PROXY:AI

1. A knowledge-accounting layer for monitoring and budgeting the use of AI resources
2. Operational event registration, administrator analytics dashboards and provider-error monitoring
3. Defining rules that allow AI actions, require approval or block tools
4. A confidentiality mode for working with sensitive data
5. An MCP gateway with multi-level tool-access control at user, role and team level
6. Restricting and auditing access to tools, functions, code execution and terminals
7. Configuring AI request and response transformations without code through simple administrative rules

1. Controlled AI use through audits, access policies, human oversight, decision logging and model-usage documentation
2. Readiness for EU AI Act requirements, including visibility of models, policy decisions, events, risks and audit trails
3. Managing personal-data exposure by detecting, masking, blocking or escalating requests
4. Registers and operational evidence useful for AI management under ISO 42001
5. Regulatory compliance templates for the EU AI Act
6. An SBOM model and compliance reporting for Cyber Resilience Act requirements
7. Human Oversight mechanisms including AI-content labeling, FRIA/ASIA export and approval gates for critical actions
8. A compliance panel presenting policies, access decisions, events, risks and active models

1. A unified management point for communication with LLMs by applications, agents and users
2. Support for multiple cloud, local and on-premises AI model providers
3. Mapping errors from different providers to one format compatible with the OpenAI API
4. Routing models according to configuration, availability, cost, policies and user permissions
5. Visualization of a request's route through rules, models, providers and policy decisions
6. Fallback-model configuration and automatic switching when the primary model is unavailable
7. A model catalog with metadata including alias, provider, type, status and usage mode
8. Per-tier SLA isolation with dedicated instances and hardware-level GPU isolation
9. Declarative regional data residency in an EU-only per-tenant model
10. Defining AI resources as Custom Resource Definitions in Kubernetes
11. Protection against excessive spending by detecting unusual consumption patterns and terminating suspicious sessions
12. A dry-run panel for testing ABAC security rules before production activation
13. No-code AI request and response transformations
14. IDE integration through a dedicated plugin with real-time cost and token visibility
15. An LLM Playground for experiments, response comparison and parameter tuning
16. A Python and WASM plugin framework extensible through a marketplace
17. Model validation before launch in the AI environment
18. An internal-currency model for accounting for AI use, including per-action costs, budgets and transfers

1. Model-access decisions based on user, group, model, data-type and environment attributes
2. Predefined security-policy profiles for regulated sectors including finance, healthcare, public administration and energy
3. Real-time detection of abuse attempts including injection, jailbreak, prohibited content and token-consumption anomalies
4. Measuring the cost of every request by user, team, project and model with hierarchical budgets
5. A central versioned prompt registry
6. Prompt-template support and request decoration with central gateway rules
7. Caching recurring requests using semantic similarity with per-tenant key isolation

1. A dashboard for database-integration status, outbox/reconciliation, cost and usage
2. A conversational Knowledge Chat assistant supporting operators or administrators
3. A model registry with test statuses, BYOK tests and routing projection
4. AI operation logging and auditing with filters and CSV/JSON export
5. Unified telemetry with replay of historical AI requests
6. Virtual-key management covering creation, synchronization, masking, rotation and auditing
7. ABAC rule administration with YAML import/export, dry-run mode and gateway publishing
8. An ABAC decision dashboard, HITL queue, user logs and risk register
9. AI security controls through guardrails, control policies, human review and evidence logs
10. Operational schedules, Langflow agents and runnable agent lists
11. Spending and token logs for cost reporting and configuration
12. Docker log browsing with a time filter
13. Orchestration of complex multi-step agent processes with state retained between steps
14. Automatic or semi-automatic model evaluation and adaptive real-time A/B prompt tests

2026/Q3IN PROGRESS

2026/Q4