More Than an AI Answer Engine: Radar’s Regulatory AI Is Built for Compliance, Not Conversation
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AI Is Everywhere. But Not Every AI Is Built for Compliance.
The rise of generative AI has led many teams to ask a tempting question:
“Why can’t we just use ChatGPT for this?”
Maybe it’s for interpreting a new regulation. Or mapping a framework. Or assessing AI system risk.
The short answer?
Because regulatory work requires precision, not predictions.
ChatGPT and other large language models (LLMs) are incredibly capable at generating natural language. But when it comes to compliance, privacy, or regulatory mapping, they fall short in critical ways – ways that can create new risk vectors, not solve existing ones.
General-Purpose AI Isn’t Designed for Legal or Regulatory Precision
LLMs like ChatGPT:
- Generate plausible content, not verified outputs
- Can’t reliably interpret legal nuance or scope
- Lack of jurisdictional awareness and clause-level understanding
- Produce hallucinated citations or fabricated mappings
- Lack of traceability without specialized design
General-purpose LLMs are trained to generate fluent, plausible text, not to produce traceable, clause-based interpretations of regulatory requirements.
Without legal-specific tuning and structured output formatting, these tools can produce answers that “sound right” but offer no citations, rationale, or consistent logic.
That’s a problem in compliance, where accuracy, repeatability, and defensibility matter more than language fluency.
Radar’s approach is different. We use a compliance-tuned LLM with no internet access, strict prompt engineering, and built-in output constraints. This reduces hallucinations, enforces clause-level reasoning, and delivers transparent outputs your team can actually act on.
Radar’s Purpose-Built AI: Designed for Compliance, Traceability, and Trust
Radar’s regulatory AI wasn’t trained to write content. It was trained to read, understand, and operationalize law. Built in collaboration with legal and compliance experts, Radar’s AI uses curated, regulation-specific data (not open web content), ensuring outputs are accurate, explainable, and defensible.
But Radar’s value doesn’t stop at accurate legal interpretations. What makes the platform indispensable for compliance teams is how it turns those insights into action. Radar delivers structured workflows, audit-ready records, and a centralized system of record that connects every regulatory obligation to a real process, owner, and outcome, making compliance not just defensible, but repeatable.
It powers two critical products across the Radar platform:
Radar Controls™ : AI-Powered Coverage Mapping
Compliance teams use Radar Controls to:
- Determine which laws, rules, and regulations (LRRs) apply to their business
- Map internal control frameworks – standard or custom – back to those legal requirements
- Visualize coverage and gaps in real time
- Support audit readiness with 3-layer clause-level citations and plain-language rationale
Radar Controls’ built-in AI Strategist maps each regulatory requirement to your framework with transparent mapping outputs, including clause-level citations and transparent mapping rationale
Want to see it in action? Start with our Radar Controls overview here.
Radar AI Risk™ : Operationalizing Responsible AI Governance
AI Risk is emerging as one of the most pressing compliance frontiers. But most organizations have no structured way to:
- Assess the regulatory exposure of AI systems
- Map AI use cases to legal and ethical risk categories
- Track transparency, explainability, or consent at the system level
- Evaluate AI system governance readiness across functions
Radar AI Risk uses our regulatory AI to help organizations:
- Identify applicable AI regulations and risk standards
- Classify systems by type, sensitivity, and exposure
- Surface risks tied to AI deployment across privacy, fairness, and accountability
You can’t manage AI risk with an AI that wasn’t built for risk governance.
With built-in system inventories, traceable decision logs, and governance readiness scoring, Radar AI Risk doesn’t just highlight issues; it enables cross-functional accountability and long-term compliance maturity.
ChatGPT Doesn’t Know What a Clause Means or Why It Matters
One of the biggest misconceptions is assuming that “smart-sounding” = “compliant.”
But in compliance, plausibility is not proof.
If your team is using a generic LLM to assess control alignment or regulatory risk:
- Can it cite with specificity the regulatory text that it’s interpreting?
- Can it explain why the mapped control meets the obligation?
- Can you show that logic to an auditor, regulator, or board?
If the answer is no, you’re not mitigating risk. You’re introducing more of it.
What You Should Expect from AI That Touches Compliance
- Clause-level interpretation of legal obligations
- Traceable mappings to control activities
- Rationale for each alignment, in plain language
- Transparent calculations of coverage and gaps
- Jurisdictional awareness and scope sensitivity
- Consistent results each and every time
These are the non-negotiables if you want AI to augment compliance, not undermine it.
Radar’s AI delivers all of them. Because it’s not general-purpose. It’s compliance-grade by design.
Choose an AI That’s Built for the Job
ChatGPT is excellent at answering questions.
Radar’s AI is excellent at answering the right questions, with evidence.
- Are we compliant?
- Where are our gaps?
- What laws apply to us?
- How do our AI systems introduce regulatory exposure?
Whether you’re searching for AI governance tools, AI risk compliance software, or regulatory AI that goes beyond LLM hype, Radar has you covered.
Curious how it works? Let us show you the difference between AI that sounds smart and AI that helps you stay compliant—with traceable logic, structured workflows, control coverage metrics, and a clear system of record for regulatory alignment.