Models in production with no AI-specific gate.
The customer operated a machine learning pipeline at hyperscale, with thousands of model variants in production at any time and an active rate of new model deployments measured in deployments per hour. Conventional code security gates ran on the surrounding software. There was no equivalent gate that understood the AI-specific attack surface: data poisoning, supply chain compromise of model artifacts, model lineage tampering, dependency vulnerabilities in machine learning frameworks, and AI-specific attack paths against the deployed model itself.
The customer's security organization had identified the gap. The engagement was to close it.
A scanner that the pipeline cannot bypass.
The scanner is integrated into the pipeline as a required gate. No model deploys to production without passing the gate or receiving a documented exception from a named approver.
- Model lineage tracking. Every production model carries a verifiable provenance chain from training data through training run through artifact through deployment. Lineage is checked against a tamper-resistant record on every scan.
- Dependency analysis. Machine learning frameworks, model architectures, and runtime libraries are scanned against vulnerability databases continuously. Findings route into the existing security incident workflow.
- AI-specific attack path enumeration. Adversarial techniques mapped to MITRE ATLAS and OWASP LLM Top 10 are enumerated against each model class. Findings are scored, deduplicated, and tracked.
- Machine-readable output. The scanner does not produce reports. It produces findings in a structured schema that integrates with the customer's existing security operations and ticketing systems.
- False positive control. Findings are tuned against red-team output. Precision metrics are tracked per finding class. Detection rules are adjusted on a defined cadence.
A required gate, not a recommendation.
The scanner is wired into the machine learning pipeline as a hard dependency of the deployment step. Engineering teams cannot move a model into production without the scanner pass result on the model and on its dependencies. Exceptions require named approval routed through the security organization. Every exception is logged, tracked, and expires on a defined schedule.
Coverage in the reference deployment exceeds ninety-five percent of production models. The remaining few percent are documented exceptions, predominantly legacy models that predate the pipeline integration and which are scheduled for retirement.
Automated where it can be, escalated where it must be.
The system runs without operator intervention. Findings that match high-severity classes are routed directly to incident response. Findings that match lower-severity classes are routed to the responsible engineering team for remediation in their normal sprint cadence. Findings that match informational classes are aggregated into the customer's metrics for trend analysis.
The pattern carries forward into BeitSystems' cybersecurity practice as a productized engagement. AI model security scanner deployments are now offered to customers operating their own production machine learning pipelines, with the integration tailored to the customer's existing security tooling and pipeline structure.
What can be discussed openly.
The customer is not named. Specific detection rules, pipeline integration patterns, and false positive control techniques remain confidential under the engagement's continuing agreement. The scanner architecture, the integration model, and the coverage outcome are publishable and inform the cybersecurity practice.