Estándares de ciberseguridad para IA

Cybersecurity standards for AI are formal guidelines that help organizations protect artificial intelligence systems from threats such as unauthorized access, data leaks, manipulation, and misuse.

These standards define processes, controls, and technical requirements specific to AI’s unique attack surface, including data pipelines, models, and outputs.

This topic matters because AI is now part of critical infrastructure, public services, and personal devices. Weak or absent security standards leave these systems vulnerable to exploitation.

For AI governance, compliance, and risk teams, applying formal standards like ISO/IEC 42001 or [NIST AI RMF](https://www.nist.gov/itl/ai-risk-management-framework) supports legal compliance, reduces incidents, and increases public trust.

Why AI-specific standards are necessary

Traditional cybersecurity frameworks were not designed for the dynamic, data-driven nature of AI. AI introduces new threats. Such as adversarial examples, model extraction, and training data manipulation. That require custom protections.

Also, AI systems may include external data sources or third-party models that increase supply chain risk. AI also creates potential for unexpected outputs, which can be exploited if not properly monitored. Standards built for AI take these risks into account and provide structured ways to manage them.

Key cybersecurity standards relevant to AI

Several national and international bodies have developed or are currently refining cybersecurity standards for AI. The most relevant include:

• ISO/IEC 42001: The first global management system standard for AI, including cybersecurity and risk controls specific to AI systems (link).

• NIST AI RMF: A risk management framework from the United States that includes categories for security, privacy, and resiliency in AI design (link).

• MITRE ATLAS: A threat modeling knowledge base for adversarial threats against machine learning systems (link).

• OECD AI Principles: Although broader in scope, these guidelines include commitments to safety and cybersecurity for trustworthy AI (link).

• ENISA Guidelines: The European Union Agency for Cybersecurity provides sector-specific advice for AI security (link).

These standards are not only technical. They also promote risk ownership, testing, documentation, and governance alignment.

Real-world examples of standard-based practice

A financial institution deploying AI for transaction fraud detection adopted NIST and ISO controls to reduce attack risks. They applied encryption across model inputs and outputs, performed red-teaming using adversarial test suites, and restricted model access through audited APIs.

A European healthcare platform working with AI diagnostics used ISO/IEC 27001 alongside ISO/IEC 42001 to implement secure logging, update control, and endpoint monitoring. These actions helped them pass certification for public sector data partnerships.

Best practices when applying cybersecurity standards to AI

Standards are only useful when applied consistently and tracked over time. Begin with an AI-specific threat model and align your controls to the system’s complexity and risk level.

Recommended practices include:

• Perform AI threat assessments: Use resources like MITRE ATLAS to map known vulnerabilities.

• Segment access by role: Restrict model, data, and API access based on user roles and audit activity.

• Test regularly for attacks: Simulate adversarial inputs and monitor model behavior for anomalies.

• Secure data pipelines: Ensure training and inference data are encrypted, validated, and logged.

• Track system updates: Maintain clear records of model version changes, patch history, and retraining events.

• Adopt AI-specific standards: Align cybersecurity governance with frameworks like ISO/IEC 42001 and NIST AI RMF.

FAQ

How is ISO/IEC 42001 different from ISO/IEC 27001?

ISO/IEC 27001 focuses on general information security management, while ISO/IEC 42001 provides an AI-specific framework, covering risks like adversarial attacks, model misuse, and data handling in machine learning systems.

Are AI cybersecurity standards mandatory?

Not yet in most countries, but adoption is growing. The [EU AI Act](https://artificialintelligenceact.eu/) and national digital safety laws increasingly require evidence of safe and secure AI practices aligned with such standards.

Do small organizations need to follow these standards?

Yes, though implementation can be scaled. Smaller organizations can follow core guidelines or adopt modular frameworks like NIST’s to improve readiness without excessive overhead.

Can standards prevent all AI attacks?

No system is perfectly secure. Standards reduce risk but do not eliminate it. They offer tested practices, documentation support, and audit mechanisms to help detect, respond to, and recover from incidents.

What standards address AI cybersecurity?

NIST Cybersecurity Framework applies broadly with AI-specific considerations. OWASP Machine Learning Security Top 10 addresses ML-specific vulnerabilities. ISO 27001 covers information security management applicable to AI data. MITRE ATLAS catalogues adversarial ML techniques. The EU AI Act includes cybersecurity requirements for high-risk systems.

How do you apply traditional security standards to AI?

Map traditional controls to AI contexts. Access control applies to models and training data. Encryption protects data at rest and in transit. Logging captures model predictions and access patterns. Incident response includes AI-specific scenarios. Vulnerability management extends to ML libraries and pre-trained models. Adapt controls for AI's unique characteristics.

Are there certification schemes for AI cybersecurity?

AI-specific cybersecurity certifications are emerging but not yet mature. ISO 27001 certification covers information security practices applicable to AI. SOC 2 Type II assessments can include AI security controls. Some industries have sector-specific security requirements (financial services, healthcare). Expect the certification landscape to develop as AI security matures.

Summary

Cybersecurity standards for AI provide a structured approach to protecting systems, users, and organizations from AI-specific threats. As AI adoption grows, aligning with standards like ISO/IEC 42001 and NIST AI RMF is no longer optional. Teams that build with security in mind will be better prepared for audits, attacks, and long-term success.