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Songlines Control × MuleSoft
Add AI Governance to MuleSoft Without Another Transit Layer.
Use Songlines Control as a pluggable Policy Decision Point alongside MuleSoft Anypoint Platform. MuleSoft continues to orchestrate enterprise traffic. Songlines Control evaluates AI-specific policy, records the decision, and correlates cross-stack evidence — without taking ownership of the underlying request path.
Designed for mature enterprise environments. Start with one API flow, one business unit, and one evidence objective.
REST APIMetadata-only evaluationCustomer-hosted optionFail-open or fail-closedCross-stack evidence
MuleSoft sidecar
Request path remains in Anypoint Platform
Metadata only
Enterprise flow
SAP
Salesforce
MuleSoft Anypoint
Internal app
API client
Policy decision point
Metadata evaluatedIdentity, model, region, use case, classification.
Decision returnedApprove, deny, redact, flag, log, or human review.
Evidence linkedPolicy and request references preserved.
MuleSoft applies
- Existing routing logic
- Approved AI provider
- Required safeguards
- Source telemetry
- Governance record
Your business payload stays in MuleSoft. Songlines Control receives only the metadata required to make and record a governance decision.
The architecture question
Enterprise orchestration is already solved. AI-specific evidence is not.
MuleSoft can route traffic, apply API policies, authenticate callers, transform payloads, and connect complex systems. Cetus AI does not replace those functions. It adds a governance-specific decision and evidence layer for AI activity spanning business platforms, cloud providers, models, and human review.
| Existing capability | Retains responsibility | Cetus AI adds |
|---|---|---|
| MuleSoft API orchestration | Routing, mediation, transformation, connectivity | AI-specific policy decision and evidence reference |
| Enterprise identity | Authentication, role, group, and service identity | Governance context for user, agent, model, and use case |
| DLP and security tooling | Network and endpoint data controls | Application-layer AI context, decision evidence, and PII workflow |
| Azure, AWS, SAP, Salesforce logs | Platform-specific telemetry | Cross-stack correlation and regulatory evidence production |
| GRC processes | Policy ownership, risk acceptance, review | Runtime decision records and traceable control evidence |
Pluggable policy decision point
One lightweight decision call. No replacement middleware.
Before MuleSoft invokes an AI service, it sends Songlines Control a metadata-only policy request describing the user or service, requested model, business use case, data classification, jurisdiction, and expected cost. Songlines Control returns a decision that MuleSoft can enforce using its existing flow logic.
Continue after redaction, model substitution, logging, or human-review controls are applied.
Stop the request and return the policy reason plus an approved alternative where configured.
Continue or pause according to policy and create a review task.
Preserve evidence without changing the request path.
The pattern supports fail-open and fail-closed configurations. A customer-hosted Policy Decision Point can run within the organisation’s Azure tenant or private environment, reducing network dependency and keeping policy metadata inside the chosen trust boundary.
Controlled flow
From request to evidence in nine controlled steps.
01
Request received
MuleSoft receives an AI request from SAP, Salesforce, an internal application, or another enterprise service.
02
Context extracted
The flow identifies the caller, use case, model, region, data classification, and policy context.
03
Policy evaluated
MuleSoft calls the Songlines Control Policy Decision API using metadata only.
04
Decision returned
Songlines Control returns approve, deny, flag, log, redact, or require-human-review.
05
Conditions applied
MuleSoft applies the required action using its existing orchestration logic.
06
AI service called
The request is sent to the approved Azure, AWS, internal, or third-party model.
07
Outcome captured
Tokens, cost, latency, model, region, and control outcomes are recorded.
08
Evidence correlated
The decision is linked to the MuleSoft request ID and source-system logs.
09
Export produced
Cross-stack records become ADM, policy, audit, cost, sustainability, and board evidence.
Designed for enterprise engineering teams
A clear request. A deterministic response. A traceable record.
The examples demonstrate the evaluation contract. Exact schemas, security controls, metadata allowlists, and performance targets are agreed during technical discovery and validated in a production-like pilot.
Policy request · application/json
{
"request_id": "enterprise-mule-2026-08-22-7a3f2c91",
"source": {
"system": "sap-successfactors",
"business_unit": "corporate-hr",
"user_role": "hr-analyst"
},
"ai_request": {
"provider": "azure-openai",
"model": "gpt-4o",
"region": "australiaeast",
"use_case": "candidate-screening-summary",
"data_classification": "confidential-hr"
},
"context": {
"contains_pii": true,
"adm_qualifying": true
}
}Policy response · application/json
{
"decision": "APPROVED_WITH_CONDITIONS",
"conditions": [
"PII_REDACTION_REQUIRED",
"HUMAN_REVIEW_REQUIRED"
],
"policy_refs": [
"ENTERPRISE-HR-003",
"ENTERPRISE-ADM-001"
],
"audit_id": "aud-2026-08-22-7a3f2c91"
}The differentiator
MuleSoft orchestrates the interaction. Cetus AI produces the governance record.
Songlines Control links the MuleSoft request ID to policy decisions and telemetry from Azure Monitor, AWS CloudTrail, SAP BTP, Salesforce Event Monitoring, and internal ML logs. Evidence can then be reviewed at the level of an individual decision, a system, a business unit, or the enterprise estate.
ADM Register
Inventory of systems that make or materially assist decisions affecting individuals.
Individual Decision Record
Evidence of data categories, model, policy outcome, human oversight, safeguards, and source logs.
Policy Enforcement Log
Approvals, denials, conditions, overrides, and review events linked to policy references.
Cryptographic Audit Chain
Tamper-evident record linking each governance event to the previous entry.
Cost Attribution
AI cost by platform, model, workflow, business unit, and project.
Sustainability Evidence
AI compute and emissions evidence for reporting workflows.
Human Capability Evidence
Optional timestamped records of demonstrated reasoning through Cogito Coach.
Review a sample regulatory evidence structure →
Sample outputs demonstrate report structure and evidence design. Customer results depend on connected source systems, policy configuration, and data quality.
Sample outputs demonstrate report structure and evidence design. Customer results depend on connected source systems, policy configuration, and data quality.
Enterprise planning model
Quantify the manual effort that policy automation and evidence correlation may return.
This calculator models labour efficiency only. It deliberately excludes breach avoidance, licence consolidation, routing optimisation, and other benefits that require customer-specific evidence.
Adjustable assumptions
Model the value of returning manual policy-review and evidence-preparation time. Change every assumption to match your own estate.
Illustrative efficiency case
Annual hours returned1,104 hrs
Annual efficiency value$165,600
Estimated payback13.0 months
Three-year net value$316,800
Policy-review hours returned840
Evidence-preparation hours returned264
Annual review events modelled48,000
Planning model only. It excludes breach avoidance, licence consolidation, model-routing savings, revenue effects, and implementation risk. It is not a quote, guarantee, or financial forecast.
Deployment patterns
Choose the trust boundary that fits the workload.
No deployment pattern is universally superior. The architecture review determines which model is appropriate for each workload, evidence objective, and control owner.
Evidence-Only
Ingest approved logs read-only and produce cross-stack governance evidence. No runtime policy decision.
Best for: Organisations starting with evidence and disclosure readiness.
Hybrid Sidecar
Run the Policy Decision Point inside the customer environment; use the Cetus AI management and reporting plane in Australia.
Best for: Mature enterprises seeking control without a new transit layer.
Dedicated Instance
Deploy the complete platform in a dedicated customer tenant or private cloud environment.
Best for: Regulated workloads and strict isolation requirements.
Managed Control Plane
Route selected AI workloads through the full managed platform where central enforcement is appropriate.
Best for: Greenfield workloads, defined business units, and consolidated AI services.
Security and reliability
Built to be evaluated by enterprise security teams.
Data minimisation
Policy evaluation can use metadata only; prompt content is not required for every decision.
Customer-hosted option
Run the Policy Decision Point within the customer’s trust boundary.
Encryption
Encrypt data in transit and at rest; publish exact key-management responsibilities by deployment mode.
Failure mode
Configure fail-open, fail-closed, local cache, retry, and alert behaviour by workload risk.
Auditability
Link each decision to the originating MuleSoft request and source-system evidence.
Residency
Offer Australian deployment options and document subprocessor or cross-border implications.
Start with one real flow
Prove the architecture and the evidence before considering scale.
Select one business unit, one MuleSoft-managed AI flow, and one evidence objective. Cetus AI will map the current controls, configure the Policy Decision Point, correlate the required logs, and produce a pilot Regulatory Compliance Export.
No enterprise-wide commitment. No assumption that every workload requires runtime enforcement.
01DiscoveryConfirm the genuine gap and avoid duplicating controls already in place.
02DesignDefine metadata, decisions, source logs, failure mode, and data boundary.
03IntegrateAdd the policy call and evidence connectors to the selected flow.
04ValidateTest policy outcomes, latency, resilience, audit integrity, and operational ownership.
05EvidenceProduce the first tailored export and enterprise-scale recommendation.
Technical questions
Frequently asked questions.
Does Songlines Control replace MuleSoft?
No. MuleSoft remains the orchestration and integration layer. Songlines Control adds AI-specific policy decisions and cross-stack evidence production.
Does AI traffic pass through Cetus AI?
Not in the sidecar pattern. MuleSoft sends a metadata-only policy request and retains the full business and AI request path. Other deployment modes remain available where a managed control plane is appropriate.
What happens if the policy service is unavailable?
The workload owner selects fail-open, fail-closed, local cache, retry, timeout, and alert behaviour according to risk. These settings are validated during the pilot.
Can the Policy Decision Point run in our environment?
Yes. The architecture supports a customer-hosted Policy Decision Point inside the organisation’s chosen Azure tenant or private environment.
Is the integration production-ready?
The sidecar contract and deployment patterns are ready for technical evaluation. Customer-specific performance, security, failure-mode, and operational requirements must be validated in a production-like pilot before any production commitment.
Does the platform make us compliant?
No platform guarantees compliance. Cetus AI supports governance processes and produces evidence that can help organisations prepare for board, audit, privacy, and regulatory review.
Architecture review
Bring the architecture you already trust.
We will identify what is already solved, where evidence remains fragmented, and whether a Songlines Control sidecar adds a defensible enterprise capability.