Smart AI Needs Smarter Data – How Data Governance Delivers Both

Smart AI Needs Smarter Data - How Data Governance Delivers Both

Discover how implementing effective data governance for AI can ensure security, compliance, and smarter decision-making.

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Agentforce and AI apps are revolutionizing how businesses operate. However, the rapid growth of data that comes with using AI brings a critical challenge: managing it responsibly. The solution? Data governance.

Managing data securely and responsibly is now more important than ever. Data governance for AI helps businesses stay compliant with changing regulations and maintains customer trust. But, when governance is overlooked, it can lead to security breaches, compliance issues, and a loss of customer confidence.

More data equals more complexity

The stakes are high. Non-compliance with data privacy laws can cost businesses an average of $14.8 million, with data breaches now running around $4.45 million per incident. These costs further highlight the need for strong data governance.

However, as companies use AI to create relevant, personalized experiences, these technologies also raise new security and privacy questions.

Today, around 80% of IT professionals feel pressured to adopt AI, but nearly half are concerned that their current security systems can't keep up with its rapid evolution. Most existing systems weren't built to handle the massive volumes of data that Agentforce and AI apps require.

Unlike traditional apps, such as rule-based chatbots, Agentforce and AI apps need significantly more data to function effectively due to several reasons:

• Learning from data: AI models learn patterns and make decisions by analyzing large datasets. The more data they have, the better they understand and generalize, allowing them to perform more effectively.
• Complexity of tasks: Agentforce takes on challenging tasks like natural language processing, image recognition, and predictive analytics. These tasks rely on uncovering subtle patterns and nuances that can only be captured through vast amounts of data.
• Model training: Training an AI model means fine-tuning millions - or even billions - of parameters to minimize errors. This process requires significant data to help the model learn and perform accurately.
• Continuous improvement: AI systems get smarter over time. As they're exposed to new data, they adapt and improve their performance.
• Handling variability: Agentforce often needs to deal with unpredictable inputs and diverse scenarios. For example, a language model must understand slang, dialects, and context. This requires training on diverse datasets.

Agentforce and AI apps have the potential to save time, boost productivity, and enhance efficiency across operations. The key to utilizing AI effectively and efficiently lies in strengthening data governance practices so you don't introduce new vulnerabilities.

3 steps of data governance for AI: turning compliance into a competitive advantage

As AI systems analyze massive amounts of data, trusted data becomes essential for success. As data volume and complexity grow, so do the risks of mishandling it. Here are some actionable steps your organization can take to strengthen its data governance strategy.

Step 1. Mask sensitive data at all stages of the development process

Agentforce becomes more helpful and accurate as they process more data. However, much of that data often includes sensitive information, such as names, Social Security numbers, credit card details, medical records, or sales revenue.

The first step to strengthening data governance is masking sensitive data throughout the development lifecycle. Data masking involves replacing sensitive information with fictional (yet realistic) data to maintain the integrity and usability of a dataset. Using data masking, developers can work with realistic datasets in sandbox environments - secure, isolated spaces used to test software - without exposing actual customer data.

Data masking is especially important for organizations that need to comply with regulations like the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA).

These laws require strict controls over how data is handled, even in non-production environments. By masking sensitive data with tools like Data Mask, organizations can reduce the risk of accidental leaks and demonstrate their commitment to protecting customer trust, which is especially critical when deploying AI solutions that rely on analyzing customer insights.

Step 2. Centralize compliance and privacy management

Data privacy laws give customers important rights, such as requesting the deletion of their data, restricting how it's processed, and controlling its use.

To remain compliant, businesses need to be able to delete, archive, or obfuscate customer information as required by various regulatory bodies. Compliance also means being transparent about data usage, obtaining proper consent, and keeping detailed data access and processing records.

For AI apps that analyze large volumes of data, meeting compliance and privacy requirements is essential for maintaining customer trust and avoiding regulatory penalties. And as regulations grow more complex, centralize compliance management to prevent missed deadlines, inconsistent data handling, and costly fines.

To adopt AI responsibly, companies need a comprehensive strategy to prioritize privacy while scaling AI solutions like Agentforce. This includes managing Data Subject Requests (DSRs), consent, and transparency in customer data use. By centralizing these processes, businesses can simplify compliance, reduce risks, and stay ahead of evolving regulations - all while building customer trust.

Tools like Salesforce's Privacy Center simplify these tasks, including automated compliance checks and data deletion. For instance, Privacy Center can flag potential violations in AI systems to ensure they align with GDPR, HIPAA, CCPA, and other regulations.

Automation also reduces the manual effort involved in managing compliance, freeing up resources to focus on AI innovation while minimizing costly fines for non-compliance. You can delete records and associated files and attachments in production environments and sandboxes to reduce the amount of storage, and comply with data minimization regulations.

Step 3. Ensure business continuity by backing up AI-critical data

Data reliability will be more critical than ever as AI becomes increasingly integral to operations. Losing key datasets can lead to significant downtime and missed opportunities for valuable insights. To avoid these risks, businesses need the ability to quickly recover from disruptions caused by human error, system failures, or cyber threats.

One way to ensure data integrity is by automatically backing up critical data sets at regular intervals. These backups provide secure storage to protect against accidental deletions, outages, and security breaches. Data should be easy to restore or export when needed, with full visibility into each backup operation so teams can quickly track progress and identify any issues.

Real-time notifications also inform teams about data backups and recovery activities, ensuring no key activities go unnoticed. Access to live insights further helps organizations track trends, evaluate performance, and optimize data management practices.

Salesforce Backup brings all these capabilities together, helping organizations maintain control over their data and supporting the reliability of AI-driven operations. In addition to minimizing setbacks, it supports long-term stability and keeps AI services running smoothly.

Prioritizing data governance for AI (because trust can't be automated)

Building trust in AI projects starts with prioritizing data governance. By focusing on these three actionable steps - masking sensitive data, centralizing compliance efforts, and ensuring business continuity - your organization can navigate the complexities of AI with confidence.

These steps not only reinforce your customers' trust but also support you to innovate responsibly and effectively in an increasingly AI-driven world. And with the right tools, your road to effective data governance for AI is on the right track.

Learn more about Salesforce Cloud Data Security and how it can help support governance strategies.

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Whitney Blaine is a product marketing leader at Salesforce. She has extensive experience marketing technology products at all stages of the product lifecycle.

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