ISO 27001 + 42001 integrated security architecture
Data Security Starts with People
Information Security
Sensitive data in AI training and inference needs protection against leaks and AI-specific attacks such as data poisoning.
We integrate ISO/IEC 27001 (Information Security) and ISO/IEC 42001 (AI Management) so your data stays confidential and intact, in line with international regulation.
ISO 27001
What You Get01 02 03 04 05
ISO 27001
AI penetration testing and threat analysis
Secure model operations (Trust by Design)
Incident response and monitoring mechanisms
Continuous security improvement cycle
Depth
Security Topic Areas
Secure Model Operations
Production AI models face prompt injection, evasion, and manipulation attacks. We protect algorithms throughout their lifecycle so outputs stay fair, transparent, and resistant to external interference.
Risk Architecture and Threat Analysis
Organisation-specific AI penetration tests and impact assessments find security gaps before attackers do. Using OWASP guidance and ISO/IEC 42001, we build a risk architecture and controls that limit operational, legal, and reputational damage.
Monitoring and Incident Management
Anomalies, performance drift, and security deviations in AI models need fast detection. We set up monitoring, incident response plans, and recovery procedures, including legal notification where required.
Continuous Security Improvement
Threats and regulations change continuously. Through internal audits, root cause analysis, and PDCA cycles we keep your security posture current against evolving risks, including the EU AI Act.
FAQ
Questions About Information Security
How does ISO 27001 address AI-specific security risks?
ISO 27001 secures information assets through access control, encryption, incident management, and supplier security. AI adds threats such as model evasion, prompt injection, training data poisoning, and inference-time data leaks. AION integrates ISO 27001 controls with ISO 42001 AI governance to cover both traditional and AI-native attack surfaces.
What is AI data poisoning and how do you protect against it?
Data poisoning happens when adversaries inject malicious samples into training or fine-tuning datasets, causing models to behave incorrectly or leak information. Protection requires data provenance tracking, integrity validation, access controls on training pipelines, anomaly detection, and regular model performance monitoring.
Can ISO 27001 and ISO 42001 be implemented together?
Yes, and they should be. Both standards share risk assessment, control selection, monitoring, and continuous improvement structures. A unified implementation avoids duplicate documentation, reduces audit overhead, and keeps information security and AI governance aligned.
What does AI penetration testing cover?
AI penetration testing evaluates model inputs, API endpoints, training pipelines, and deployment environments against OWASP LLM Top 10 and organisation-specific threat models. Tests include prompt injection, jailbreak attempts, data exfiltration via model outputs, adversarial input crafting, and supply-chain vulnerability assessment.
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