What is the difference between AI security and AI safety?
Short answer
AI security focuses on protecting artificial intelligence systems from intentional attacks, unauthorized access, and adversarial manipulation. AI safety focuses on preventing unintended harm, behavioral failures, and alignment issues when the model operates normally. While security defends the system from malicious threat actors trying to compromise data or model behavior, safety ensures the system operates reliably, fairly, and responsibly without causing accidental harm.
The details
AI security protects the machine learning lifecycle against deliberate attacks cataloged by frameworks such as MITRE ATLAS and risks documented in the OWASP Top 10 for Large Language Model Applications. Security vulnerabilities include prompt injection, training data poisoning, model extraction, and insecure API integrations. Security teams enforce technical controls across development, deployment, and operational phases to preserve the confidentiality, integrity, and availability of models, data pipelines, and infrastructure against active adversaries.
AI safety focuses on ensuring the model operates responsibly, reliably, and without unintended harm. Safety hazards include hallucinations, unfair bias, toxic generations, runaway automation, and negative societal impacts during normal usage. Guidance from the NIST AI Risk Management Framework bridges these disciplines. An adversarial security exploit can bypass safety guardrails, while an unaligned or unreliable model introduces severe operational and legal risks even without an attacker present.
Comparing the core dimensions clarifies the boundary. For primary goals, security defends against deliberate compromise, whereas safety prevents unintentional operational harm. For threats, security counters malicious actions such as model tampering and unauthorized data extraction, whereas safety counters systemic bias and inaccurate output. For controls, security teams deploy network firewalls, strict access controls, and vulnerability management, whereas safety teams deploy alignment tuning, content guardrails, and human review.
What to do
- Model adversarial threats by auditing pipelines against prompt injection and training data poisoning vulnerabilities.
- Implement input sanitization and prompt-whitelisting to block malicious user prompts before they reach the model.
- Apply content moderation filters and output guardrails to prevent biased, toxic, or hallucinated responses.
- Enforce least privilege access on model APIs and databases using strict authentication and token management.
- Conduct AI red teaming to test both adversarial resistance and safety alignment under stress.
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.
- 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.
- MITRE ATLAS
- MITRE ATLAS is a public knowledge base of adversary tactics and techniques against AI-enabled systems, modeled on MITRE ATT&CK.
- NIST AI RMF
- The NIST AI Risk Management Framework is voluntary guidance for managing AI risks, organized into four functions: Govern, Map, Measure and Manage.
- 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.
- Prompt injection
- Prompt injection is an attack on AI applications in which crafted input, typed directly or hidden in content the model reads, overrides the developer’s instructions.