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Model: rae-jax/cie-auditor-final Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: mistralai/Mistral-7B-Instruct-v0.2
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tags:
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- text-generation
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- fine-tuned
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- compliance
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- cybersecurity
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- grc
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- auditing
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- unsloth
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- mistral
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- dpo
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language:
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- en
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pipeline_tag: text-generation
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---
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# 🛡️ CIE Auditor — Enterprise GRC Compliance AI
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A custom fine-tuned and DPO-aligned version of **Mistral 7B Instruct**, trained to function as a **Senior IT Compliance Auditor**. Feed it a security incident or corporate scenario, and it produces a structured, board-ready **9-Part Audit Report** in seconds.
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> Tested against real-world breaches including Capital One (2019), Equifax (2017), and Uber (2022) — correctly identifying root causes, violated controls, and remediation steps.
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---
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## 🚀 What Makes This Different
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This is **not** a ChatGPT wrapper or a prompt-engineered chatbot. The model weights themselves were modified using two phases of training:
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1. **Supervised Fine-Tuning (SFT):** Trained on a custom JSONL dataset of real compliance frameworks including ISO 27001, SOC 2 Type II, GDPR, PCI-DSS, HIPAA, and NIST CSF. The model learned to map any security scenario to the correct regulatory controls.
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2. **Direct Preference Optimization (DPO):** Constitutional AI alignment was applied to enforce auditor-grade behavior. The model was trained to:
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- ✅ Always flag `INSUFFICIENT EVIDENCE` when data is missing instead of speculating
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- ✅ Escalate critical findings to board level
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- ✅ Refuse to act like a chatbot or provide conversational responses
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- ✅ Maintain a strict, formal auditor persona at all times
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---
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## 📋 Output Format
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Every response follows a strict 9-part structure:
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```
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1. Executive Summary
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2. Audit Scope
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3. Controls Violated
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4. Finding
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5. Impact
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6. Remediation Steps
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7. Risk Assessment
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8. Audit Recommendations
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9. Management Review
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INSUFFICIENT EVIDENCE to determine: [flagged unknowns]
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Next Steps: [actionable items]
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```
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---
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## 💻 Quick Start
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```python
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "rae-jax/cie-auditor-final",
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max_seq_length = 2048,
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dtype = None,
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load_in_4bit = True,
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)
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FastLanguageModel.for_inference(model)
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messages = [
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{
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"role": "system",
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"content": "You are a Senior Compliance Auditor. Assess the scenario and output a highly structured 9-part compliance audit report. If evidence is insufficient, state INSUFFICIENT EVIDENCE."
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},
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{
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"role": "user",
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"content": "An attacker exploited a misconfigured ModSecurity WAF via SSRF to extract temporary IAM credentials from the AWS EC2 metadata service, gaining access to an S3 bucket containing the PII of 100 million customers."
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}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt"
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).to("cuda")
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outputs = model.generate(
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input_ids=inputs,
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max_new_tokens=1024,
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temperature=0.1,
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top_p=0.9
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)
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print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
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```
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---
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## 🧪 Example Scenarios to Try
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**Cloud Misconfiguration:**
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> *"Our AWS S3 bucket was left publicly accessible for 12 months, exposing 400GB of customer PII and proprietary product designs."*
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**Social Engineering + PAM Compromise:**
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> *"An attacker bypassed MFA using push notification fatigue on a contractor. They then found hardcoded PAM credentials in a PowerShell script on an internal network share, gaining full control of AWS, GCP, and Slack."*
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**Unpatched Vulnerability:**
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> *"Attackers exploited CVE-2017-5638 in Apache Struts. The patch had been available for 2 months but was not applied. The attackers maintained access for 76 days and exfiltrated SSNs for 147 million people."*
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---
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## 🏗️ Training Details
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| Parameter | Value |
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|---|---|
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| Base Model | `mistralai/Mistral-7B-Instruct-v0.2` |
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| Training Framework | Unsloth + TRL |
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| SFT Dataset | Custom JSONL (ISO 27001, SOC 2, GDPR, PCI-DSS, HIPAA, NIST CSF) |
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| DPO Alignment | Constitutional AI pairs (chosen vs rejected) |
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| Quantization | 4-bit (LoRA adapters merged into full weights) |
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| Hardware | Kaggle T4 GPU (Free Tier) |
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| Context Length | 2048 tokens |
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---
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## ⚠️ Intended Use
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This model is designed for:
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- 🎓 Educational and portfolio demonstration purposes
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- 🔬 Research into Constitutional AI and compliance automation
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- 🏢 Prototyping GRC tooling and compliance assistants
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This model is **not** a substitute for certified compliance professionals (CISA, CISSP, CPA). Always have findings reviewed by qualified auditors before acting on them.
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---
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## 📄 License
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Apache 2.0 — Free to use, modify, and distribute with attribution.
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---
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*Built with ❤️ using [Unsloth](https://github.com/unslothai/unsloth)*
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