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Leader in AI-First Product Engineering Read Full Article IT Managed & Outsourcing Contact UsAI is only as trustworthy as the practices behind it.
This document outlines Appinventiv’s artificial intelligence policy for data governance across every AI engagement, the principles we operate by, the controls we put in place. And the standards we hold ourselves to when building AI systems for enterprise clients represent what a robust company AI policy looks like in practice.
Our Governing Principles
The AI policies and principles that guide every engagement. Every AI system we design, build, or deploy is governed by a set of non-negotiable principles. These aren't aspirational statements, they are operational standards embedded into how our teams work.
We collect and process only the data that is necessary for the AI system to function. If it is not needed, it is not used.
Every model we build is documented. Every decision an AI system makes within a client's environment should be explainable to internal stakeholders, regulators, and end users where applicable.
AI systems built by Appinventiv include defined checkpoints where human review is required before outputs are acted upon. Automation does not mean the absence of accountability.
Data provided by clients for AI development remains theirs. We do not use client data to train models for other engagements, improve internal systems, or share with third parties outside the agreed scope of work.
Every AI project at Appinventiv has a designated governance owner. Responsibility for data handling, model behavior, and compliance alignment is assigned before development begins, not after.
DATA CLASSIFICATION FRAMEWORK
Not all data carries the same level of risk. Part of governing it responsibly is being clear about the types of data this policy applies to and the level of care each requires.
Across AI engagements, Appinventiv works with four broad categories:
Information that identifies or could reasonably be used to identify individuals. This includes names, contact details, financial records, health information, and behavioral data. This category carries the highest protection requirements and is subject to the strictest access and handling controls across every stage of the engagement.
Client-owned information that is commercially sensitive. Operational data, customer records, pricing models, internal processes. This data is treated as confidential throughout the engagement and is not used outside the agreed project scope under any circumstances.
Datasets used to train, fine-tune, validate, or benchmark AI models. The origin, preparation, and use of this data is documented at each stage. Clients retain visibility into what was used and how, and can request changes under agreed terms.
Data that has been generated or transformed to remove identifying information. Where possible, we use synthetic or anonymized data during development and testing phases to limit exposure of sensitive information. When real data is required for validation, access is restricted and logged.
Each category is handled according to its risk profile. Controls are applied proportionally, not uniformly.
Data governance at Appinventiv is not a post-deployment checklist. It is built into every phase of how we work with client data.
Before any data is ingested into an AI pipeline, we work with clients to define what data is required, where it originates, and what restrictions apply. Data sources are documented, and ingestion is scoped strictly to project requirements.
Client data used in AI development is stored in controlled environments with role-based access. Access is granted on a need-to-know basis and logged throughout the engagement. Storage environments are aligned with our AI security policy and the compliance requirements applicable to the client's industry and region.
When client data is used to train or fine-tune models, we document what data was used, how it was prepared, and what transformations were applied. Clients retain visibility into this process and can request data removal or retraining under agreed terms.
Where possible, we use anonymized or synthetic data during testing phases to reduce exposure of sensitive information. When real data is required for validation, access is restricted to authorized team members and logged accordingly.
At deployment, we work with clients to establish data handling protocols for live environments, including retention schedules, access permissions, and escalation paths for data-related incidents.
We do not retain client data beyond the scope of the engagement without explicit agreement. At project close, data disposal follows a documented process aligned with applicable regulations.
HOW WE IMPLEMENT AI GOVERNANCE IN OUR DEVELOPMENT PROCESS
Governance is not a layer added on top of development. At Appinventiv, it is embedded into the workflow from the first sprint to final deployment.
Every AI engagement begins with a governance scoping session. We identify the risk profile of the system being built, map applicable compliance requirements, define data ownership, and assign governance roles before a single line of code is written.
Governance controls are integrated directly into development pipelines. This includes data validation checks, access controls on model training environments, version control for datasets and models, and documented approval workflows for any changes to model behavior.
Before a model moves from development to staging, it undergoes a structured review. This covers performance against defined metrics, fairness and bias checks relevant to the use case, and documentation of known limitations. Models are not progressed without sign-off from the designated governance owner.
Prior to go-live, we conduct a pre-deployment audit that reviews data handling configurations, access permissions, output logging setup, and compliance documentation. Any gaps identified are resolved before deployment proceeds.
For engagements that include ongoing support, we implement monitoring protocols that track model behavior, flag anomalies, and trigger review processes when outputs fall outside defined parameters. Governance does not end at launch.
Any significant change to a deployed AI system, including model updates, data source changes, or shifts in use case, including adoption of new generative AI capabilities, triggers a governance review under our generative AI policy before the change is implemented.
THIRD-PARTY AI TOOLS AND VENDOR GOVERNANCE
Enterprise clients reasonably want to know whether third-party AI tools are being used during their projects and, if so, how that is managed.
Appinventiv maintains a vendor evaluation process for any third-party AI tools, platforms, or foundation models used during client engagements. Before a tool is approved for use on client work, it is assessed against our AI usage policy criteria that include data handling practices, contractual data protections, compliance posture, and whether client data could be used to train or improve the vendor's own models.
This includes foundation models and generative AI tools, covered under the same generative AI policy standards as all other approved platforms. Tools that do not meet our standards for client data protection are not used in client-facing work. There is no exception to this based on how useful a tool might be.
Where third-party AI tools are used in an engagement, this is recorded as part of the project's governance documentation. Clients can request visibility into which tools were used and the basis on which they were approved.
The AI tooling landscape changes quickly. Our vendor evaluation process is reviewed regularly to reflect how these tools evolve and what new platforms bring to the table in terms of both capability and risk.
EMPLOYEE RESPONSIBILITIES
A governance framework is only as effective as the people working within it. At Appinventiv, responsible data handling is part of how teams are expected to work, not something left to individual judgment.
Everyone involved in an AI engagement, including engineers, data scientists, project leads, and delivery managers, operates under internal acceptable use standards that form part of Appinventiv's broader AI security policy governing how client data is accessed, stored, shared, and used throughout the project.
Team members working on AI projects are expected to understand the data classification and handling requirements relevant to their role. Access to client data is granted based on what the project requires. It is revoked when that need ends.
Governance ownership on every project sits with a designated lead who is accountable for ensuring these standards are followed from the first data ingestion through to final delivery.
Internal practices are reviewed as our AI capabilities grow and as regulatory expectations around AI continue to develop. What is required of our teams today will keep evolving. That is the nature of this space.
COMPLIANCE FRAMEWORKS WE ALIGN WITH
Appinventiv's AI development practices are designed to support compliance with the regulations and standards most relevant to enterprise AI deployments. The frameworks below inform how we structure data handling, model documentation, and governance controls across engagements.
Alignment with these frameworks is embedded into our AI policy and project governance process. Specific compliance requirements applicable to a client's industry and geography are identified during the scoping phase and carried through the engagement.
YOUR DATA RIGHTS AS A CLIENT
How We Stay Accountable Principles require enforcement mechanisms. Here is how Appinventiv maintains accountability across AI engagements:
You can request a summary of what data was used in your AI project, how it was processed, and what controls were applied at any stage of the engagement.
We maintain documentation for every model built or fine-tuned during your engagement, including training data provenance, evaluation results, and known limitations.
If you require specific data to be removed from training sets, pipelines, or storage environments, we have a defined process to action this within agreed timelines.
If your organization is subject to an internal or external audit related to an AI system we built or contributed to, we provide documentation and support to assist that process.
If a data-related incident occurs within the scope of an engagement, clients are notified promptly and in line with the notification timelines required by applicable regulations. Initial notification happens as soon as an incident is confirmed. Updates follow as the situation becomes clearer. Clients are not kept waiting for a complete picture before being informed that something has happened.
HOW WE STAY ACCOUNTABLE
Principles require enforcement mechanisms. Here is how Appinventiv maintains accountability across AI engagements:
Every AI project has a named governance owner responsible for ensuring data handling practices, model documentation, and compliance alignment are maintained throughout the engagement.
AI projects are subject to internal governance checkpoints at defined stages, pre-development, pre-deployment, and at any significant change milestone. These reviews are documented, retained, and form part of our standing AI policies.
We use a dedicated set of governance, monitoring, and compliance tools, including platforms for model monitoring, bias auditing, data access governance, and compliance lifecycle management to enforce controls programmatically rather than relying solely on manual oversight.
Our governance practices are reviewed and updated as regulations evolve, new frameworks emerge, and our understanding of responsible AI matures. What we publish here reflects how we work today, and we are committed to improving it.
