AI Governance

AI Governance for the
Enterprise SDLC

AI agents that write code and prepare releases need firm rules around them. Sanciti AI builds those rules in from the start. Anything sensitive gets redacted before it leaves, and nothing is final until a person has signed off. Generated code goes through four layers of checks along the way.

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Governance controls built into the platform

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Requirements traceability

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Test coverage

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Independent security layers

Why It Matters

Why Governance Matters

Sanciti AI agents work on requirements, code, tests and release packages, which is most of the pipeline. Speed is the benefit. The catch is that a wrong assumption early on travels down the line, and the later it’s found, the more it costs to fix.

1

More than suggestions

The agents do the actual work, so checks belong at each handoff and not only at the final review.

2

Data stays protected

You want the best model for the job without handing over customer data. Sanciti AI redacts PII and PHI before a payload leaves.

3

Ready for audits

Who approved this, and on what basis? Auditors ask that a lot. Every interaction and requirement in Sanciti AI is traced to its source, so you can answer from the record instead of from memory.

How It Works

How Governance Works Across the Pipeline

Follow a single change through Sanciti AI and you can see where each control steps in.

1

The work starts with what already exists

RGEN reads the existing system and pulls out requirements, use cases and dependencies. Each one keeps its traceability, so later steps can point back to where it came from.

2

Sensitive data gets handled first

Before anything reaches a model, the guardrails classify the data and redact PII and PHI. Every prompt and response, to any model, goes through them, and policy and audit are enforced at that point.

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The agents do the work and LEGMOD directs it

LEGMOD orchestrates RGEN, CodeGen, TestAI, Deploy and PSAM, and the Secure Agent (MCP) keeps its access tightly locked down.

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Generated code gets scanned

CodeGen output goes through SAST, SCA and an LLM scan before anyone reviews it.

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Human-in-the-loop approval gates

Nothing the AI produces is final until a person approves it. RGEN, CodeGen, TestAI and Deploy hand their output to review gates, with a route to escalate anything that looks off. A human gate sits right before any merge.

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Releases go out with a paper trail

Deploy packages come with rollback plans, compliance documentation and the audit trail regulated industries ask for. Every artifact can be traced back through the earlier stages.

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Production support keeps watching

After go-live, PSAM sorts the incoming production issues and sends each ticket to whoever owns it. It also matches known resolutions and drafts responses.

Security

Security Built Into the Pipeline

Governance means little if the security underneath it is weak. Sanciti AI protects the data first, limits what the agents can do, and checks the code from four directions.

Your data

PII and PHI are redacted before a payload leaves, whichever model is used
Every prompt and response goes through the guardrails
Full lineage on every interaction

Your agents

Commands are denied by default
Repositories are scoped
The Secure Agent has no production connectivity
Role-based access (RBAC) throughout

Your code

Security scored against OWASP and NIST on every output
Zero-trust generation
Four independent layers of checks, shown below

Your releases

A human gate before any merge
Rollback plans and compliance documentation in every release package
An audit trail for regulated industries

Security Pipeline

Four Security Layers on Every Output

Code goes through SAST, SCA, an LLM scan and finally a human gate. Each catches problems the others can miss, which is the reason for having four. Anything found is fixed or escalated before the code goes further.

OWASP Top 10NISTSASTSCAZero-trust generation

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SAST

Static analysis of the generated code

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SCA

Software composition analysis for dependency risks

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LLM Scan

AI-specific vulnerability detection and hallucination checks

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✓

Human Gate

Final approval before merge, with no exceptions

Traceability and Observability

End-to-End Traceability and Platform Observability

Every artifact from RGEN, CodeGen, TestAI and Deploy can be traced back to the requirement behind it. Admins can monitor usage, tokens and response times across the platform.

Traceability

RGEN links each requirement to its stories and deliverables. CodeGen, TestAI and Deploy keep their output tied to it, and every edit leaves a version behind.

RGEN’s traceability matrix maps each requirement to its stories and deliverables
RGEN, CodeGen and TestAI save every edit or regeneration as a new version
CodeGen and TestAI let you compare any two versions
Release history is tracked at platform level

RGEN

Requirements keep their source and map to stories and deliverables

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CodeGen

Code changes are versioned and compared with the previous version

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TestAI

Test revisions are versioned and compared the same way

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Deploy

Release packages trace back through the earlier stages

Platform Observability

Admin Dashboard

Total users, conversations and messages processed
Active users and average response time
Usage trend analytics

Usage Statistics

API calls and tokens consumed
Average tokens per request
Daily trend graphs and top-user reports

User-Level Reports

API calls and token use per user
Prompt and completion tokens counted separately
Average tokens per request and active days

Tenant Overview

A super admin sees every tenant at a glance
Statistics open for any single tenant

Always On

Controls That Run Throughout

Some controls don’t belong to a single step. They stay on from the first requirement through to production support.

Audit and Traceability

Every interaction and requirement traced to its source
Requirements linked to code, tests and releases
Full lineage when auditors ask

Risk and Compliance

Formal risk register, reviewed quarterly
Continuous compliance monitoring

Runtime Monitoring

Models checked against golden datasets
Drift detection
Latency and quality SLOs

Human-in-the-Loop Oversight

RGEN: refine or regenerate a single section of a report
CodeGen: compare AI-generated changes side by side
TestAI: edit generated test files and save them
IDE plugin: approve, reject or compare each proposed edit or command, with undo

Framework Alignment

Frameworks and Certifications

Your risk and compliance teams already work from these standards. Mapping the controls to them cuts down the back-and-forth in security reviews and vendor questionnaires.

NIST AI RMF

Sanciti AI’s risk approach follows it, with a formal risk register that gets reviewed each quarter.

EU AI Act

The guardrails enforce policy and audit against it. Human oversight and full lineage back that up.

ISO 42001, GDPR and CCPA

Controls are mapped to all three, and compliance monitoring runs continuously. It doesn’t wait for an audit to start.

Certifications held by V2Soft, the company behind Sanciti AI

Scope and details are on the Certifications page.

Industries

Industries We Serve

Healthcare

For healthcare teams, patient data comes first. PHI gets redacted before a model sees anything, and every change can be tied back to a requirement.

Insurance

A claims or policy system has years of business rules buried in its code. When it’s modernized, those rules are carried forward, not rebuilt from scratch.

Automotive

V2Soft holds TISAX AL3. Requirements are also traced through to their tests, which gives you a trail when someone asks for safety evidence.

Enterprise IT

Any prompt, from any team or agent, goes through the same guardrails, so one set of rules covers the whole organization.

Book a Live Demo

In the demo we run a real pipeline while you watch. You’ll see sensitive data get redacted, code get scanned, an approval happen and the audit trail fill in. If you’ve got a use case, bring it along.

AI Governance for theEnterprise SDLC

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