
Shriti Pandey contributed to IBM/mcp-context-forge by engineering security-focused plugins and policy enforcement features using Python, FastAPI, and Docker. Over five months, Shriti integrated LLMGuardPlugin for prompt analysis, developed Open Policy Agent and Cedar-based RBAC plugins, and optimized policy evaluation with asynchronous processing and batch support. Their work emphasized robust error handling, logging, and plugin architecture, enabling granular access control and scalable, policy-driven security. Shriti also enhanced operational reliability by improving Redis integration and enriching plugin context with user attributes. The depth of these contributions established a maintainable, extensible backend foundation for secure, high-performance policy management across contexts.

February 2026 monthly summary for IBM/mcp-context-forge: Focused on Cedar policy evaluation performance optimization by introducing asynchronous processing and batch support to boost throughput and provide more flexible policy management. The work aligns with the Fix/cedar plugin optimization (#2554) commit and strengthens readiness for high-volume policy evaluation.
February 2026 monthly summary for IBM/mcp-context-forge: Focused on Cedar policy evaluation performance optimization by introducing asynchronous processing and batch support to boost throughput and provide more flexible policy management. The work aligns with the Fix/cedar plugin optimization (#2554) commit and strengthens readiness for high-volume policy evaluation.
January 2026 monthly summary for IBM/mcp-context-forge focused on reliability, RBAC readiness, and performance improvements. Delivered targeted features and bug fixes across Redis integration, the OPA plugin, and plugin context enrichment to drive business value in security, availability, and performance.
January 2026 monthly summary for IBM/mcp-context-forge focused on reliability, RBAC readiness, and performance improvements. Delivered targeted features and bug fixes across Redis integration, the OPA plugin, and plugin context enrichment to drive business value in security, availability, and performance.
Month: 2025-12 | IBM/mcp-context-forge. Delivered two core policy-and-plugin improvements focused on security, reliability, and maintainability. Emphasized Cedar-based policy enforcement and robust error handling to support scalable, policy-driven access control across contexts.
Month: 2025-12 | IBM/mcp-context-forge. Delivered two core policy-and-plugin improvements focused on security, reliability, and maintainability. Emphasized Cedar-based policy enforcement and robust error handling to support scalable, policy-driven access control across contexts.
October 2025 monthly summary for IBM/mcp-context-forge: Delivered Open Policy Agent (OPA) plugin enhancement with expanded hooks and policy enforcement across prompt and resource pre/post operations, accompanied by comprehensive tests and documentation. This work strengthens security posture, policy governance, and operational control across the platform.
October 2025 monthly summary for IBM/mcp-context-forge: Delivered Open Policy Agent (OPA) plugin enhancement with expanded hooks and policy enforcement across prompt and resource pre/post operations, accompanied by comprehensive tests and documentation. This work strengthens security posture, policy governance, and operational control across the platform.
September 2025 (IBM/mcp-context-forge) – Focused on strengthening security and extensibility through the LLMGuardPlugin integration for prompt analysis and security. Delivered guardrails to analyze inputs/outputs for prompt injection, toxicity, and data privacy (anonymization/deanonymization), with configurable single-plugin and multi-plugin setups, plus caching and vault management for secure cross-plugin data sharing. This work establishes a scalable, policy-driven security layer and foundation for future guardrails.
September 2025 (IBM/mcp-context-forge) – Focused on strengthening security and extensibility through the LLMGuardPlugin integration for prompt analysis and security. Delivered guardrails to analyze inputs/outputs for prompt injection, toxicity, and data privacy (anonymization/deanonymization), with configurable single-plugin and multi-plugin setups, plus caching and vault management for secure cross-plugin data sharing. This work establishes a scalable, policy-driven security layer and foundation for future guardrails.
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