CHAKALI / TRUST CENTER

Enterprise AI with
control in the path.

A concise view of how Chakali approaches security, governance, deployment and evidence—so leaders and technical teams can evaluate the platform with clarity.

Review the architecture ↓Request the evaluation pack ↗
CONTROL POSTURE ACTIVE
Security and governance
operate with the workflow.
POLICYACCESSHUMANEVIDENCE
AI OS
Logical architecture summary · Configuration varies by deployment
00 / OPERATING PRINCIPLES

Trust is designed
into the operating model.

Three principles shape how Chakali approaches control, evidence, and deployment from the beginning.

01Control before action

Permissions, policy, model and tool boundaries are evaluated before consequential work proceeds.

02Evidence by design

Runtime records stay connected to the models, knowledge, decisions and approvals behind an outcome.

03Deployment is a strategy

Choose SaaS, private cloud or on-premises based on organizational risk and operating requirements.

01 / DEPLOYMENT ARCHITECTURE

One platform.
Three operating patterns.

The control model remains consistent while infrastructure ownership and integration boundaries adapt to the organization.

YOUR ORGANIZATION
Identity providerEnterprise knowledgeBusiness systemsSecurity controls
CONTROLLED CONNECTION
01MANAGED

SaaS

Managed application operation with organizational identity, data and integration boundaries.

Rabita Noor operated
02DEDICATED

Private cloud

Dedicated deployment pattern aligned to private networking and organizational cloud controls.

Shared operating model
03CONTROLLED

On-premises

Application and connected models operated inside infrastructure controlled by the organization.

Customer operated
IDENTITY ENCRYPTION POLICY LOGGING RECOVERY
02 / CONTROLLED DATA FLOW

Every request passes through
an accountable execution path.

This logical view intentionally communicates the control model without exposing sensitive implementation detail.

REQUEST PLANE
01User or system
02Identity + workspace
03Orchestrator
RUNTIME CONTROL GATEPurpose · Access · Policy · Risk
INTELLIGENCE PLANE
04Permission-aware RAG
05Approved tools
06Approved model
DECISION GATEGuardrail · Exception · Human approval
OUTCOME PLANE
07Action or answer
08Report
09Audit evidence
03 / GOVERNANCE EVIDENCE

From policy statement
to reviewable proof.

Governance becomes useful when requirements can be connected to runtime decisions and retained evidence.

CONTROL INPUTS
PoliciesRolesModel approvalsKnowledge scopesGuardrails
GOVERNED
EXECUTIONPolicy applied at runtime
EVIDENCE RECORD
Identity + purpose Model + knowledge Tools + actions Guardrail results Human decisions Outcome + timing
04 / FOUNDATION CONTROLS

Protect the operating
foundation.

Evaluate the deployment, identity, access, and security boundaries around the platform.

01

Security architecture

Logical control layers, execution boundaries and runtime policy enforcement.

Available
02

Deployment

Managed SaaS, private cloud and on-premises patterns aligned to organizational requirements.

Configurable
03

Identity + access

Workspace roles, scoped assets, least privilege and deployment-dependent identity integration.

Configurable
05 / AI EXECUTION CONTROL

Govern intelligence
while it is in use.

Review how data, models, organizational knowledge, and runtime decisions remain controlled.

04

Data protection

Data boundaries, encryption expectations and deployment-specific key-management choices.

Deployment-dependent
05

Model governance

Approved-provider routing, agent-level model selection and observable model usage.

Available
06

Permission-aware RAG

Authorized retrieval scopes, evidence linkage and governed knowledge lifecycle.

Available
06 / EVIDENCE + ASSURANCE

Keep every outcome
reviewable.

Understand the evidence, resilience, and responsible-AI practices that support ongoing oversight.

07

Audit evidence

Run inputs, outputs, tools, models, policy checks, approvals, timings and outcomes.

Available
08

Resilience + recovery

Backup, recovery and continuity controls defined for the selected operating model.

Deployment-dependent
09

Responsible AI

Human accountability, purpose boundaries, review paths and evidence-oriented governance.

Available
ASSURANCE & EVIDENCE

Claims grounded
in evidence.

Chakali is designed to support security, privacy and responsible AI governance controls. Certifications and attestations are stated only when supported by current, independently verifiable evidence.

During an appropriate evaluation, qualified organizations may request available control mappings, architecture documentation, recovery commitments, penetration-testing summaries and security questionnaire responses.

SECURITY + ARCHITECTURE

Evaluate Chakali
around your requirements.

Bring your deployment boundary, data classes, identity model, critical integrations and governance requirements.

Request an architecture review ↗[email protected]