PRISM by Block Convey

Product Introduction

1. PRISM by Block Convey is an open, plug-and-play compliance layer designed for AI startups and developers to integrate audits, bias checks, and explainability reports into their workflows. It provides automated tools to ensure AI models meet regulatory, ethical, and transparency standards during development and deployment. The platform operates as a modular layer that integrates seamlessly with existing AI pipelines.
2. The core value of PRISM lies in enabling teams to ship trustworthy AI models by embedding compliance, fairness, and transparency into every stage of the development lifecycle. It reduces the risk of regulatory penalties and reputational damage by proactively addressing governance gaps. The platform prioritizes speed and adaptability for fast-moving teams in dynamic regulatory environments.

Main Features

1. PRISM automates model audits by scanning for compliance with global AI regulations like the EU AI Act, GDPR, and sector-specific frameworks. It generates audit trails with actionable insights and flags non-compliant model behaviors in real time. The system supports custom rule sets to align with internal governance policies.
2. The platform performs bias detection using explainable AI (XAI) techniques to identify demographic disparities in model outputs. It quantifies bias metrics across protected attributes like gender, race, and age, and provides mitigation recommendations. Users can benchmark results against industry standards such as ISO/IEC 24027.
3. PRISM’s AI Regulation Radar tracks real-time updates to AI governance policies across 150+ jurisdictions. It offers curated summaries of regulatory changes, compliance deadlines, and ethical guidelines. Users receive automated alerts for policy shifts impacting their deployed models or geographies.

Problems Solved

1. PRISM addresses the challenge of maintaining compliance in fragmented and rapidly evolving AI regulatory landscapes. Manual tracking of governance requirements often leads to oversights, delays, and non-compliance risks. The platform eliminates the need for fragmented third-party audit tools.
2. The product targets AI startups, enterprise ML teams, and developers building high-stakes models in regulated industries like finance, healthcare, and public sector AI. It is optimized for organizations lacking in-house legal or governance expertise.
3. Typical use cases include pre-deployment model validation for credit scoring algorithms, bias audits for hiring tools, and compliance reporting for healthcare diagnostic AI. Teams also use PRISM to monitor post-deployment model drift against updated fairness standards.

Unique Advantages

1. Unlike siloed audit tools, PRISM combines regulatory tracking, bias analysis, and audit reporting in a unified API-first platform. Competitors typically focus on single aspects like bias detection without governance integration.
2. The AI Regulation Radar uses NLP to analyze policy documents and extract jurisdiction-specific obligations, a feature absent in most compliance tools. It maps regulatory clauses directly to technical model parameters for precise adherence.
3. PRISM’s competitive edge stems from its open architecture, allowing customization for niche regulations like New York’s Algorithmic Bias Law (Local Law 144). It supports 30+ pre-built compliance templates while enabling low-code adjustments for unique use cases.

Frequently Asked Questions (FAQ)

1. How does PRISM integrate with existing ML pipelines? PRISM provides REST APIs and Python SDKs to embed audit checks into training or inference workflows without refactoring code. It supports integration with TensorFlow, PyTorch, and major MLOps platforms like MLFlow.
2. Which regulations does PRISM currently cover? The platform covers the EU AI Act, U.S. Executive Order on AI, Canada’s AIDA, Singapore’s AI Verify Framework, and sector-specific rules like HIPAA for healthcare AI. Coverage expands via weekly updates to the Regulation Radar.
3. Can users customize audit parameters for proprietary models? Yes, teams can define custom fairness thresholds, add organization-specific bias metrics, and modify compliance scoring algorithms through a no-code dashboard or YAML configuration files.
4. How does PRISM handle bias detection in unstructured data? The platform applies adversarial testing and counterfactual analysis to NLP and computer vision models, detecting biases in text generation or image recognition outputs. It uses SHAP values and LIME explanations for model interpretability.
5. Does PRISM support real-time monitoring for deployed models? Yes, it offers continuous monitoring via inference-layer interception, tracking performance metrics, fairness drift, and compliance status. Alerts are triggered through Slack, email, or webhooks when thresholds are breached.