01
Interoperate
Connect to what is already working.
Work alongside enterprise orchestration, cloud, business, identity, security, and monitoring platforms instead of replacing them.
AI governance — from first step to enterprise scale
Build Trusted AI Governance. Start Where You Are.
Whether you are establishing your first AI governance framework or integrating controls across a complex enterprise environment, Cetus AI provides the platform, evidence, and expertise to move from initial visibility to enforceable governance.
Start with a baseline, a focused pilot, or a review of the controls already in place. The pathway can expand as your governance requirements mature.
Enterprise architecture
Additive governance pattern
Existing stack retained
Existing enterprise stack
MuleSoft
SAP
Salesforce
Azure
AWS
Internal ML
Identity · DLP · SIEM · GRC
Cetus AI governance layer
Policy Decision APIApprove, deny, flag, redact, log, or require review.
Read-only log ingestionCollect source references without replacing control ownership.
Evidence correlationLink identity, request, model, policy, cost, and oversight.
Governance outputs
- ADM system register
- Individual decision records
- Policy evidence
- Tamper-evident audit chain
- Cost and sustainability
- Human oversight evidence
Add governance capability without replacing the enterprise platforms that already orchestrate, secure, monitor, and record the underlying workload.
Stage 1
Beginning the Journey
Establish a baseline, uncover Shadow AI, and understand the policy and evidence foundations your organisation needs first.
Assess your starting point →Stage 2
Scaling Governance
Move from visibility to consistent policy decisions, cost accountability, and evidence for board and assurance teams.
Explore the platform →Stage 3
Enterprise-Wide Control
Connect policy and evidence across multi-cloud, business platforms, orchestration, identity, security, and GRC systems.
Review the architecture →The enterprise delta
Your systems record AI activity. They do not automatically produce one defensible governance record.
Azure Monitor records Azure. AWS CloudTrail records AWS. SAP and Salesforce record activity within their own platforms. Cetus AI correlates those records with policy decisions, identity, model usage, data classifications, cost, and human oversight so evidence can be reviewed by boards, auditors, privacy teams, and regulators.
02
Decide
Apply AI-specific policy consistently.
Return approve, deny, flag, redact, log, or require-human-review decisions through a pluggable Policy Decision API.
03
Evidence
Create the record your stack does not.
Correlate source logs, identity, models, policies, cost, sustainability, and human oversight into reviewable governance evidence.
04
Assure
Connect controls with human judgement.
Combine technical enforcement evidence with optional timestamped records of demonstrated reasoning through Cogito Coach.
Enterprise outcome stories
See how the architecture addresses specific evidence and control gaps.
Explore four practical patterns spanning automated-decision evidence, cross-cloud logs, MuleSoft runtime policy, and human judgement. Each scenario states the gap, architecture pattern, and evidence produced.
No illustrative scenario matches both filters.
Reset the filters or choose a broader industry or compliance use case.
Illustrative enterprise scenarios based on common architecture and governance patterns. They are not customer testimonials, named deployments, or guaranteed outcomes. Customer-approved case studies will replace these examples as they become available.
What mature enterprises already have
- Azure Monitor records Azure activity
- AWS CloudTrail records AWS activity
- MuleSoft records orchestration events
- SAP and Salesforce record platform events
- DLP and SIEM record security signals
The governance record Cetus AI adds
ADM system register
Individual decision records
Policy decisions and overrides
Tamper-evident audit chain
Cost and sustainability attribution
Human capability evidence
Evidence before scale
Prove the delta before asking the enterprise to change.
The first engagement is an architecture and evidence review, not a platform-replacement proposal. The outcome may confirm a pilot, narrow the scope, or show that the problem is already solved.
01
Map what is already solved
Document the existing orchestration, identity, DLP, monitoring, GRC, and cloud controls.
02
Find the genuine evidence gap
Identify where AI-specific decisions or cross-stack assurance records cannot be produced today.
03
Validate one real workflow
Pilot one business unit, one integration path, and one board or regulatory evidence objective.
Architecture review
Start with the controls you already trust.
We will map your existing estate, identify the evidence you can already produce, and test whether Cetus AI adds a defensible capability — without assuming the answer in advance.