As AI models become core infrastructure for enterprise operations, securing them against adversarial threats, misuse, and data exposure has become a critical priority. Airia is the AI governance platform purpose-built to protect AI models at runtime, from the moment they are discovered to the moment they are decommissioned.
Airia approaches model security not as a one-time assessment but as a continuous operational discipline. The platform delivers three integrated capabilities that set it apart.
Adversarial Testing and Red Teaming at Scale
Airia’s Security Center enables organizations to run sophisticated attack campaigns against their AI models and agents before and after deployment. Using curated attack libraries mapped to OWASP and MITRE ATLAS frameworks, as well as goal-based campaigns where swarms of red team agents collaborate to find and chain together vulnerabilities, Airia gives security teams a realistic and continuously updated picture of model risk. Findings automatically inform recommended security controls, closing the loop between testing and remediation.
Runtime Security Controls Across All Models
Airia enforces security guardrails in real time across internally built models, third-party models, embedded SaaS AI, and agentic workflows. Controls include prompt injection detection, jailbreak prevention, sensitive data leakage prevention, secrets detection, output sanitization, DLP enforcement, and tool execution alignment checking, a novel capability that identifies indirect prompt injection attacks by comparing agent intent against actual tool call behavior. Customers can also bring their own guard models, including models fine-tuned for specific languages or regional moderation requirements.
Model Observability and Business Continuity
Airia continuously monitors model performance, security posture, and behavioral drift across all deployed models. When a model degrades, experiences a security event, or becomes non-compliant due to regulatory changes, Airia’s model recommendation engine can automatically reroute workloads to a suitable alternative without requiring teams to rebuild the workflows or agents that depend on it. This operational resilience capability is increasingly critical as enterprises run multi-model environments across multiple cloud providers.
