HiddenLayer

Data science team initiates download of a third-party model (.bin or .pkl file).

HiddenLayer's Model Scanner automatically intercepts or is triggered via CI/CD to scan the file.

The scanner analyzes the serialized payload to detect malicious code execution or ransomware.

A security report is generated, and the deployment is either blocked or approved based on policies.

User submits a prompt to the enterprise-facing LLM application.

The MLDR sensor evaluates the input in milliseconds against known adversarial patterns.

If a prompt injection is detected, the request is flagged and blocked before reaching the LLM.

An alert is generated and sent to the SOC via SIEM integration.

An attacker begins sending thousands of carefully crafted inputs to the AI API.

The MLDR behavioral engine detects an anomalous pattern of queries designed to map the model's decision boundaries.

HiddenLayer automatically throttles or blocks the attacker's IP/session.

Security teams are notified of the extraction attempt to conduct further forensic analysis.

A developer pushes a newly trained model to the artifact registry.

A GitHub Action triggers the HiddenLayer CLI to initiate a vulnerability scan.

The scan verifies the model's integrity and checks against CVE databases.

If the model passes, the deployment pipeline proceeds; if it fails, the pipeline breaks and notifies the team.

HiddenLayer actively monitors all AI system interactions and logs security events.

The compliance team exports periodic audit reports from the HiddenLayer dashboard.

Reports detail the model's robustness, intercepted threats, and vulnerability posture.

Documentation is provided to external auditors to prove proactive AI security measures.