Monitaur

Automatically monitors deployed AI models for performance degradation, data drift, and behavioral anomalies in real-time. Generates alerts when models deviate from expected baselines without requiring manual checks.

Creates and maintains comprehensive audit logs of all AI system decisions, inputs, and outputs for regulatory compliance. Automatically captures evidence needed for SOC 2, HIPAA, and other compliance frameworks without manual documentation.

Detects when AI model performance degrades over time or after updates, comparing current performance against historical baselines. Alerts teams to performance regressions before they impact users or business outcomes.

Provides tools to define and customize governance frameworks tailored to specific organizational needs and regulatory requirements. Enables teams to build governance policies without requiring engineering resources.

Enforces configurable governance policies across AI workflows, blocking or flagging decisions that violate organizational rules. Enables teams to define custom policies for bias detection, output validation, and risk thresholds without code changes.

Identifies and flags instances where AI models generate false, misleading, or unsupported information. Automatically detects hallucinations in LLM outputs and other generative AI systems to prevent misinformation.

Detects and blocks prompt injection attempts that try to manipulate AI system behavior through malicious inputs. Identifies suspicious patterns in user inputs designed to override system instructions or extract sensitive information.

Continuously monitors AI model outputs for demographic bias, fairness violations, and discriminatory patterns across protected attributes. Generates reports on fairness metrics and identifies when models treat different groups inequitably.

Generates compliance reports and evidence artifacts automatically based on collected audit data and monitoring results. Creates documentation needed for regulatory audits, certifications, and stakeholder reviews without manual compilation.

Manages governance policies and monitoring across multiple AI models and applications from a centralized platform. Enables consistent policy enforcement and compliance tracking across diverse AI systems without managing each separately.

Automatically assesses and scores risks associated with AI system deployments based on model characteristics, data sensitivity, and use case criticality. Generates risk profiles and recommendations without manual risk analysis.

Tracks the complete lineage of data flowing through AI systems, documenting where data originates, how it's transformed, and where it's used. Provides visibility into data provenance for compliance and debugging purposes.