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What is AI security?
AI security is the discipline of protecting AI models, the underlying data, and the surrounding applications from threats. It extends traditional IT security to include AI-specific aspects.
Four threat areas: model security (extraction, theft, sabotage), data security (poisoning, leakage, unauthorized access), prompt and interaction security (injection, jailbreak, manipulation), and operational security (availability, isolation, access control).
Key types of attacks: prompt injection (manipulative inputs), data poisoning (corrupted training data), model extraction (replicating a model), membership inference (determining whether a data point was in the training set), adversarial examples (targeted misclassification).
For small and medium-sized businesses, AI security is a new field that builds upon and complements traditional IT security. Investing in security today will result in significant savings tomorrow in terms of incidents and fines.
Best Practices for AI Security
These six principles have proven effective:
- Security by Design: Build security in from the start—don’t treat it as an afterthought.
- Zero Trust: No blind trust—authenticate and authorize every request.
- Defense in Depth: Combine multiple layers of protection; don’t rely on just one.
- Least Privilege: Models and users are granted only what they need.
- Regular Testing: Red teaming and penetration tests reveal vulnerabilities.
- Prepared for Incidents: Incident response is mandatory—even for AI-specific attacks.
Common Mistakes in AI Security
We often see these pitfalls:
- Relyingsolely on traditional IT security: Firewalls and passwords aren’t enough—AI needs specific guardrails.
- Trusting user input: Without input filters, any input can be an attack.
- Lack of monitoring: Attacks go undetected—response only comes after damage has been done.
- No incident plan: When things go wrong, panic sets in instead of a structured response.
- Security as a roadblock: Rules that are too strict prevent usage—there’s a lack of balance with the business perspective.
AI Security vs. IT Security vs. Data Protection
A comparison of three related disciplines:
- AI security: Specific to AI models, data, and interactions—new threats.
- IT Security: Traditional security for systems, networks, and applications. The foundation.
- Data Protection: Protection of personal data — GDPR, specific requirements.
Building AI Security in Your Company
In a free initial consultation, we’ll review your AI applications and outline a security strategy—including a threat analysis, recommended measures, and a roadmap.
As an AI partner for small and medium-sized businesses, we take a pragmatic approach to building AI security—with guardrails, monitoring, incident response, and regular red teaming.
What We Offer
- AI Consulting — Security Strategy and Implementation.
- Guardrails in the Glossary — the technical layer of protection.
- GDPR and AI in the glossary —data protection as the foundation.
- AI Compliance in the Glossary — the regulatory framework.
