Scoped access
Connect only approved data sources, systems, tools and actions to each AI service.
Security & Governance
Enterprise AI with control built into execution.
Security and governance are not only infrastructure concerns. They also determine what AI can access, which models it can use, what actions it may take, how outputs are validated and where human approval is required.
Governance principle
The business process should remain the source of control. AI reasoning operates inside approved data, tool, validation and approval boundaries.
Security by design
Control the context AI can see and the actions it can take.
Each enterprise deployment should be designed around the customer’s architecture, data classification, identity model and security requirements.
Identity & permissions
Align solution access with enterprise identity, role and authorization patterns where applicable.
Data handling
Design data flows around customer policy, sensitivity, storage requirements and permitted processing boundaries.
Integration control
Use scoped connectors, APIs and tool permissions rather than broad system access.
AI governance
Govern models, reasoning and outputs separately.
Aigent separates business control from model intelligence so the enterprise can change models without losing the process framework around them.
Model Governance
Select public, private, open-source or hybrid model strategies according to workload, security, privacy, quality, latency and cost requirements.
Business Rules
Keep methodology, decision criteria, policies and workflow stages outside the LLM as explicit business controls.
Deterministic Validation
Check calculations, schemas, mandatory evidence, cross-source consistency and policy conditions independently of AI generation.
Human Approval
Require review or confirmation at defined thresholds, exception conditions or sensitive actions.
Output Contracts
Specify required sections, formats, evidence, calculations, confidence or acceptance criteria before execution.
Traceability
Preserve the relevant path from inputs and evidence through agents, tools, validations, exceptions, approvals and final output.
eSynapse controls
Ready-made agents still need enterprise boundaries.
Packaged capability does not mean uncontrolled access. eSynapse deployments can be configured around the data, systems and business actions each agent is permitted to use.
Voice Agent boundaries
Control call flows, scripts / verified facts, permitted CRM actions, escalation and captured data.
Search permissions
Respect authorized repositories and return source-backed responses from permitted content.
BI data scope
Limit analytics to authorized systems, datasets and business measures.
Talent workflow control
Define recruiter review points, criteria and the role AI is allowed to play in the hiring workflow.
Deployment architecture
Design around customer policy.
Deployment and model choices should be evaluated per customer environment. Security, privacy, residency, latency, integration and operating requirements determine the appropriate architecture.
Customer architecture alignment
Fit the solution into the customer’s identity, network, cloud, data and integration environment.
Model choice
Select model providers or private / open-source options based on customer requirements and workload characteristics.
Compliance support
Design the technical and operational controls needed to support the customer’s applicable compliance obligations. Certifications and regulatory status remain environment- and scope-specific.
Security discussion
Talk to the teamNeed to review architecture, data flow or governance before discussing the use case?
We can start from your enterprise security and AI governance requirements, then design the solution path around them.