AI & ML Companies
Protect your model weights, training data, and GPU infrastructure
Model weight exfiltration, cryptominer deployment on GPU clusters, and insider data theft are the defining threats for AI labs, LLM platform companies, and ML-native startups. ManySignal monitors your S3 buckets, Kubernetes workloads, and ML engineer identities — detecting threats before your IP leaves your environment.
$100B+
Estimated value of proprietary AI model weights at risk of theft
GPU hijacking
Top cloud security incident type for AI companies (2023)
ISO 42001
New AI management system standard — certification increasingly required
EU AI Act
High-risk AI systems face mandatory monitoring and audit requirements
How ManySignal protects AI companies
Model weights and training data protection
Proprietary model weights, fine-tuning datasets, and RLHF data are the crown jewels of an AI company — and they are stored in cloud object stores that are routinely misconfigured or over-permissioned. ManySignal monitors access to model storage (S3, GCS, Azure Blob), GPU cluster workloads, and ML experiment tracking systems (MLflow, Weights & Biases) to detect exfiltration attempts.
- S3 and GCS access baselining per ML engineer role
- Bulk model weight download anomaly detection
- ML experiment tracking system access outside normal research hours
Model weights and training data protection
AI infrastructure and GPU cluster security
AI training infrastructure — A100/H100 GPU clusters on AWS, GCP, or Azure — is expensive and a target for cryptominer deployment and competitor espionage. ManySignal monitors Kubernetes workloads on GPU nodes, container image provenance, and IAM access to compute APIs. It detects cryptominer process patterns, unexpected container pulls from external registries, and GPU workloads initiated by non-ML service accounts.
- GPU workload anomaly detection — non-training processes on GPU nodes
- Container image pull from external or unexpected registries
- IAM privilege escalation in ML platform service accounts
AI infrastructure and GPU cluster security
ISO 42001 AI management system monitoring
ISO 42001 is the emerging AI management system standard, addressing AI risk management, transparency, and governance. ManySignal provides continuous monitoring evidence for ISO 42001 requirements — particularly controls around AI system access, data integrity, and audit trails for AI decisions. For AI companies seeking certification, ManySignal's audit export simplifies the certification assessment.
- ISO 42001 Section 8.4 (AI system lifecycle monitoring) evidence
- Model version access audit trail for governance requirements
- AI incident management timeline tracking per ISO 42001 A.6
ISO 42001 AI management system monitoring
Standards and compliance requirements supported
AI company security — common questions
How does ManySignal protect model weights that are stored in S3 or GCS?
ManySignal ingests AWS CloudTrail (for S3 object-level logging) and GCS Audit Logs to monitor every access event on buckets containing model weights. It baselines which ML engineers, CI/CD service accounts, and inference services normally access which buckets. Bulk object listing or download outside normal training or deployment workflows triggers an immediate alert with the account identity, source IP, and object count.
Can ManySignal detect if an employee is exfiltrating model weights before leaving the company?
Yes. This is one of the highest-priority insider threat scenarios for AI companies. ManySignal combines user behaviour analytics with data egress monitoring: it detects large S3 downloads to new IP addresses or personal cloud accounts, unusual model file access on days adjacent to resignation notices, and access to model repositories outside the employee's normal project scope.
Does ManySignal support ISO 42001 AI Management System certification?
ManySignal provides monitoring evidence for ISO 42001:2023 requirements — specifically Section 8.4 (AI system operation and monitoring), A.5 (AI risk assessment), and A.6 (AI incident management). The platform tracks access to AI systems, monitors for operational anomalies, and provides audit trail evidence for governance controls. ManySignal's own ISO 42001 certification is in progress — contact [email protected] for the current status.
How does ManySignal monitor Kubernetes workloads running LLM inference?
ManySignal ingests Kubernetes API server audit logs and monitors pod exec events, container privilege escalations, and service account token usage in namespaces running LLM inference. It detects cryptominer process injection via unusual CPU/GPU utilisation patterns correlated with new container exec events, and flags inference pods communicating with unexpected external endpoints.
What compliance requirements are most relevant for AI companies?
AI companies face: SOC 2 Type II (required by enterprise customers), ISO 27001:2022 and ISO 42001:2023 (for AI-specific governance), GDPR and CCPA (for platforms processing user data), and emerging AI-specific regulations including the EU AI Act (for high-risk AI systems). ManySignal monitors continuously for all of these frameworks simultaneously.
Protect your model weights from the first day
Connect your AWS or GCP environment, ML experiment tracking system, and GitHub. We'll show you model weight access baselining and insider threat detection in one session.