Product capability
Review the specific controls and interfaces included in the proposed product configuration. A feature described for one product should not be assumed to exist across the entire portfolio.
Resources & evaluation
GENELLIPSE FOR ENTERPRISE
Trust & Responsible AI Centre
Practical evaluation guidance for security, data governance, responsible AI, and accountable enterprise adoption.
THE DECISION BEHIND THE TECHNOLOGY
Can your team demonstrate why an AI-enabled action was permitted?
Trust is a set of reviewable decisions about a particular use case. A low-risk drafting assistant and an agent allowed to update financial records need different controls. Start with the potential harm, information sensitivity, and authority being delegated; then ask for evidence appropriate to that boundary.
Explore the decision and evidence flow
Select a stage to inspect its owner, evidence, and stopping condition.
CONTROLLED PROGRESSION
Intended use
Define permitted users, affected people, decisions, and prohibited actions. Classify the consequences of a wrong answer or action.
- Accountable owner
- Business and risk owners
- Evidence to retain
- Use-case assessment · authority boundary
- Stop or escalate when
- Do not evaluate against an undefined purpose or expand authority without another review.
ILLUSTRATIVE BUSINESS CONTEXT
Illustrative review: a servicing assistant drafts a response using policy information. A document contains a conflicting instruction and an old policy version. The useful test is whether the assistant rejects the instruction, identifies the version conflict, and routes the case for review—not whether its prose sounds confident.
Your discussion checklist
Use these prompts with your team. Checked items are a local working aid, not a readiness certification; they reset when you leave this guide.
0 / 4prompts reviewed
Know what is being evaluated
Trust depends on the selected product, configuration, providers, and operating model. This centre explains the review topics and how Genellipse can work with your team; it does not assert a certification or a contractual service commitment.
Delivery responsibility
Identify what Genellipse configures, what the customer operates, and what a hosting or model provider supplies. Document the boundaries before implementation.
Evidence and commitments
Request available security documentation and relevant evidence through a scoped evaluation. Any certification scope, contractual obligation, or service level must be verified in the applicable documentation.
Data handling and access
Genellipse can help map information from its source through processing, review, retention, and disposal. Use a data inventory to keep that discussion specific.
Purpose and permitted use
Identify sensitive fields, approved purposes, source restrictions, and the minimum data needed for the workflow. Use synthetic or de-identified examples during early demonstrations where practical.
Processing and retention
Confirm hosting and model-provider locations, provider use of submitted data, retention periods, deletion procedures, and export needs. Record the agreed answers in the solution design.
Permissions and traceability
Review user and agent identities, access boundaries, activity records, and approval authority. Test both permitted actions and attempts that should be denied.
Responsible AI through the lifecycle
For Cortex AI and AI-assisted workflows, Genellipse can help define the task, evaluation dataset, approval boundaries, and operational measures before extending automation.
Define the intended use
Specify the business task, supported inputs, expected outputs, and situations in which a human must decide. Identify uses outside the evaluated scope.
Evaluate quality and limitations
Test representative cases, missing information, conflicting evidence, and inappropriate requests. Evaluate source support, task success, failure handling, and relevant fairness concerns.
Approve consequential actions
Require the agreed reviewer for actions that change records, affect a person, or create a financial commitment. Define when an agent should stop, ask for help, or return an incomplete result.
Monitor and improve
Review errors, overrides, feedback, usage, and cost. Re-evaluate material changes to models, prompts, tools, or data, with a documented rollback or suspension process.
How the products support the discussion
The products contribute different parts of the evaluation. Genellipse can help your specialists assess their fit together.
GeneData and GeneAnalytics
Review data quality, lineage, ownership, access, metric definitions, and the evidence behind analytical results.
Cortex AI and AegisNow
Review agent permissions, business authority, exception handling, model assumptions, and professional sign-off in the chosen workflow.
GeneComply and GeneSecure
Review how control evidence, review responsibilities, security telemetry, and investigations connect to your existing operating processes.
Prepare a focused trust review
Bring your security questionnaire, data classification, intended use, and proposed deployment requirements. Genellipse can coordinate a review and identify which evidence is available and which questions need technical validation.
Review output
A record of requirements, supporting evidence, open questions, owners, and acceptance criteria.
Security enquiry
Use the security and trust enquiry option to arrange an initial discussion. Do not include credentials, sensitive records, or confidential incident details in the public contact form.
Ask for evidence that answers a specific question
Use the review questions below to assemble an assurance pack. Framework alignment supports a structured assessment; it does not by itself establish certification or regulatory compliance.
Information exposure
Can someone retrieve information they are not allowed to see? Request permission tests covering different user groups, sensitive fields, logs, exports, and retrieval indexes. Confirm deletion propagation and legal retention requirements with the relevant owners.
Decision reliability
Does the workflow distinguish an unsupported answer from a supported one? Review source versions, test coverage, reviewer corrections, and the handling of contradictory evidence. Compare error severity as well as average accuracy.
Human accountability
Can the reviewer decline, correct, or pause the action before harm occurs? Look for real decision authority, understandable evidence, workload capacity, and recorded overrides. A compulsory click is not meaningful oversight.
Operational response
Can the team isolate an affected model, tool, dataset, or workflow without losing the evidence? Request an incident scenario with named responders, communications, recovery checks, and conditions for restart.
Apply controls in proportion to the consequence
Use a local risk assessment rather than a generic label. NIST describes its AI Risk Management Framework as voluntary guidance for incorporating trustworthiness into AI design, development, use, and evaluation.
Drafting and search
Restrict sources and output use; make provenance and uncertainty visible. Evaluate whether incorrect summaries could still mislead a consequential downstream decision.
Recommendations and prioritization
Assess who may be disadvantaged, how recommendations are challenged, and whether a reviewer sees alternatives. Monitor outcomes by relevant populations where lawful and appropriate.
Actions with financial or customer impact
Require explicit transaction authority, separation of duties where needed, bounded execution, reconciliation, and exception handling. Define conditions that prevent automatic action rather than relying only on after-the-fact monitoring.
Standards and further reading
Primary references for the related guidance. Engagement-specific architecture, controls, and obligations require their own assessment.