Security at Cognic
How we build and operate systems that business data depends on.
Our Position on Security
Security is an architectural property, not a final checklist. Every system Cognic builds treats authentication, access control, encryption, audit and data protection as requirements defined during discovery — and the deployment model follows the sensitivity of the data, not vendor convenience.
Businesses hand software systems their operational data, their customers’ data and sometimes their regulated data. This page explains plainly what Cognic does about that responsibility — the practices built into our engineering process and the options available for data-sensitive deployments. Where your project requires specific regulatory certifications or attestations, we align to your compliance requirements as part of the engagement.
What We Practice
Data Security
Client data is treated as a boundary: scoped to the systems it belongs in, protected in transit and at rest, and never used beyond the engagement. Data-handling requirements are established during discovery — what data exists, where it lives, who may touch it and what rules govern it — and the architecture enforces the answer.
Authentication
Identity is verified before anything else happens. We implement standard authentication patterns — password policies, token handling, session management and single sign-on where the client environment requires it — rather than inventing auth schemes. For enterprise systems, SSO integration with the client’s identity provider is the default we build to.
Authorization & Access Control
Access is granted by role, verified at the point of action. Role-based access control is designed with the workflow during product definition — who sees what, who does what, who approves what — so permissions are a structural property of the system rather than a retrofit before launch.
Encryption
Data is encrypted in transit and at rest using standard, current mechanisms. For sensitive workflows we apply encryption at the component level — documents in storage, fields in databases — so that a compromise of one layer does not expose everything beneath it.
Audit Logging
Systems that manage business actions record what happened: who did what, when, and to what. Audit trails are designed for the workflows that require accountability — approvals, exceptions, data changes — and built as part of the action, not logged afterward from memory.
API Security
Integrations are attack surface, and we engineer them that way: authenticated credentials, scoped permissions, validated inputs, rate awareness and failure states that fail closed. Third-party services are integrated with the same discipline we apply to our own code.
AI Data Handling
AI systems add a specific question: where does business data go when a model processes it? We answer it in architecture — model and provider selection considers data sensitivity, RAG systems keep knowledge in controlled retrieval layers, prompts and outputs are scoped to the workflow, and human-review workflows ensure sensitive decisions keep a person in the loop. For data-sensitive industries, private or on-premise model deployment is available so data never leaves the client’s environment.
Environment Separation
Development, staging and production are separated environments with separated data. Real production data is not used for development experimentation; test data is representative without being real. Environment promotion follows a controlled path — the version in production is the version that was tested in staging.
Monitoring
Production systems are observed: availability, errors, usage and — for AI systems — output quality and cost. Monitoring exists so problems are found by instrumentation, not by users. Alerting routes to the people who can act, in both our team’s and the client’s operations.
Backup
Data is backed up on a schedule matched to its criticality, with restore paths that are tested rather than assumed. Backup strategy is defined with the deployment — retention, scope and recovery expectations — because a backup that has never been restored is a hope, not a plan.
Deployment Options
The sensitivity of your data determines where your system runs. We deliver to all three models:
Public Cloud
Managed cloud platforms (Azure, AWS) with the provider’s security controls plus our application-layer practices. The right default for most business systems — strong security foundations, full scalability, predictable cost.
Private Cloud
Dedicated or isolated cloud environments where data residency, isolation or organizational policy requires separation. Applied where “cloud, but ours” is the requirement.
On-Premise
Deployment inside the client’s own infrastructure, for data that cannot leave the building — common in healthcare, insurance and regulated financial work. On-premise systems receive the same engineering practices; the operating responsibility is shared by agreement.
Security in the Delivery Process
Security appears in every stage of how we work: requirements define the sensitivity and rules during discovery; architecture encodes authentication, access control and audit into the design; QA includes security testing alongside functional coverage; and deployment places the system in the environment the data requires. The result is a system whose security was designed, not appended.
For regulated industries, these practices align with the expectations of the frameworks our clients operate under — and where a specific certification or attestation is required for your engagement, that requirement is scoped and handled in the project’s compliance plan.
This page describes Cognic’s engineering practices. It does not assert certifications on behalf of any client engagement; compliance scope is defined per project.
Have Security Requirements That Shape the Project?
Tell us the data, the rules and the systems involved. Security requirements are part of the first conversation — bring them to it.