Source before synthesis
Material output should identify the source, version, time, scope, and unresolved contradiction needed to evaluate it.
AI GOVERNANCE + HUMAN AUTHORITY
Use AI to accelerate the work. Keep authority attributable.
THONIS designs AI as an evidence-aware assistant inside a governed workflow. Models can retrieve, compare, classify, explain, draft, and propose. Deterministic controls and qualified people decide what is accepted, released, submitted, published, or executed.
No borrowed assurance. Reference to NIST, ISO, OMB, or another framework means it informs the design; it does not establish assessment, certification, compliance, authorization, or endorsement.
Company-wide AI control model
Assistance, evidence, control, authority, and outcome stay distinct.
Provisional before accepted
AI interpretation remains visibly proposed until the qualified reviewer corrects or accepts the controlling meaning.
Deterministic gates
Identity, permission, payload, policy, freshness, required evidence, and acknowledgement are checked outside a model where they control consequential action.
Exact human authority
Approval is attributable and applies only to the evidence, scope, version, and result the person actually reviewed.
Change reopens work
A material change to evidence, model, prompt, policy, source, configuration, or target invalidates dependent approval and triggers targeted reassessment.
Failure remains visible
Unavailable tools, weak retrieval, conflicting sources, low confidence, and missing evidence remain Unknown, blocked, or escalated instead of being polished into certainty.
Data and model boundary
Customer information is not an assumed training asset.
Approve the exact corpus
Define permitted records, classifications, rights, markings, locations, retention, deletion, and prohibited data before processing begins.
Select for the environment
Document provider, model and version, hosting, subprocessors, training posture, logging, access, residency, isolation, and exit requirements.
Authorize capabilities separately
Retrieval, code execution, connectors, writes, publication, and external actions each require scoped identity, permission, receipts, failure handling, and recovery.
Preserve qualified review
Role, independence, conflict, competence, segregation of duties, approval scope, and override behavior must match the consequence of the decision.
Lifecycle assurance
Evaluate the system that is actually operating.
Bound and test
Threat-model the workflow; test expected, adversarial, degraded, denial, override, and recovery conditions; define prohibited behavior and stop conditions.
Observe and constrain
Record model and policy identity, input boundaries, tool calls, failures, human interventions, outcome checks, and incidents at a level appropriate to the risk.
Reassess what moved
Changed sources, permissions, models, prompts, tools, policies, environments, and intended uses can reopen the affected evidence, tests, and approvals.
Primary design references
Use recognized frameworks without turning them into badges.
NIST AI RMF 1.0
Govern, Map, Measure, and Manage provide a voluntary structure for trustworthy and responsible AI risk management.
NIST AI 600-1
The Generative AI Profile identifies risks and actions specific to generative systems.
ISO/IEC 42001
ISO/IEC 42001 describes an AI management-system standard. THONIS does not claim certification.
SCOPE AN AI-AUGMENTED WORKFLOW
Bring the consequential decision, current systems, approved evidence, and accountable authority.
THONIS can help design a connected, traceable, evidence-backed workflow in which AI reduces friction without silently inheriting authority. Do not send sensitive source material through first contact.
Discuss an AI-augmented workflow