The security posture of AI systems — covering model security, data security in inference, prompt-injection defense, supply-chain provenance, and deployment posture.
AI security is the security posture of AI systems across the full deployment lifecycle. It includes: model security (model integrity, supply-chain provenance, weights protection), data security (data flow during inference, encryption at rest and in transit, deployment posture), prompt-injection and adversarial-input defense, integration security (authentication, authorization, audit logging across connected systems), and operational security (monitoring, incident response, vulnerability management).
AI security is a growing concern as AI moves from prototype to production. Specific threats include: prompt injection (adversarial input that manipulates agent behavior), model extraction (recovering model weights or behavior through query patterns), data exfiltration (extracting sensitive context the agent has access to), and supply-chain attacks (compromised model weights, compromised training data, compromised orchestration infrastructure).
AI deployments are increasingly entrusted with sensitive data and high-stakes decisions. Without AI-specific security posture, organizations carry risk that traditional security frameworks don't fully address.
Prompt-injection defense — input filtering, output validation, deterministic guardrails on agent actions
Model supply-chain provenance — signed model artifacts, reproducible training pipelines, vetted base models
Data security in inference — deployment posture choice (on-premise, air-gapped) for sensitive workloads
Integration security — least-privilege scoping per integration, full audit logging across connected systems
Beth's security posture is built around the AI-specific threats: deterministic guardrails on agent actions (regardless of model behavior), input validation and output filtering, deployment-posture choice for sensitive workloads, model supply-chain provenance documentation, integration permission scoping, and full audit logging. Standard enterprise security framework alignment (SOC 2, ISO 27001, sectoral) layered on top.
Beth's deterministic guardrails prevent the agent from taking actions outside its configured scope, regardless of model behavior under prompt injection. Input validation and output filtering provide additional defense layers. The combination ensures that even successful prompt injection on the model layer can't lead to unauthorized actions in the integration layer.
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