A Spinoff of Datadigest BV
Network & Runtime AI Isolation

Multi-Tenant Security for
Inference & Training

GradSec provides hyperscalers and GPU clouds with network and runtime-level workload isolation: restricting privilege escalation, controlling data access, preventing lateral movement, and making potential cross-tenant leakage instantly visible and actionable in a user-friendly dashboard.

Closed Alpha Q3 2026 Zero-trust infrastructure GPU-level protection

Secure Shared GPU Nodes at Network & Runtime Levels

Enforce strict operational isolation boundaries, control data pathways, and limit privilege levels for parallel inference and training sessions.

Restricting Privilege Escalation

Enforces micro-segmented runtime permissions for training containers and AI endpoints, blocking container escapes and sandbox privilege abuse.

Controlling Data Access

Maintains strict cryptographic namespaces for datasets, weights, and inputs, ensuring no client or workload can access unauthorized file structures.

Preventing Lateral Movement

Isolates workload namespaces at the network level, disabling lateral connection vectors and unauthorized inter-container communication.

Real-Time Leakage Visibility

Maps activation streams, query data flows, and inter-tenant communication visually on a user-friendly dashboard to make potential leaks immediately visible and blockable.

GPU Memory Isolation (Time Slicing)

Enforces strict physical boundaries on specialized GPU fabrics using time slicing and fractional GPU resource allocation to prevent tenant cache bleeding.

Model Weight & Supply Chain Defense

Guarantees cryptographic model weight encryption in transit/rest and enforces admission control policies to block malicious runtime package dependencies.

The GradSec Active Isolation Engine

Observe how our threat detection engine analyzes memory page bounds and blocks tensor leakage in real time.

CONSOLE // ISOLATION-HYPERVISOR-v1.4.2
> Initializing GradSec Sentinel...
TENANT TRAFFIC RADAR ISOLATION SECURE
T-01
Inference
T-02
Training
GS SHIELD
T-03
Inference
ALL TENANTS ISOLATED
Zero lateral data frames detected.
Active Workloads
16 Inference
8 Training
Leakage Risk Index
0.00% Safe
Action Center

Frequently Answered Questions

GradSec is a hypervisor-level security framework built for hyperscalers and GPU cloud providers, designed to isolate weights, neural activations, and GPU memory pages across general AI workloads (both training and inference) in shared infrastructure.
Our software runs at the host runtime layer, directly monitoring tensor dimensions and cache allocations. We intercept data streams, scan neural activation paths, and clear register caches between context switches, ensuring that no tenant's dataset or query can influence or leak into another client's model state.
As specialized GPU neoclouds scale to support multi-tenant clusters, securing the raw hardware fabric is critical. GradSec secures this middle layer by enforcing physical time-slicing and fractional GPU memory boundaries, encrypting model weights at rest and in transit, and rendering full real-time runtime visibility for AI workloads (PyTorch, Ray, Jupyter) across tenant nodes.
We encourage hyperscalers, GPU cloud platforms, enterprise AI infrastructure teams, and organizations running multi-tenant AI training or inference pipelines to join our waitlist for the closed alpha.