The enterprise AI governance market is evolving rapidly. Most platforms today focus on observability, risk cataloging, or perimeter security. CTGT takes a fundamentally different approach: active, real-time policy enforcement at the model's output layer, purpose-built for regulated industries.
Understanding where each category excels, and where it falls short, is critical when evaluating governance for high-stakes, regulated use cases.
| Capability | CTGT | AI Security / Observability | AI GRC Platforms |
|---|---|---|---|
| Core Function | Active policy enforcement and content remediation on LLM outputs in real time | Network-level monitoring, prompt filtering, shadow AI detection, DLP | Model inventory, risk assessment workflows, compliance documentation |
| Policy Enforcement Model | Active Prevents non-compliant content before it reaches the user; remediates in real time | Reactive Detects and blocks based on broad rules and keyword patterns; alerts after the fact | Passive Catalogs risk and generates reports; does not operate on live model outputs |
| Regulatory Depth (FINRA, SEC, SOPs) |
Deep Ingests full regulatory frameworks and internal SOPs as structured policy graphs. Adjudicates across thousands of granular rules simultaneously | Limited Policies are typically broad data-loss or acceptable-use rules. Not designed for sub-rule level regulatory adjudication | Moderate Maps AI systems to regulatory frameworks at a documentation level. Does not enforce rules on live outputs |
| Speed to Deploy New Policies |
Minutes Upload a document, point to a SharePoint. Policy graph auto-generates from unstructured sources | Varies. Policy creation requires manual configuration of detection rules, categories, and thresholds | Governance workflows require manual setup, stakeholder alignment, and custom configuration |
| Model Agnostic | Yes Works as an API layer over any model (OpenAI, Anthropic, Google, open-source). No access to model internals required | Yes Network-level approach is inherently model-agnostic | Yes Operates at the inventory and workflow level, independent of model provider |
| Deterministic Audit Trail | Full Every decision logged with: policies triggered, criticality scores, intent vectors, and contribution to final result. Designed for regulatory examination | Partial Logs prompts, responses, and policy violations. Audit context is broad, not policy-specific at the sub-rule level | Partial Documents risk assessments and compliance status. Not linked to runtime AI decisions |
| Hallucination Reduction | Core Proprietary ensemble methods reduce model fallibility from ~50% to ~4% on average. Policy engine validates factual grounding at inference time | Not core Security focus. Hallucination mitigation is not a primary capability | Not core Governance focus. May flag hallucination risk in assessments but does not mitigate at runtime |
| Legacy System Replacement Potential |
High Designed to replace brittle regex-based and classic ML compliance stacks for e-comms and content review | Low Complements existing security stack rather than replacing compliance infrastructure | Low Adds a governance layer on top. Does not replace operational compliance tooling |
| Deployment Options | Multi-tenant SaaS, single-tenant VPC, fully on-premise. TLS 1.3 + AES-256 encryption. SOC-2 aligned | Primarily SaaS with single-tenant options. Network proxy or agent-based deployment | Primarily SaaS. Some offer on-premise or hybrid options |
CTGT's policy engine has been benchmarked against baseline models, standard enterprise RAG pipelines, and Anthropic's Constitutional AI system prompt. Results are consistent across open-source and frontier models.