Cohere Compliance Witnessing

v0.7.8 PREVIEW – Cohere adapter ships with v0.7.8. Streaming, system prompt drift detection, and RAG witnessing. Available on PyPI and npm at release.

1. The Certification Boundary

Compliance certifications have scope boundaries. Cohere's certifications cover what Cohere operates. Your certifications cover what you operate. The AI inference happens at the boundary between the two.

Layer	Responsible Party	Certifications	What It Covers
Model Platform	Cohere	FedRAMP High, SOC 2 II, ISO 27001, HIPAA BAA	Infrastructure, data handling, model training, API availability
Inference Decision	Gap	Neither party covers this	What the model decided, on what input, producing what output
Application	You	Your SOC 2, HIPAA, FedRAMP	Business logic, access controls, data governance, logging

The inference decision sits in the gap. Cohere's platform processed it securely. Your application consumed the result. But neither party's certification explicitly attests to the decision itself: what went in, what came out, which model version generated it, and whether the response was complete or refused.

SWT3 fills this gap. Every inference call produces a witness anchor that captures the decision fingerprint. The anchor lives in your compliance boundary, governed by your policies, but it references the exact model and response from Cohere's platform. It is the bridge artifact that connects both certification scopes.

2. Cohere's Enterprise Compliance Position

3. The Evidence Gap in Your Application

Consider what happens during a typical Cohere inference call in your application:

• Your application sends a prompt to client.chat()
• Cohere's platform processes it (covered by their SOC 2)
• The response arrives in your application (covered by your SOC 2)
• Your application uses the response to make a business decision

Now an auditor asks: "On September 12th, what model version generated the response that informed this loan decision?"

Cohere's API logs (if you have access to them) show that a call was made. Your application logs (if you built logging) show what your app did with the result. But the decision itself -- the mapping from this specific prompt to this specific response by this specific model version -- is not captured by either party's standard logging.

SWT3 captures exactly this. A cryptographic fingerprint of the prompt, the response, the model version, and the timestamp. Independently verifiable. Stored in your infrastructure. Survives regardless of what happens to either Cohere's or your application's logs.

4. Witnessing Cohere Inference

Python

import cohere
from swt3_ai import Witness
from swt3_ai.adapters.cohere import wrap_cohere

witness = Witness(
    endpoint="https://your-instance.example.com/api/v1/witness",
    api_key="axm_your_key",
    tenant_id="YOUR_TENANT",
)

client = cohere.ClientV2(api_key="your-cohere-key")
witnessed = wrap_cohere(client, witness)

# Identical usage -- your code does not change
response = witnessed.chat(
    model="command-r-plus",
    messages=[
        {"role": "system", "content": "You are a medical coding assistant."},
        {"role": "user", "content": "Assign ICD-10 codes to this clinical note."},
    ],
)

# response is untouched
print(response.message.content[0].text)

witness.flush()

TypeScript

import { CohereClientV2 } from 'cohere-ai';
import { Witness, wrapCohere } from '@tenova/swt3-ai';

const witness = new Witness({
  endpoint: 'https://your-instance.example.com/api/v1/witness',
  apiKey: 'axm_your_key',
  tenantId: 'YOUR_TENANT',
});

const client = new CohereClientV2({ token: 'your-cohere-key' });
const witnessed = wrapCohere(client, witness);

const response = await witnessed.chat({
  model: 'command-r-plus',
  messages: [
    { role: 'system', content: 'You are a medical coding assistant.' },
    { role: 'user', content: 'Assign ICD-10 codes to this clinical note.' },
  ],
});

await witness.flush();

5. Streaming Witnessing

Cohere's chat_stream() returns a sequence of typed events: message-start, content-delta, message-end. The SWT3 adapter accumulates text from content deltas and extracts token usage from the message-end event, then mints a single anchor when the stream completes.

# Python streaming
stream = witnessed.chat_stream(
    model="command-r-plus",
    messages=[{"role": "user", "content": "Summarize this document"}],
)

for event in stream:
    if event.type == "content-delta":
        text = event.delta.message.content.text
        print(text, end="", flush=True)

# Anchor minted automatically on stream completion

Your streaming experience is identical. The adapter sits between the stream and your code, observing events without modifying them. The witness anchor captures the complete response hash, actual model ID, and total token counts.

6. RAG Pipeline Witnessing

Cohere is widely deployed in RAG architectures. Command R was designed for grounded generation with citations. When you combine Cohere inference with a retrieval layer, you can witness both independently:

from swt3_ai import Witness
from swt3_ai.adapters.cohere import wrap_cohere

witness = Witness(
    endpoint="https://your-instance.example.com/api/v1/witness",
    api_key="axm_your_key",
    tenant_id="YOUR_TENANT",
)

# Witness the retrieval context (AI-RAG.1)
witness.witness_rag_context(
    chunks=[
        {"source": "policy_v3.pdf", "text": "Section 4.2: Coverage limits..."},
        {"source": "amendment_2026.pdf", "text": "Effective date: March 1..."},
    ],
    query="What are the coverage limits?",
    retriever="qdrant-v1.12",
)

# Witness the Cohere inference (AI-INF.1)
client = cohere.ClientV2(api_key="your-key")
witnessed = wrap_cohere(client, witness)

response = witnessed.chat(
    model="command-r-plus",
    messages=[
        {"role": "system", "content": "Answer using only the provided context."},
        {"role": "user", "content": context + "\n\nQuestion: " + query},
    ],
)

# Two anchors: retrieval provenance + generation evidence
witness.flush()

7. Regulatory Evidence Mapping

Framework	Requirement	Cohere's Certificate	SWT3 Anchor
SOC 2 CC7.2	Monitor system components	Platform monitoring	AI decision monitoring in your app
SOC 2 CC6.1	Logical access controls	API key management	Per-inference identity binding
FedRAMP AU-2	Auditable events	API access logs	Decision-level audit events
HIPAA 164.312(b)	Audit controls	BAA covers data handling	PHI inference decision trail
SR 11-7 / SR 26-2	Model performance monitoring	Not applicable (not a bank)	Latency, refusal, drift tracking
EU AI Act Art. 12	Automatic operation logging	Provider logging (Art. 53)	Deployer logging per inference
NIST AI RMF MN 2.2	Track deployed AI behavior	Platform metrics	Per-decision behavior fingerprint

8. Quick Start

Install

# Python
pip install swt3-ai cohere

# TypeScript
npm install @tenova/swt3-ai cohere-ai

Three Lines to Close the Gap

import cohere
from swt3_ai import Witness
from swt3_ai.adapters.cohere import wrap_cohere

witness = Witness(
    endpoint="https://your-instance.example.com/api/v1/witness",
    api_key="axm_your_key",
    tenant_id="YOUR_TENANT",
)
client = cohere.ClientV2(api_key="your-key")
witnessed = wrap_cohere(client, witness)  # Bridge the gap

# Every inference now produces a cryptographic witness anchor
response = witnessed.chat(
    model="command-a",
    messages=[{"role": "user", "content": "Classify this transaction"}],
)

witness.flush()

9. References

• Cohere API Documentation
• Cohere Security and Compliance -- SOC 2 II, ISO 27001, HIPAA
• Cohere FedRAMP High Authorization (May 2026)
• PwC-Cohere Global Alliance
• SR 11-7 / SR 26-2 Model Risk Overlay
• EU AI Act Compliance Crosswalk
• NIST AI RMF Crosswalk
• SWT3 SDK Documentation
• swt3-ai on PyPI
• @tenova/swt3-ai on npm