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Whitepaper

Securing AI Systems

Enterprise AI adoption has moved faster than the security programs meant to govern it, and most organizations are now running models, agents, and inference pipelines that sit outside their established control frameworks. The risks are not theoretical. Training data can be poisoned to introduce behavior that surfaces only under specific conditions, prompts can be crafted to bypass guardrails and extract sensitive information, and the service accounts and API keys connecting AI systems to enterprise data are often over-permissioned and poorly monitored. This whitepaper maps the AI attack surface end to end, from data ingestion and model training through deployment and runtime inference, and sets out the security controls that apply at each stage. It gives security architects a practical basis for extending existing identity, data protection, and detection capabilities to cover AI systems rather than standing up a parallel program from scratch.