AI Data Security Platform | Protection & Compliance

AI security platforms protect AI applications, models, agents, and workflows from threats that traditional security tools often miss. They monitor prompts, outputs, tool calls, retrieved content, and agent actions for risks such as prompt injection, data leakage, jailbreaks, source poisoning, unsafe API calls, and behavioral drift. PromptHalo adds inline enforcement, so risky actions can be blocked before they execute.

AI data security is the practice of protecting sensitive information used by, generated by, or exposed through AI systems. It includes preventing confidential data from leaking in responses, controlling which users or agents can access data, monitoring AI behavior across sessions, and keeping audit evidence for compliance. PromptHalo enforces data-access policy inline before protected data reaches the wrong destination.

PromptHalo inspects AI inputs and outputs in real time, applying policy before a response is delivered. It can detect sensitive information moving across users, sessions, tenants, or multi-step interactions, then allow, transform, restrict, or block the action. Because enforcement runs inline rather than after the fact, exposure can be stopped before confidential information leaves the AI workflow.

No. PromptHalo is designed to work without touching proprietary models or requiring model retraining. It can be deployed through an API gateway, agent mode, or inline middleware depending on the AI application architecture. This lets enterprises add protection across AI applications from different vendors while keeping model internals, training data, and proprietary systems separate.

PromptHalo generates decision-level, tamper-evident audit logs for security teams and regulatory reporting. Each record can include the decision reason, acting agent or passport identity, tenant and session context, timestamp, and action outcome. The evidence trail is replayable, making it useful for investigations, debugging, compliance exports, and mapping controls to frameworks such as OWASP LLM Top 10 and NIST AI RMF.

PromptHalo is designed to deploy in under a day with no model retraining and no major code rewrite. Teams can integrate through API gateway patterns, SDKs, inline middleware, or agent-oriented deployment modes. The goal is to place inspection and enforcement into existing AI workflows quickly, then tune policies around the organization’s risk, compliance, and operational requirements.

PromptHalo addresses AI-native threats including prompt injection, retrieval and RAG injection, jailbreaks, data leakage, source poisoning, behavioral drift, unsafe tool actions, out-of-scope API calls, and agent-to-agent risk escalation. Its closed-loop approach connects red teaming discoveries with runtime defenses, so newly identified attack patterns can strengthen production enforcement through the shared Threat Library.

Yes. PromptHalo is positioned for enterprises and regulated environments such as financial services, fintech, and payments, where AI decisions affect transactions, compliance workflows, and customer interactions. The platform provides real-time enforcement, scoped agent authority, budget and risk thresholds, and decision-level audit evidence to help teams quantify and contain AI risk as adoption grows.