What is the difference between AI security and cloud security?
Short answer
Cloud security protects digital infrastructure, including servers, networks, storage, and cloud access controls. AI security focuses on protecting machine learning models, training data, prompts, and autonomous agent workflows. While cloud security secures the environment where models and applications run, AI security defends against threats unique to machine learning, such as prompt injection and data poisoning. You need both to protect modern AI workloads.
The details
Cloud security relies on established defensive disciplines like identity and access management, perimeter firewalls, encryption, and patch management. It ensures that cloud environments remain isolated, resilient, and compliant with standards like SOC 2. In cloud security, threats typically involve misconfigured storage buckets, credential compromises, unpatched virtual machines, or exposed administrative application programming interfaces (APIs). These controls safeguard the computing platform and storage foundation, regardless of what software or workload runs inside.
AI security addresses threats that traditional firewalls and cloud access rules cannot inspect or prevent. For instance, an attacker can exploit application logic through prompt injection or poison training data without breaching the cloud network layer. Guidance from the OWASP Top 10 for Large Language Model Applications and MITRE ATLAS documents vulnerabilities specific to algorithms, model behavior, and input manipulation. Securing AI requires continuous guardrails around inputs, outputs, model weights, and pipeline integrations.
What to do
- Enforce robust cloud identity access controls and least-privilege permissions across all model hosting environments.
- Validate and sanitize all model inputs and system prompts against injection attacks.
- Secure training and retrieval datasets against unauthorized modification and data poisoning.
- Isolate AI agent execution environments to restrict outbound network actions and access to sensitive tools.
- Continuously monitor and log both cloud API activity and model input-output transactions.
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.
- 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.
- Patch management
- Patch management is the process of finding, prioritizing, testing and installing software updates that fix security flaws.