What is the difference between AI security and cybersecurity?
Short answer
Cybersecurity protects digital infrastructure, networks, endpoints, and data from unauthorized access and disruption. AI security is a specialized discipline within cybersecurity focused specifically on safeguarding artificial intelligence systems, models, and training pipelines from unique vulnerabilities. While traditional cybersecurity mitigates risks like malware and network intrusions, AI security defends against machine-learning-specific threats such as prompt injection, training data poisoning, and model evasion.
The details
AI security builds directly upon foundational cybersecurity. Traditional cybersecurity controls like identity management, encryption, access controls, and patch management remain essential because artificial intelligence workloads operate on standard cloud servers, networks, and APIs. However, traditional security measures are often blind to machine learning risks. The NCSC Guidelines for Secure AI System Development emphasizes that AI introduces novel security vulnerabilities across design, development, deployment, and operation that must be evaluated alongside conventional software threats.
The core distinction lies in how systems fail and how attackers exploit them. Traditional cybersecurity addresses code vulnerabilities like buffer overflows or SQL injection. In contrast, artificial intelligence systems rely on statistical weights and natural language prompts. Threat matrices like MITRE ATLAS and the OWASP Top 10 for Large Language Model Applications document risks specific to models, including prompt injection, training data poisoning, and model inversion. Standard network firewalls cannot reliably identify malicious prompts embedded within natural text.
What to do
- Establish foundational cybersecurity controls including multi-factor authentication, patch management, and endpoint detection.
- Inventory all artificial intelligence applications, vendor tools, and models operating within your technical environment.
- Threat model AI assets using specialized frameworks like MITRE ATLAS alongside traditional software assessments.
- Sanitize and validate all model inputs and outputs to prevent prompt injection and data leakage.
- Enforce strict least privilege on model execution environments and external tool integrations.
Useful next reads and tools
Terms explained
- Data poisoning
- Data poisoning is the manipulation of data used to train, fine-tune or ground an AI model so that it behaves in a way the attacker wants.
- Encryption
- Encryption transforms readable data into ciphertext that can only be turned back into readable form with the correct key.
- HTTPS and TLS
- HTTPS is HTTP protected by TLS encryption, which keeps data between a browser and a website private and tamper-resistant and confirms the site controls its domain.
- Malware
- Malware is any software designed to harm, spy on or gain unauthorized control of a device, network or account.
- MITRE ATLAS
- MITRE ATLAS is a public knowledge base of adversary tactics and techniques against AI-enabled systems, modeled on MITRE ATT&CK.
- OWASP Top 10
- The OWASP Top 10 is an awareness list of the most critical security risks to web applications, published by the Open Worldwide Application Security Project.