i4 Ops provides an AI‑native data‑exfiltration prevention platform that inspects data streams within AI pipelines in real time. Lightweight agents and a policy engine enforce context‑aware rules at the data‑in‑use layer, blocking unauthorized outbound transfers while logging events to an immutable audit trail. The solution integrates via standard APIs across on‑prem, hybrid, and multi‑cloud environments for regulated enterprises.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying AI across sales, manufacturing, supply‑chain, and other critical functions expose large volumes of proprietary data to continuous processing. Conventional security controls focus on perimeter defenses and user authentication, yet data can still be exfiltrated by credentialed users or shadow‑AI workloads. The resulting data loss erodes competitive advantage and creates compliance risk.
Solution
i4 Ops delivers an AI‑native data‑exfiltration prevention platform that continuously inspects data streams within AI pipelines. By embedding lightweight agents and a policy engine at the data‑in‑use layer, the system can identify and block unauthorized outbound transfers in real time, even when the request originates from a valid login. Context‑aware policies are defined by data classification tags, usage intent, and regulatory requirements, allowing legitimate analytics to proceed while restricting export paths. All events are logged to an immutable audit trail and fed into a cloud‑based analytics service that surfaces anomalies and supports forensic investigations. The platform integrates via standard APIs and supports FHIR, REST, and gRPC, enabling seamless deployment across on‑prem, hybrid, and multi‑cloud environments without disrupting existing AI workloads.
Target Audience
The primary customers are large enterprises and mid‑market organizations that run AI‑driven analytics in regulated or competitive industries—such as manufacturing, retail, energy, and customer‑experience teams—along with security operations centers responsible for data loss prevention.
Features
- Real‑time data‑flow inspection agents that hook into popular AI frameworks (TensorFlow, PyTorch, Spark) and capture provenance metadata for every tensor and model output
- Policy engine with rule‑based and machine‑learning classifiers that enforce data‑classification, destination, and user‑context constraints at the point of use
- Zero‑trust encryption of in‑flight data, combined with end‑to‑end TLS and hardware‑rooted attestation for tamper‑resistant monitoring
- Immutable audit log stored on a tamper‑evident ledger, searchable via a web console and exportable via SIEM connectors (Splunk, Elastic, QRadar)
- Adaptive anomaly detection that scores outbound requests against historical usage patterns and triggers automated quarantine or alert workflows
- Scalable SaaS analytics backend that aggregates telemetry, provides visual dashboards of data‑flow topology, and offers API access for custom reporting
- Pre‑built integrations for Industry 4.0 use cases (sales intelligence, smart manufacturing, supply‑chain optimization, customer retention, energy management) with out‑of‑the‑box data‑classification templates
- Role‑based access control and compliance templates aligned with GDPR, CCPA, and ISO 27001 requirements