High-risk AI needs
high-trust evidence

Agents that summarize adverse media and transaction history. When a suspicious pattern is missed, you need proof of exactly what data was retrieved and fed to the context window.

Compliance officers use chatbots to query complex regulations. If the bot hallucinates an exemption that doesn't exist, and the officer acts on it, you need an immutable record of that hallucination.

LLMs generating drafts of Suspicious Activity Reports (SARs). You need to trace the lineage of every claim in that report back to the source document.

AI assistants helping prepare regulatory submissions. Every fact in the filing must be traceable to its source document.

Agents listening to consults and updating EMRs. You need an immutable diff between the transcript and the AI summary to catch hallucinations during audit.

Patient-facing bots that collect symptoms before a nurse sees them. If the bot fails to escalate a keyword (e.g., "Chest pain"), you need proof of the exact logic path the agent took.

Agents mapping clinical notes to billing codes. Incorrect coding leads to insurance fraud investigations. You need a chain of custody for every code generated.

AI drafting preliminary radiology reports. Every finding must be traceable to the specific image region that triggered it.

Agents parsing invoices and police reports. Every parse_document tool call needs to be audited for accuracy and completeness.

Adjusters asking, "Is flood damage covered in this specific rider?" If the RAG system retrieves the wrong rider, the claim decision is wrong.

Chatbots collecting initial accident details. You need a forensic transcript of what the customer actually typed versus how the Agent interpreted it.

AI researching liability and recovery potential. Every source document and conclusion must be traceable.

Recruiters often tweak system prompts to get "better" results (e.g., adding "look for high energy"). These tweaks can introduce illegal bias. You need an immutable history of Active Prompt Versions to prove the prompt used was the legally approved version.

To pass a Bias Audit, you must run statistical tests on the outputs. You need a guaranteed, tamper-proof dataset of every score generated to prove the Selection Rate didn't violate the 4/5ths rule.

Agents often hallucinate personality traits based on proxies (e.g., assuming a candidate is "rigid" because they worked at a legacy bank). You need the raw Chain of Thought log to see why the Agent assigned a low score.

If a recruiter changes a prompt to "filter for young people," runs a batch, and changes it back, you need proof of that deviation. Cynsta's immutable ledger captures it.

Agents iterating through data rooms to flag risks. Every file opened and every flag generated must be logged.

Agents searching case law. Hallucinated citations are a disbarment risk. You need proof of the source text the Agent cited.

Agents tagging documents as "Privileged" or "Responsive." If you withhold a document incorrectly, you face court sanctions.

AI reviewing thousands of documents for M&A. If the AI misses a material clause, malpractice liability follows.

Agents answering questions about taxes, benefits, and voting. Every response is a matter of public record.

Agents reading emails and routing them to the correct department. If an urgent case is dropped, you need to trace why the Agent misclassified it.

Agents helping compile documents for Freedom of Information requests. The search parameters used by the Agent are subject to scrutiny.

AI helping case workers determine eligibility. Every recommendation and its basis must be documented for judicial review.