AGIMA

AI Gateway Emerges as Unified Control Layer for Enterprise LLM Access and Data Protection

As organizations scale LLM usage beyond individual enthusiasts to dozens of teams, IDEs, internal agents, and automations, AI Gateway becomes essential infrastructure for security, cost management, and reliability. The solution acts as a single entry point that authenticates users, enforces policies, performs DLP checks, depersonalizes sensitive data, routes requests across providers, and enables detailed auditing and billing. Companies face strict requirements under Russian Federal Law 152-FZ on personal data, along with risks of leaks involving API tokens, client information, and internal code when employees bypass official channels. Mature implementations from Cloudflare, Kong, and Microsoft Azure API Management already demonstrate analytics, rate limiting, fallback mechanisms, and governance features. Compatibility with OpenAI API reduces migration friction when switching between providers, including Chinese models, while handling edge cases like tool calls and vision capabilities. The article stresses separating AI Gateway responsibilities from DLP, MCP Gateway, and guardrails to avoid creating an unmanageable monolithic system. Early implementation priorities include ownership, comprehensive auditing from day one, real-world client compatibility testing, shadow-mode DLP, and clear documentation of production architecture.

Fine-Tuning Prompt Injection Detector: Five Rounds, Four Failures and Gates Against Regression

AGIMA technical director Andrey Nepryakhin details the challenges of adapting an open-source GLiNER-based prompt injection detector for Russian corporate traffic. The system processes nearly one million requests monthly from eighty employees using agentic tools. Five fine-tuning rounds using LoRA adapters on microsoft/mdeberta-v3-base were conducted, yet only the second round reached production. Failures stemmed from regression on previously fixed attacks, imbalanced negative samples, and reliance on rule-based signals instead of the model. The team introduced cumulative corpora, person-based splits, and ablation checks against the base model to prevent silent rollbacks. A gradient mass rule limits any single phenomenon to 10-20 percent of training data to avoid precision or recall collapse.