LogicGate Risk Cloud

Maps identified risks and controls simultaneously to multiple compliance frameworks (NIST CSF, ISO 27001, SOC 2, CIS), from a single assessment, eliminating per-framework re-mapping.

Maintains the policy library, routes exceptions for approval, tracks exception expiry, and ties policy requirements to associated risks and controls.

Tracks regulatory and standard updates (new NIST guidance, amended GDPR guidance, PCI DSS version updates), and maps changes to affected controls in the program.

Triggers and tracks remediation actions and treatment plans for identified risks.

Tracks regulatory obligations, controls and compliance posture across frameworks.

Connects to enterprise data sources and security and IT tools to feed risk and control data.

Manages IT and technology risk, including control assessment, monitoring and remediation.

Models risk scenarios and analyzes potential impact to support planning decisions.

Centralizes enterprise risks, controls, issues and the risk register across the organization.

Governs AI use and risk as a capability within the GRC platform, including AI inventory, risk assessment and reporting.

Delivers decision-ready risk reporting and dashboards for stakeholders and the board.

Identifies and registers risks across the enterprise from signals, assessments and connected data.

Automates GRC workflows for assessments, issues, approvals and remediation across teams.

Quantifies risk in financial or comparable terms to support prioritization and reporting.

Plans, executes and tracks internal audits with findings and remediation.

Runs vendor security questionnaires and impact assessments, centralizes third-party risk profiles with document and renewal date tracking, and links vendor risks to controls and action plans. Named to avoid collision with the Third-Party Risk Management niche label per the kit rule that feature names must not match niche names.

Generates standardized documentation such as model cards and datasheets for auditors and regulators.

Provides comprehensive audit trails of platform actions and activities across the AI life cycle.

Connects across the AI and data stack, including data governance, model observability, AI discovery and AI security tools.

Classifies, assesses and mitigates AI-specific risks such as bias and robustness, with content libraries for regulations and frameworks including the EU AI Act, NIST AI RMF and ISO 42001.

Documents trust, risk and security assessments, testing and validation results, and remediation evidence for AI systems.

Maintains a centralized, discoverable registry of all AI use cases, applications, agents and models with metadata, ownership and deployment status.

Automates AI use-case intake, risk and security assessment, sign-off, attestation and approval workflows.

Brings risk-domain data subscriptions into each third party's record, such as outside-in cybersecurity ratings, external attack surface findings, financial health, sanctions and adverse media, and ESG data, whether produced natively or ingested from a ratings or data-aggregator provider, and uses that data in scoring and ongoing monitoring so an indicator crossing a threshold updates the risk score and starts a workflow rather than only refreshing a dashboard.

Watches third parties between assessments for new risk events, such as security incidents, financial distress, sanctions or adverse-media hits and regulatory actions, and surfaces them through dashboards, reports, alerts, reminders and notifications; stronger implementations re-score the third party and trigger escalation or corrective action when an event crosses a defined threshold instead of only updating a dashboard.

Scores each third party's inherent and residual risk and measures its potential impact on the business or supply chain to produce an impact estimate, aggregating domain-level results into a composite score that can be rolled up across the portfolio and correlated with enterprise objectives and control performance.

Profiles each third party at intake, capturing criticality, data sensitivity, service type, geography and regulatory requirements, to determine which risk domains apply to it and to scope the depth and cadence of assessment accordingly.

Distributes, collects and scores third-party assessments and security questionnaires from a maintained template library that spans risk domains and standards, with evidence requests, reminders, reviewer collaboration and scoring rules; stronger implementations scope questionnaire depth and cadence dynamically from the third party's risk profile rather than sending one template to every vendor.

Turns identified risks into tracked findings and issues with owners, due dates and action plans, routes them through escalation and exception or risk-acceptance approval, recommends or preconfigures the remediation workflow, and reports status until closure.

Reads third-party-supplied documents such as SOC 2 reports, ISO certificates, policies and prior questionnaires with AI, extracts the relevant answers and evidence to prepopulate assessment responses, and evaluates submitted responses for gaps or inconsistencies so reviewers work the exceptions rather than reading every document.

Gives third parties their own portal to complete assessments, upload evidence, report issues and keep their documentation current, with customer branding and multilingual support, so much of the assessment workload shifts to the third party rather than the risk team.

Lets risk teams build and change assessment templates, scoring rules, routing, approval steps, workspaces and dashboards through no-code configuration after go-live, without vendor professional services or IT development, so the program can add a risk domain or respond to a new regulation without a project.

Ships maintained content for third-party regulations and regulated industries as prebuilt assessments, workflows, libraries and reports, for example DORA, the German Supply Chain Act, APRA and sanctions and anti-bribery laws, and updates that content as regulations change so the program does not have to build regulatory coverage itself.

Expresses a third party's risk in measurable, decision-ready terms, such as a financial exposure range or a calibrated score built from likelihood and impact, instead of a qualitative heat map, so third parties can be compared, prioritized and reported to executives on estimated impact.

Applies AI across the third-party data set to classify and score risk, flag red flags and emerging risk, recommend responses, and generate summaries and risk reports on demand, including natural-language questions over the third-party repository, rather than relying on analysts to read every record.