MLCode

Centralizes and synchronizes data security policies across heterogeneous deployment environments (cloud, on-premises, hybrid) using HexaKube's distributed orchestration layer. The system maintains a single source of truth for security rules while translating them into environment-specific enforcement mechanisms, eliminating manual policy duplication and drift that occurs when teams manage separate security stacks per environment.

Automatically captures and maps data flow through ML training, inference, and batch processing pipelines by instrumenting data access points (data loaders, feature stores, model inputs/outputs). The system builds a directed acyclic graph (DAG) of data transformations and identifies which raw data sources feed into which models, enabling security policies to be applied at the source rather than reactively at the point of breach.

Maintains a complete version history of trained models with associated metadata (training data, hyperparameters, security policies, compliance status) and enables rapid rollback to previous versions. The system validates that rolled-back models meet current security and compliance requirements before allowing deployment, preventing rollback to versions that violate current policies.

Enables training models on distributed data without centralizing sensitive data by implementing federated learning protocols where model updates are computed locally and only aggregated centrally. The system supports differential privacy techniques to add noise to model updates, preventing reconstruction of training data from model weights, and coordinates training across heterogeneous environments (cloud, on-prem, edge devices).

Applies context-aware data masking rules to training datasets before they reach model training jobs, using pattern matching and semantic analysis to identify sensitive data (PII, credentials, proprietary metrics) and redact or tokenize them. The system integrates with feature stores and data loaders to intercept data at the point of access, ensuring models never see raw sensitive values while preserving statistical properties needed for model performance.

Enforces fine-grained access controls on model inference requests by validating user identity, data context, and request metadata against security policies before predictions are returned. The system logs all inference requests with full context (user, timestamp, input features, output predictions) to an immutable audit trail, enabling forensic analysis and compliance reporting for regulated use cases.

Translates regulatory requirements (HIPAA, GDPR, SOC2, PCI-DSS) into executable security policies that can be deployed across ML infrastructure. The system maintains a library of compliance templates and uses natural language processing to map regulatory text to specific technical controls (data masking, encryption, access logging), reducing the manual effort of translating compliance documents into code.

Monitors training data and inference inputs for anomalies, statistical drift, and adversarial patterns that indicate data poisoning attacks. The system builds statistical baselines of normal data distributions during training and flags inputs that deviate significantly, using techniques like isolation forests, autoencoders, and statistical hypothesis testing to detect both obvious and subtle poisoning attempts.

Encrypts trained model weights, checkpoints, and metadata at rest using hardware-backed encryption (HSM, KMS) and in transit using TLS 1.3. The system manages encryption keys separately from model artifacts, supports key rotation policies, and integrates with cloud KMS services (AWS KMS, Azure Key Vault, GCP Cloud KMS) to avoid storing keys in MLCode infrastructure.

Continuously monitors deployed security policies across all environments and detects deviations from the intended policy state (policy drift). The system compares actual deployed configurations against the centralized policy definition, identifies which environment(s) have diverged, and generates alerts with remediation recommendations to bring drifted environments back into compliance.

Implements fine-grained access control using both role-based access control (RBAC) and attribute-based access control (ABAC) to restrict who can access which data, models, and features. The system evaluates access requests against policies that consider user role, data classification, data residency, model sensitivity, and contextual attributes (time of day, IP address, device type) before granting access.

Detects security incidents (unauthorized access attempts, policy violations, data exfiltration attempts) and automatically executes remediation workflows such as revoking access, isolating affected systems, quarantining suspicious data, or triggering manual escalation. The system uses rule-based incident detection and integrates with SIEM systems and incident management platforms (PagerDuty, Splunk) for alerting and orchestration.