If you are building Artificial Intelligence, your threat landscape looks vastly different from a traditional SaaS platform. You aren’t just worried about SQL injection or DDoS attacks. You are worried about model inversion, data poisoning, and prompt injection.
This is where ISO 27001 Annex A 5.7: Threat Intelligence becomes a critical survival tool rather than just a compliance checkbox. For an AI company, threat intelligence isn’t about reading generic security news; it is about knowing exactly how attackers are trying to break, steal, or manipulate your models right now.
Here is how to implement Annex A 5.7 in an AI context, turning abstract requirements into a concrete defense strategy for your algorithms.
Table of contents
What is Annex A 5.7 Actually Asking For?
The standard requires you to collect and analyze information about threats to produce threat intelligence. The goal is to provide awareness so you can take appropriate mitigation actions.
In plain English: Don’t wait to get hit. Find out what hits are coming and duck.
For an AI company, this means moving beyond standard cybersecurity. If you are only monitoring for Windows Server vulnerabilities but your entire stack is Python-based inference engines running on GPUs, your intelligence is blind.
The Three Layers of AI Threat Intelligence
To implement this effectively, you need to categorize your intelligence. This helps you filter the noise (which is vital when you are a lean startup).
1. Strategic Intelligence (The “Who” and “Why”)
This is the high-level view. Who is attacking AI companies and why?
- Competitors trying to reverse-engineer your weights.
- Nation-states trying to poison training data to influence outputs.
- “Jailbreakers” trying to bypass safety guardrails for sport or profit.
Knowing who is targeting you helps you decide where to spend your budget. If your threat is IP theft, you lock down the weights. If the threat is reputation damage, you harden the safety filters.
2. Tactical Intelligence (The “How”)
This covers the methodologies, or TTPs (Tactics, Techniques, and Procedures). For AI, this changes weekly.
- Adversarial Attacks: New papers on how to trick vision models with invisible noise.
- Prompt Injection: New “jailbreak” prompts that bypass standard RLHF (Reinforcement Learning from Human Feedback) controls.
- Supply Chain Poisoning: Malicious models uploaded to public repositories like Hugging Face.
3. Operational Intelligence (The “What”)
These are the specific indicators.
- IP addresses known for scraping data.
- Specific malicious hashes of PyTorch pickle files.
- Signatures of known “poisoned” datasets.
How to Implement This for AI (Step-by-Step)
Step 1: Curate Your AI-Specific Sources
Standard threat feeds won’t cut it. You need to look where the AI researchers hang out. Your “Annex A 5.7 Source List” should include:
- OWASP Top 10 for LLMs: The bible of Large Language Model security.
- The AI Incident Database: A collection of real-world AI failures and attacks.
- arXiv.org (Security/AI Section): Yes, academic papers are threat intel in this field. If a paper publishes a new attack vector, you can bet someone is building a tool for it within 48 hours.
- Hugging Face Security Discussions: Monitor the community for reports of malware in model weights.
Step 2: The Analysis Phase (The “So What?”)
Collecting data is easy. Analyzing it is where you pass or fail the audit. You need a process that asks: “Does this apply to our model architecture?”
Example:
Intel: A new paper releases a “Universal Adversarial Trigger” for GPT-based models.
Analysis: We use Llama-3, but the architecture is similar. We are vulnerable.
Action: Test this trigger against our evaluation dataset immediately.
Step 3: Documenting the Process
You need to prove to the auditor that this isn’t just happening in your head. You need a Threat Intelligence Procedure. This document should outline:
- Who is responsible for gathering intel (e.g., the Lead ML Engineer).
- Which sources you monitor.
- How often you review them (e.g., Weekly Security Sync).
- How you track remediation (e.g., Jira tickets linked to intelligence reports).
If you need a framework to get this documented quickly, Hightable.io provides ISO 27001 toolkits with pre-built Threat Intelligence registers and procedures. These templates are designed to be flexible enough for high-tech companies, ensuring you capture the right level of detail without creating busywork.
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Integrating Intel into MLOps
The ultimate goal of Annex A 5.7 is to change how you build. In an AI company, threat intel should feed directly into your MLOps pipeline.
- Model Evaluation: Add new “attack prompts” discovered in your intel gathering to your automated red-teaming suite.
- Data Sanitization: Update your data ingestion filters based on new reports of poisoning techniques.
- Dependency Scanning: Block specific versions of ML libraries that have been flagged as vulnerable.
Common Pitfalls for AI Companies
1. Ignoring the “Model” Threat
Focusing only on cloud security (AWS/Azure) and ignoring the AI-specific threats. An auditor will ask: “How do you know about threats to your algorithm?” If you only show them firewall logs, you will struggle.
2. Information Overload
Trying to read every paper on arXiv. You must filter for relevance. If you build Computer Vision, don’t waste time analysing LLM prompt injections.
Conclusion
For an AI company, ISO 27001 Annex A 5.7 is your radar. It allows you to navigate the incredibly fast-moving waters of AI security without crashing. By curating the right sources, analyzing them for relevance to your specific models, and feeding that data back into your development loop, you turn compliance into a competitive advantage.
Don’t let the documentation slow you down. Use the resources at Hightable.io to get your policies and registers in place, so you can focus on building safe, robust AI.
About the author
Stuart Barker is a veteran practitioner with over 30 years of experience in systems security and risk management.
Holding an MSc in Software and Systems Security, Stuart combines academic rigor with extensive operational experience. His background includes over a decade leading Data Governance for General Electric (GE) across Europe, as well as founding and exiting a successful cyber security consultancy.
As a qualified ISO 27001 Lead Auditor and Lead Implementer, Stuart possesses distinct insight into the specific evidence standards required by certification bodies. He has successfully guided hundreds of organizations – from high-growth technology startups to enterprise financial institutions – through the audit lifecycle.
His toolkits represents the distillation of that field experience into a standardised framework. They move beyond theoretical compliance, providing a pragmatic, auditor-verified methodology designed to satisfy ISO/IEC 27001:2022 while minimising operational friction.
