It's like having a security camera that automatically identifies every AI app anyone in your company is using, even the ones nobody told IT about.

Oximy deploys an agentless, network-layer sensor that passively monitors all outbound traffic to identify connections to AI services. Machine learning models trained on thousands of known AI tool signatures perform real-time traffic classification, automatically categorizing each detected tool by type, risk level, and data sensitivity. Anomaly detection algorithms flag previously unknown or newly emerging AI services that don't match existing signatures, enabling zero-day shadow AI discovery. The system continuously updates its classification models as new AI tools enter the market, maintaining comprehensive coverage without manual rule creation. Natural language processing is applied to HTTP metadata and request payloads to distinguish between casual browsing and active AI tool usage, reducing false positives. All discoveries are logged in an audit-ready journal with timestamps, user attribution, and risk scores, giving CISOs a living inventory of their organization's AI footprint.

It's like a bouncer at a nightclub who memorizes every face, spots the fake IDs, and keeps a perfect guest list—except the nightclub is your corporate network and the guests are AI tools.

It automatically reads what employees are pasting into AI chatbots and blocks anything sensitive—like a spell-checker for secrets.

Oximy's data protection module applies natural language processing and large language model-based classifiers to inspect data in transit between enterprise users and external AI services. As employees interact with tools like ChatGPT, Claude, or domain-specific AI applications, the system analyzes prompts, file uploads, and copy-paste content in real time to detect sensitive data categories including PII, PHI, financial records, source code, and trade secrets. When a policy violation is detected, the platform can block the request, redact sensitive content, or alert the security team—all within milliseconds. The ML models are fine-tuned on enterprise-specific data taxonomies, allowing organizations to define custom sensitivity categories beyond standard PII. Reinforcement learning from human feedback on flagged incidents continuously improves classification accuracy and reduces false positives over time. The entire enforcement pipeline operates at the network layer, requiring no browser extensions or endpoint software, which eliminates the friction that typically undermines DLP adoption.

It's like having a postal inspector who reads every letter going out, blacks out the classified parts, and sends back the ones that shouldn't leave the building—but at the speed of light.

It watches how much your company spends on every AI tool and tells you which ones are a waste of money—like a financial advisor for your AI budget.

Oximy's spend intelligence module aggregates usage telemetry from its network-layer discovery engine and correlates it with procurement and licensing data to build a comprehensive cost-per-tool, cost-per-user, and cost-per-department view of enterprise AI spending. Machine learning models perform time-series analysis on usage patterns to identify underutilized subscriptions, redundant tools serving overlapping functions, and usage spikes that indicate emerging demand. Clustering algorithms group similar AI tools by capability, surfacing consolidation opportunities where multiple teams are paying for functionally equivalent services. Predictive models forecast future AI spend based on adoption trends, headcount growth, and historical usage curves, enabling finance and IT leaders to budget proactively rather than reactively. The platform generates natural language summaries and recommendations, translating complex usage analytics into actionable insights for non-technical stakeholders. Anomaly detection flags unexpected spend increases—such as a team suddenly scaling API usage—before they hit the invoice, giving procurement teams early warning to negotiate or intervene.

It's like having a financial planner who watches every streaming subscription in your household, cancels the ones nobody uses, and warns you before your teenager signs up for three more.