Ethics and Compliance as Foundations, Not Checkboxes
In the burgeoning world of Artificial Intelligence enabled software offerings, it’s tempting for new companies entering the marketplace to see compliance and ethics as boxes to tick. In reality, an ethically designed, and legally compliant approach to building a product, is the bedrock required for a sustainable, trustworthy, AI business. The companies that last will be those that take these issues seriously, not necessarily because regulators force them to, but because their customers, employees, and partners demand it.
The Risks of Cutting Corners
If you’ve been following the news, you’re well aware that the global software market is in the middle of what can only be described as an “AI gold rush.” For legal, security, and compliance teams, this creates a push and pull between supporting the needs of internal stakeholders to access the newest, bleeding edge technologies, and protecting company and customer data from exfiltration, misuse or theft - a very reasonable fear originating from too many real-world horror stories of products, rushed to ship, by vendors that have skipped critical safeguards, ignored ethical considerations, or treated legal compliance as an afterthought.
For both new market entrants and mature organizations alike, when developing AI software offerings, it is important for legal, security, and compliance teams to proactively address the fears of potential customers and stress that, while a “think about it later” mentality might shave weeks off of a product roadmap, it can introduce extreme, company-ending, risks that can create customer distrust, reputational harm, civil litigation and regulatory exposure.
Embedding Ethics from Day One
Ethical AI use in software development means asking not just: “can we build this?” but also “should we build this, and if so, how do we build it responsibly?” To put it another, perhaps more technical, way, ethical AI use in software development requires designing, building, and deploying AI systems in ways that are transparent, fair, privacy-protective, and accountable.
Mature AI companies understand that responsible practices must be baked into the DNA of their operations, not bolted on later. At Vivun, this means aligning every internal organization, from product, to security and legal, all the way through sales, around a common framework for responsible AI use. By embedding these values early, building products with a customer’s concerns in mind, and keeping interdepartmental communications open and on a frequent schedule, a company can avoid the much harder problem of retrofitting governance into an already scaled system.
While internal reticence or hesitation to compliance evaluations should be a yellow flag for legal and security teams, it should be treated as an opportunity to enhance collaborative practices by sharing knowledge, explaining risk profiles, and inspecting future roadmaps.
Vivun’s Principles of Ethical AI Development
At Vivun, we adhere to the following core principles of ethical AI development, to help ensure we meet our goal of building world class products that our customers can trust. Our six core principals are:
- Transparency
- AI systems should not operate as “black boxes” - customers should know what is being ingested by an AI system, where it’s going, and how it is being used.
- Proper documentation that is routinely reviewed by engineering, legal and security and is publicly shared, ensures that customers not only know where their data is going, but can rest assured that they will be notified as a product evolves.
- We tell our customers what we collect and how we use it - with the goal of ensuring that our customers have confidence in our product. We assess our external documentation, including legal agreements, security information, and engineering resources proactively and with calendared frequency.
- Bias Mitigation
- Every AI system carries the risk of replicating or amplifying bias. Mature businesses that aim to ethically create products must invest in processes that identify, monitor, and reduce bias in both training data and outputs.
- In practice, this does not just mean stress testing your product, but sharing and workshopping the results internally to allow the collective to share their insights.
- Our team of engineers stress test for biases in our product with each evolution and iteration. Our teams know that customer trust is contingent on a good experience, and that we must be vigilant when it comes to building systems that interact with their inputs.
- Privacy Protection
- The efficacy of an AI system is contingent on data the customers provide, but maximizing potential results should never be used to justify eroding individual rights. Strong privacy safeguards protect customers, reinforce trust, and keep companies aligned with global data protection standards.
- This means that we collect only the data that is needed for the benefit of the customer, and covenanting to leverage a customer’s data solely for the customer’s benefit. This also means that we do not use a customer’s data for the model training of any third party and that we do not use a customer’s data for data model training that would benefit Vivun.
- Safety and Reliability
- AI systems should be rigorously tested to prevent unintended consequences - full stop.
- We require that fail-safes, monitoring mechanisms, and human oversight be both incorporated into the design of our AI systems and explained to department heads to maximize the knowledge share.
- Responsible Governance
- Ethics aren’t just technical challenges—they require leadership. Accountability structures, cross-functional oversight, and ongoing training on each department’s understanding of best practices ensures that the development of AI systems remains aligned with both law and the developer’s core ethical principles. Mechanisms for auditing, reporting, and redress are equally as important as surfacing information to colleagues.
- We believe that departmental leadership is not just showing up, but also owning the process of creating and providing ample resources to our development teams. We routinely audit and resurface our internal governance policies to our teams and provide easy to understand breakouts of why certain limitations or standards should be adhered to.
- Human-Centric Design
- As far as core principals go, this is likely the most difficult to nail down - but at its foundation, “Human-Centric Design” is the process of building your product to benefit the customer, not yourself.
- When a product is designed for the maximized benefit of the product developer, users are rightfully wary of substantively interacting with that product, which reduces trust and long term viability of the offering.
- Our use of AI Systems are designed to enhance human capabilities and respect the value of a customer's privacy; they convey a respect for the customer’s dignity, autonomy, and rights. It is not just routine, but of fundamental importance, that we perennially ask: “is this for the benefit of the customer?”
How Legal Can Support Ethical AI Development
At Vivun, we believe that the legal team plays a critical role in ensuring that principles of ethical AI development are more than aspirational; they are actionable. By partnering closely with engineering, product, and security teams, Vivun Legal helps build the structures that uphold the core values enumerated above.
- Transparency: Legal ensures that customer-facing agreements, privacy policies, and security documentation are accurate, clear, and updated on a regular cadence so customers know exactly how their data is used.
- Bias Mitigation: Legal provides frameworks for documenting testing results, facilitating cross-functional reviews, and ensuring that disclosures reflect fair, bias-aware practices.
- Privacy Protection: Legal safeguards trust by embedding data-minimization principles into contracts, enforcing limitations on data use, and aligning operations with global privacy standards.
- Safety and Reliability: Legal helps define and document accountability measures, such as incident response and oversight protocols, that keep product safeguards enforceable and auditable.
- Responsible Governance: Legal supports leadership by drafting and maintaining governance policies, driving training on compliance requirements, and ensuring clear mechanisms for accountability and redress.
- Human-Centric Design: Legal reinforces the principle that customer benefit comes first, ensuring that policies, terms, and product practices respect individual rights and dignity.
By providing the above, and keeping an always open door, Legal doesn’t just monitor compliance; it acts as a strategic partner, embedding ethical safeguards into the DNA of Vivun’s AI systems and helping to earn and sustain the trust that our customers place in us. For legal teams,
Compliance and Ethics as Competitive Advantage
Far too often compliance is viewed as a burden. In today’s market, however, it is an indispensable asset. Companies that commit, early on, to demonstrate transparency, fairness, and respect for data privacy differentiate themselves from competitors. More importantly, they build durable relationships with customers who know they can trust the integrity of the product and the people behind it.
The future of enterprise AI belongs to companies that treat ethics and compliance not as obligations, but as opportunities to lead.
