Kontrols ensures trust, reliability, and compliance Throughout all phases of AI operation, it delivers a multi-tiered defense and policy enforcement system for LLMs, agents, and classic ML models—covering input checks, runtime controls, and output oversight.
🔐 1. GateKeep: Input Validation & Policy Layer
Filter inputs — block unsafe, irrelevant, or non-compliant data.
Purpose: GateKeep acts as the front door to your AI system — ensuring that all incoming requests, prompts, or data streams are validated, sanitized, and compliant with policy and intent.
Key Capabilities:
- 🧹 Input Sanitization: Strip unsafe instructions, PII, or malicious content before processing.
- ⚖️ Policy Enforcement: Apply access control, rate limiting, and intent-based allow/deny rules.
- 🧠 Prompt Classification: Detect injection attempts, jailbreaks, or adversarial tokens.
- 🧩 Dynamic Whitelists/Blacklists: Enforce domain or topic restrictions dynamically.
- 📊 Input Risk Scoring: Score and log incoming inputs for audit and compliance visibility.
- 🔄 Integration Hooks: Acts as a pre-processor for APIs, agent queries, or chat prompts.
⚙️ 2. Enforcer: Runtime Governance & Behavior Control
Guide your AI’s actions — focus on behavior, not just words.
Purpose: Enforcer provides in-flight governance for model and agent actions — enforcing business, ethical, and safety guidelines during execution. It acts like a policy engine and governor that dynamically monitors, restricts, or adjusts model behavior.
Key Capabilities:
- 🧭 Runtime Policy Engine: Apply specific rules when running inference or using tools (e.g., restrict external requests or expenses).
- 🧩 Action Control Framework: Permit, restrict, or adjust agent actions instantly.
- 🕵️ Behavioral Auditing: Record and explain decision chains for transparency and reproducibility.
- 🧠 Adaptive Constraints: Adapt reasoning depth, context span, or temperature based on safety and efficiency needs.
- ⚡ Feedback Loop Integration: Initiate auto fallback, retry, or model swap in response to runtime events.
- 🔒 Compliance Layer: Ensure runtime behavior aligns with internal or regulatory policies.
🛡️ 3. Shield: Output Moderation & Safety Layer
Safeguard your users and brand — screen, censor, and verify all replies.
Purpose: Shield acts as the final checkpoint prior to output delivery — screening, evaluating, and editing replies for accuracy, compliance, safety, and tone.
Key Capabilities:
- 🧩 Output Moderation: Detect and block toxicity, bias, or sensitive content.
- 🔍 Factuality & Consistency Checks: Cross-check generated responses against trusted sources or ground truth.
- 🧠 LLM-based Review Layer: Use specialized reviewers for tone, privacy, or policy adherence.
- ⚖️ Redaction & Sanitization: Remove sensitive identifiers or confidential data from outputs.
- 📊 Explainable Moderation Reports: Provide transparency into why outputs were blocked or modified.
- 🔄 Feedback into Training: Return moderation results to refine models and boost policy alignment.
🔗 The Unified Kontrols Message
Kontrols provides governance for intelligent systems—keeping your AI safe, ethical, and policy-compliant from start to finish.
GateKeep filters inputs, Enforcer governs execution, and Shield protects outputs.