LangProtect
Challenge
Enterprises are adopting AI across employee workflows, internal tools, customer-facing applications, and autonomous agent environments, but most traditional security controls are not built for these new interaction layers. The core challenge behind LangProtect was to create a platform that could secure AI usage in real time across multiple risk surfaces without slowing adoption.
The biggest problems included lack of visibility into shadow AI usage, rising exposure to prompt injection and jailbreak attacks, unsafe outputs in production AI applications, and growing risks from autonomous agents and MCP-connected workflows that could access sensitive data or trigger high-risk actions without proper guardrails. Traditional DLP and security tools could scan files, keywords, or movement patterns, but they could not understand the intent, semantics, or behavior of AI interactions. That left enterprises with fragmented controls, weak auditability, and delayed detection after incidents had already occurred.
The challenge was not just technical. The platform also had to support enterprise governance, policy enforcement, audit readiness, and performance at scale while remaining practical for production environments. Quokka Labs needed to design a solution that balanced runtime protection, governance depth, fast risk detection, and operational usability in one unified system.
Enterprises are adopting AI across employee workflows, internal tools, customer-facing applications, and autonomous agent environments, but most traditional security controls are not built for these new interaction layers. The core challenge behind LangProtect was to create a platform that could secure AI usage in real time across multiple risk surfaces without slowing adoption.
The biggest problems included lack of visibility into shadow AI usage, rising exposure to prompt injection and jailbreak attacks, unsafe outputs in production AI applications, and growing risks from autonomous agents and MCP-connected workflows that could access sensitive data or trigger high-risk actions without proper guardrails. Traditional DLP and security tools could scan files, keywords, or movement patterns, but they could not understand the intent, semantics, or behavior of AI interactions. That left enterprises with fragmented controls, weak auditability, and delayed detection after incidents had already occurred.
The challenge was not just technical. The platform also had to support enterprise governance, policy enforcement, audit readiness, and performance at scale while remaining practical for production environments. Quokka Labs needed to design a solution that balanced runtime protection, governance depth, fast risk detection, and operational usability in one unified system.
Solution
Quokka Labs designed and developed LangProtect as a unified enterprise AI security and governance platform that protects AI interactions across employees, AI applications, and agent/MCP environments. The solution was built to provide real time visibility, inline runtime protection, centralized policy enforcement, and audit-ready governance in a single platform.
The product included several tightly connected security layers. For AI applications, LangProtect Armor applied inline runtime enforcement during live request-response cycles to inspect prompts, context, and outputs before harmful behavior could reach downstream systems. For employees using public or unsanctioned AI tools, the platform enabled shadow AI monitoring and governance through browser-level visibility and contextual policy enforcement. For agentic environments, LangProtect introduced centralized controls for agents and MCP workflows with RBAC, semantic intent analysis, tool-call validation, resource guardrails, and DLP-aware protections.
Key capabilities included prompt injection and jailbreak defense, PII and PHI detection and redaction, toxic and unsafe content control, runtime AI application security, shadow AI visibility, agent guardrails, RAG protection, and structured compliance evidence. The solution was engineered using React and Vite on the frontend, Python 3.12 with FastAPI, Uvicorn, Pydantic, and SQLAlchemy Asyncio on the backend, Redis-powered scan services, PyTorch and Transformers for the AI protection layer, Electron for interceptor capabilities, and browser extension technologies for monitoring and enforcement. This architecture allowed the platform to remain scalable, fast, and enterprise-ready
Quokka Labs designed and developed LangProtect as a unified enterprise AI security and governance platform that protects AI interactions across employees, AI applications, and agent/MCP environments. The solution was built to provide real time visibility, inline runtime protection, centralized policy enforcement, and audit-ready governance in a single platform.
The product included several tightly connected security layers. For AI applications, LangProtect Armor applied inline runtime enforcement during live request-response cycles to inspect prompts, context, and outputs before harmful behavior could reach downstream systems. For employees using public or unsanctioned AI tools, the platform enabled shadow AI monitoring and governance through browser-level visibility and contextual policy enforcement. For agentic environments, LangProtect introduced centralized controls for agents and MCP workflows with RBAC, semantic intent analysis, tool-call validation, resource guardrails, and DLP-aware protections.
Key capabilities included prompt injection and jailbreak defense, PII and PHI detection and redaction, toxic and unsafe content control, runtime AI application security, shadow AI visibility, agent guardrails, RAG protection, and structured compliance evidence. The solution was engineered using React and Vite on the frontend, Python 3.12 with FastAPI, Uvicorn, Pydantic, and SQLAlchemy Asyncio on the backend, Redis-powered scan services, PyTorch and Transformers for the AI protection layer, Electron for interceptor capabilities, and browser extension technologies for monitoring and enforcement. This architecture allowed the platform to remain scalable, fast, and enterprise-ready
Results
LangProtect delivered a strong combination of security coverage, speed, and operational governance for enterprise AI adoption. The platform achieved measurable outcomes that demonstrated both technical effectiveness and business readiness. It monitored more than 100 AI tools, detected over 1 lakh prompts, supported 20+ governance policies, reached 99 percent sensitive data coverage, and enabled risk detection in under 5 milliseconds.
These results gave enterprises stronger visibility into employee AI activity, better runtime protection for production AI applications, and more reliable control over autonomous agent workflows. Instead of relying on fragmented point tools or post-incident reviews, organizations could detect and govern risks in real time. The platform also improved compliance readiness by creating structured audit evidence and governance records that security and compliance teams could use during internal reviews or external audits.
From a delivery standpoint, Quokka Labs also accelerated the build timeline significantly. While a traditional AI security platform of similar scope could take roughly 24 to 32 weeks across planning, development, integration, testing, and compliance validation, LangProtect was delivered in approximately 8 to 12 weeks through an accelerated AI-driven workflow. This showed Quokka Labs’ ability to execute complex enterprise AI products faster without sacrificing architecture quality, governance depth, or production readiness.
LangProtect delivered a strong combination of security coverage, speed, and operational governance for enterprise AI adoption. The platform achieved measurable outcomes that demonstrated both technical effectiveness and business readiness. It monitored more than 100 AI tools, detected over 1 lakh prompts, supported 20+ governance policies, reached 99 percent sensitive data coverage, and enabled risk detection in under 5 milliseconds.
These results gave enterprises stronger visibility into employee AI activity, better runtime protection for production AI applications, and more reliable control over autonomous agent workflows. Instead of relying on fragmented point tools or post-incident reviews, organizations could detect and govern risks in real time. The platform also improved compliance readiness by creating structured audit evidence and governance records that security and compliance teams could use during internal reviews or external audits.
From a delivery standpoint, Quokka Labs also accelerated the build timeline significantly. While a traditional AI security platform of similar scope could take roughly 24 to 32 weeks across planning, development, integration, testing, and compliance validation, LangProtect was delivered in approximately 8 to 12 weeks through an accelerated AI-driven workflow. This showed Quokka Labs’ ability to execute complex enterprise AI products faster without sacrificing architecture quality, governance depth, or production readiness.