AI Security vs. AI Safety: Why the Distinction Matters

In conversations about AI risk, the terms "AI Security" and "AI Safety" are often used interchangeably. This conflation isn't just imprecise—it obscures fundamentally different threat models, required expertise, and organizational responsibilities.

Defining the Terms

AI Safety concerns itself with ensuring AI systems behave as intended and don't cause unintended harm. This includes:

• Alignment with human values and intentions
• Preventing harmful, biased, or unethical outputs
• Managing long-term risks from advanced AI systems
• Ensuring AI systems remain under human control

Major organizations like Anthropic, OpenAI, and DeepMind have dedicated significant resources to AI Safety research, and regulatory frameworks like the EU AI Act address safety concerns around high-risk AI applications.

AI Security, by contrast, focuses on protecting AI systems from adversarial attacks and ensuring their integrity in deployment:

• Defending against adversarial attacks (evasion, poisoning, extraction)
• Protecting model intellectual property and training data
• Securing the AI/ML development and deployment pipeline
• Runtime monitoring, access control, and threat detection
• Supply chain integrity for models and dependencies

Why This Matters

The distinction matters because the threat models are different, the expertise required is different, and the solutions are different.

AI Safety asks: "Will this model produce harmful outputs or behave unexpectedly?"
AI Security asks: "Can an adversary compromise this system, steal this model, or manipulate its behavior?"

A model can be perfectly "safe" in terms of alignment and still be vulnerable to prompt injection attacks, model extraction, or supply chain compromise. Conversely, a model can be hardened against adversarial attacks while still producing biased or harmful outputs.

Different Expertise Required

AI Safety research typically draws on expertise in:

• Machine learning and model interpretability
• Philosophy and ethics
• Human-computer interaction
• Policy and governance

AI Security, meanwhile, requires deep expertise in:

• Adversarial machine learning and attack research
• Traditional cybersecurity and threat modeling
• Secure software development and DevSecOps
• Infrastructure and cloud security
• Cryptography and privacy-preserving computation

The overlap exists—both fields care about robustness, for instance—but the core competencies and threat models diverge significantly.

Why AISF Focuses on Security

Excellent organizations are working on AI Safety. Anthropic's Constitutional AI, OpenAI's alignment research, and academic institutions worldwide are making progress on ensuring AI systems behave safely.

The gap is in AI Security at the implementation layer. Organizations deploying AI today have no industry-recognized framework to implement, no certification to pursue, no common vocabulary to communicate their security posture to customers, partners, or regulators.

AISF exists to fill that gap—not because AI Safety doesn't matter (it does), but because AI Security is the complementary discipline that's been underserved, and because organizations need actionable standards they can implement today.

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