Best practices for responsible AI implementation

February 8th 2025 | Box News

From automating time-consuming tasks to uncovering data insights, artificial intelligence (AI) can transform your business operations. But how do you ensure an AI model delivers accurate responses and maintains data privacy? Implementing responsible AI practices helps you address concerns about reliability, security, and transparency.

With best practices, you can establish a framework for the responsible use of AI, navigate ethical challenges, and set your organization up for success.

What is responsible AI?

Responsible AI is a set of principles and practices that allow businesses to develop, deploy, and use artificial intelligence in an ethical, transparent, and accountable way across every business application. The goal is to minimize risks associated with bias, data privacy violations, and unethical practices when using AI-powered platforms or systems.

What’s more, with enterprise AI adoption, ethical use of AI models helps distinguish your business from the competition. According to a PwC survey, 46% of organizations invest in responsible AI tools to differentiate their businesses, products, and services.

If your goal is to implement trustworthy AI, keep in mind that your solution must be transparent about its principles, especially when it comes to data use.

Key principles of responsible artificial intelligence

Think of responsible AI implementation as the foundation of your enterprise AI strategy. It helps comply with regulatory requirements and build trust with stakeholders. By prioritizing fair principles, you ensure every step of your AI integration reflects your company’s values while managing potential risks that might impact your reputation or financial stability, such as data breaches, compliance violations, and biased decision-making.

When developing your own policies for AI usage, consider these responsible AI principles.

Fairness: Ensure AI-powered applications treat all individuals without discrimination, which involves conducting regular audits and using diverse sources of training data
Transparency: Provide clarity on how AI and machine learning models work, including the datasets they use
Accountability: Establish clear ownership and responsibility for AI systems and their potential impact
Privacy: Safeguard personal data by implementing strong data governance practices
Ethical use: Use AI in ways that align with societal values and moral standards, avoiding harmful applications
Security: Protect AI systems from malicious cyberattacks, data breaches, or leaks
Inclusivity: Develop and deploy AI systems that are accessible to people of all backgrounds and abilities
Sustainability: Evaluate the environmental impact of AI technologies and strive to minimize their carbon footprint

Why commit to the ethical and responsible use of AI

AI models rely on data to train and improve. Without a responsible AI framework, you risk compromising privacy by inputting sensitive data without proper consent.

When you use AI responsibly, you show partners, clients, and employees how committed you are to ethical principles and data integrity. Benefits include:

Reduced risk of bias: Industries that create AI-powered content, including images, videos, and text, must avoid publishing articles or launching campaigns that reinforce stereotypes or misrepresent groups. Adopting reliable AI models minimizes the risk of reinforcing stereotypes and prevents your system from prioritizing certain topics or perspectives over others.
Enhanced security: Implementing robust measures to prevent security threats is a premise of responsible AI adoption. It involves enforcing practices like access controls, file encryption, and regular audits to protect content from misuse or potential exposure of personally identifiable information (PII).
Compliance with data privacy and protection regulations: An Accenture report reveals that 77% of organizations consider the future regulation of AI a current company-wide priority. Responsible frameworks help companies navigate complex data privacy laws, such as GDPR, by prioritizing sensitive information privacy.
Improved trust: Communicating how your AI systems function and make decisions enhances credibility and fosters stronger customer relationships. For example, a media company should clarify how its AI models enable content discovery based on user’s preferences and browsing history, assuring customers that their data stays secure.

Looking for new AI applications? Discover how to use AI for business.

Responsible AI implementation: 5 best practices for businesses

Global Market Intelligence forecasts the AI governance market size will grow at a CAGR of over 52% between 2024 and 2032, reaching $5.4B at the end of this period. One of the reasons for this growth is that organizations increasingly seek guidance from services that support ethical AI policies and initiatives.

To drive the sustainable use of this technology, follow these five best practices for responsible AI implementation.

1. Assess content readiness

Before integrating AI into your operations, evaluate the content you’re working with. A Box-sponsored IDC survey shows that only 10% of business data is structured, which means 90% of your data may require additional processing for AI to understand and extract value.

Make sure to address these critical areas:

Identify different types of content, such as documents, images, and videos
Categorize your content into structured, semi-structured, and unstructured formats
Clean and validate data to remove errors or duplicate files before feeding it into AI models

Ensuring your information is accessible, clean, and consistent is the first stage to transform your content into a resource for intelligent analysis.

Understand content intelligence and how it can benefit your business.

2. Support content with metadata

Metadata management allows AI models to understand and contextualize content. It’s like giving AI a roadmap to interpret information accurately, reducing the risk of skewed or outdated outputs. For example, a marketing team can tag blog posts with relevant keywords, target audience segments, and campaign goals to help an AI model quickly locate and retrieve documents for tailoring new strategies.

3. Look for secure, responsible AI tools

Responsible artificial intelligence is all about selecting tools that stick to their AI principles. Look for platforms that comply with data privacy regulations, don’t train models using customer content, and have built-in safeguards against misuse.

For example, if you manage sensitive customer data, make sure your AI solution offers secure retrieval-augmented generation (RAG) models. Features like password protection and granular access controls prevent users without the proper file or folder permissions from locating, viewing, or manipulating documents they shouldn’t access.

Discover how secure RAG addresses your AI-related concerns

4. Test AI’s effectiveness in extracting content value

Without testing your trusted AI model, you can’t be sure it delivers the results you want. For example, if you’re using Intelligent Content Management to speed up content creation, see if your team can personalize information, summarize extensive reports, or catch grammar mistakes.

Regular testing of AI systems helps extract insights from unstructured data. This practice involves running pilot programs to verify AI reliability in terms of accuracy, efficiency, and the ability to process data without distortion or errors.

5. Monitor the impact of your responsible AI framework

Assessing AI performance enables you to spot any unintended result — such as an AI platform delivering wrong answers based on your documents. For example, a legal department using AI to organize case files must ensure that the AI accurately retrieves all relevant documents and doesn’t omit critical case details.

Periodically reviewing and adjusting your framework helps maintain standards of AI responsibility and ensures that the system aligns with your goals.

Check out the best practices for measuring the impact of AI on your business.

Deploy and use AI responsibly with Box AI

Box provides a unified platform for creating, storing, and managing your content through its entire lifecycle. With our Intelligent Content Management platform, you gain insights from your critical documents, while enhancing collaboration and harnessing the full power of AI.

With Box AI, you have access to responsible AI models that are never trained on your data without consent. Plus, enterprise-grade security features like encryption and granular access permissions keep your information accessible and protected from any potential threats.

You can integrate Box with 1,500+ applications, including your favorite platforms for communication, document creation, CRM, project management, and more. You can also extend Box AI capabilities to other business apps, enabling efficient content creation, accurate information retrieval, and data insights — all from within the tools you already use.

While we maintain our steadfast commitment to offering products and services with best-in-class privacy, security, and compliance, the information provided in this blog post is not intended to constitute legal advice. We strongly encourage prospective and current customers to perform their own due diligence when assessing compliance with applicable laws.