How is Prefactor different from governance documentation tools like Credo AI?

Credo AI produces governance documentation — model cards, bias reports, compliance artefacts. Prefactor enforces governance operationally — continuous assessment, inline blocking, and approval routing on every agent run. They're complementary: Credo documents that governance exists, Prefactor ensures it executes.

How does Prefactor calculate risk scores for agents?

Prefactor generates a composite risk score from three dimensions: outcome quality (did the agent produce the right result), cost efficiency (was the spend proportionate), and scope adherence (did the agent stay within approved boundaries). Each dimension is scored per run, and the composite score drives enforcement actions.

Runtime Visibility and Control for AI GRC Teams

Q: Can we set different risk thresholds for different agent types?

Yes. Thresholds are fully configurable per agent, per team, per business unit, or per risk classification. A customer-facing agent might have stricter quality thresholds than an internal automation agent. Risk appetite is encoded in the configuration.

Q: Does Prefactor support our existing compliance framework?

Prefactor is framework-agnostic and maps to EU AI Act, NIST AI RMF, ISO 42001, SOC 2, and industry-specific requirements. The operational controls and audit trails adapt to your framework rather than imposing a new one.

Q: How does Prefactor support regulatory reporting?

Every risk assessment, threshold breach, and enforcement action is recorded in an immutable audit trail. This data is exportable in formats suitable for regulatory reporting, internal audit, and risk committee review. Evidence is generated continuously, not compiled at reporting time.

Move beyond policy documentation with operational visibility, runtime boundaries, and enforceable controls for enterprise AI systems.

The Challenge: Traditional GRC Models Were Not Built for AI Runtime Activity

AI systems evolve continuously across workflows, teams, and connected systems — often faster than traditional governance and review processes can adapt.

Policies Without Runtime Enforcement

Most organizations have AI principles, governance frameworks, and risk policies — but little operational visibility into whether AI systems are actually operating within those boundaries.

Operational Risk Is Difficult to Quantify

Permissions, integrations, workflow access, and runtime activity evolve continuously over time. Without centralized runtime visibility, organizations struggle to understand where operational risk is emerging.

Evidence Gaps Appear During Reviews and Incidents

When internal audit, risk teams, or regulators request operational evidence, organizations often have policies and governance documents — but limited runtime visibility into what AI systems actually accessed, changed, or triggered.

AI Systems Span Multiple Teams

AI deployments increasingly cut across engineering, security, operations, platform, legal, and business teams, making ownership and operational accountability harder to maintain.

How Prefactor Helps AI GRC Teams Stay in Control

Prefactor provides runtime visibility, operational boundaries, and intervention across AI systems as adoption scales.

Runtime Boundaries

Define operational boundaries around what AI systems can access, automate, and change — with enforcement directly at runtime.

Real-time enforcement
Access restrictions
Action blocking and throttling
Approval and escalation workflows

Runtime Risk Scoring

Aggregate runtime activity, permissions, integrations, policy violations, and sensitive data exposure into a unified operational risk signal.

Multi-factor scoring
Configurable thresholds
Trend-based alerts
Automated safeguards

Operational Drift Detection

Detect changes in runtime behavior, access patterns, integrations, and workflow activity before they become operational incidents.

Drift monitoring
Runtime anomaly detection
Risk pattern alerts
Automated escalation workflows

Approval Routing

Route high-risk actions, operational exceptions, and escalation events to the right stakeholders with full runtime context.

Context-rich approvals
Intelligent routing rules
Async approval workflows
Escalation chains

Runtime Activity History

Every runtime action, access attempt, escalation, and policy decision is logged and queryable.

Immutable activity records
Full-text search
Operational investigation support
Incident and audit workflows

Agent Inventory

Maintain visibility into active AI systems, ownership, connected systems, frameworks, and operational scope across the organization.

Agent registration
Ownership visibility
Connected system tracking
Deployment and lifecycle status

Built for Enterprise AI Governance Operations

Prefactor supports operational visibility and runtime control across:

Internal AI copilots

Workflow automations

AI-enabled operational tooling

Multi-agent systems

Customer-facing AI systems

MCP-connected workflows

Cross-functional AI environments

Supports Enterprise Governance Frameworks

Prefactor helps organizations operationalize AI governance frameworks with runtime visibility and enforceable controls.

EU AI Act

Runtime visibility, operational boundaries, and immutable activity history support governance expectations for high-risk AI systems.

NIST AI RMF

Operational monitoring, runtime visibility, and intervention workflows align with ongoing AI risk management practices.

ISO 42001

Agent inventory, operational controls, and runtime evidence support AI management system requirements.

Three Lines of Defense

Operational teams maintain runtime visibility, governance teams configure boundaries and thresholds, and audit teams access operational history and evidence.

SOC 2 and Internal Audit

Immutable runtime records and operational visibility support internal reviews, incident investigations, and audit processes.

Frequently Asked Questions

How is Prefactor different from AI governance documentation tools?

Prefactor focuses on runtime visibility and enforceable controls. It helps teams understand what AI systems actually accessed, changed, or triggered, and provides operational evidence beyond policy documentation.

How does Prefactor support AI risk scoring?

Prefactor combines runtime activity, permissions, integrations, policy violations, sensitive data exposure, and operational drift into risk signals that teams can monitor and act on.

Can we set different risk thresholds for different agent types?

Yes. Thresholds can be configured by agent, team, business unit, environment, or risk classification, with routing and intervention workflows attached to high-risk activity.

Does Prefactor support our existing compliance framework?

Prefactor helps operationalize frameworks such as the EU AI Act, NIST AI RMF, ISO 42001, SOC 2, and internal audit programs through runtime visibility, boundaries, and evidence records.

How does Prefactor support regulatory reporting?

Every runtime action, access attempt, escalation, and policy decision is logged and queryable, giving teams operational evidence for regulatory reporting, internal audit, and risk reviews.

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