development](workshop/03-spec-driven-development/)
[Instrumenting AI Metrics](workshop/04-instrumenting-ai-metrics/)
Technology
[Eval Gates in CI/CD](workshop/05-eval-gates-cicd/)
Structured
Autonomous
Detecting exposed AWS credentials in source code repositories, CI/CD pipelines, and configuration files using TruffleHog, git-secrets, and AWS-native detection mechanisms to prevent credential theft and unauthorized account access.
Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.
Implement Amazon Macie to automatically discover, classify, and protect sensitive data in S3 buckets using machine learning and pattern matching for PII, financial data, and credentials detection.
Implementing AWS Security Hub to aggregate security findings across AWS accounts, enable compliance standards like CIS AWS Foundations and PCI DSS, configure automated remediation with EventBridge and Lambda, and create custom security insights for organizational risk management.
Flexible and powerful framework for managing multiple AI agents and handling complex conversations
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The secure gateway connecting AI agents to enterprise systems.
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