Paving a Secure Path for AI – 4 Key Practices to Protect Your Data

Paving a Secure Path for AI - 4 Key Practices to Protect Your Data

Adopt a secure AI strategy to keep a pulse on compliance and safeguard customer trust.

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Imagine a future where AI agents handle routine tasks, allowing your team to focus on more strategic priorities - such as strengthening customer relationships, driving innovation, and making smarter decisions. But this isn't a vision; it's already a reality.

And while AI and custom apps have the potential to boost efficiency and unlock new opportunities, it's important to be aware of new risks. Sensitive data, such as personal and financial information, is often at the heart of AI-driven solutions, making protection and compliance non-negotiable.

To realize the full potential of AI while protecting what matters most, businesses need to adopt strong AI security strategies that prioritize customer trust, ensure regulatory compliance, and strengthen overall security. Naturally, the first step in building such a strategy is understanding the common obstacles in the way of secure, trusted AI implementation.

Roadblocks to secure AI implementation

For businesses in finance, healthcare, and other regulated sectors, AI offers transformative capabilities. A finance company might use low-code tools to build an AI agent that analyzes credit scores or detects fraud, relying on sensitive transaction data. Similarly, a healthcare provider could create an AI agent to improve patient care by analyzing medical records and treatment plans.

However, AI agents like these shouldn't handle sensitive information without additional security and compliance safeguards. These protections are essential to shielding businesses from data protection risks and regulatory non-compliance. Here are some of the most common hurdles businesses face:

• Data breach risks: AI systems often handle vast amounts of sensitive data - think personal details, financial records, and medical data - making them attractive targets for cyberattacks. A breach could expose this information if proper safeguards are lacking, leading to serious consequences such as financial loss, reputational damage, and legal penalties.
• Regulatory compliance: Beyond cyber threats, businesses must also navigate the complex regulatory landscape that governs AI. Laws like the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA) set strict rules for how data is stored, processed, and accessed. AI systems that handle personal data need to comply with these rules, and failure can result in hefty fines and operational disruptions. These laws differ across regions and industries and change frequently, which turns compliance into a moving target.
• Data quality and bias: AI relies on high-quality, accurate data to make reliable predictions. Poor-quality or biased data can introduce security risks - for example, giving unauthorized access due to misclassified user permissions. Ensuring clean, relevant data throughout the AI lifecycle remains a constant hurdle for businesses.
• Data access management: Without strict controls on who can access sensitive datasets and models, there's an increased risk of unauthorized users gaining entry, which could compromise the entire AI system. Effective access management is essential for protecting data integrity and overall system security.

Secure AI innovation with 4 must-have security capabilities

Amid these challenges, organizations are looking for security tools that allow them to protect data while innovating with AI. The right set of advanced security tools can offer both protection and compliance, allowing teams to more effectively monitor, safeguard, and manage critical data.

The best tools for secure AI innovation are the ones that allow you to strike a balance between security and new technology, as Salesforce Shield does with its suite of security tools (including Event Monitoring, Field Audit Trail, Platform Encryption, and Data Detect).

And when combined with building Agentforce agents and AI apps, this suite supports secure, compliant, and scalable AI-driven solutions. For businesses seeking to harness the full potential of AI while safeguarding sensitive data, building on Salesforce delivers both agility and peace of mind.

Here are four ways to help teams overcome the barriers to secure AI implementation.

1. Detect and prevent potential data breaches before they escalate

Support your organization in tackling potential threats by catching data breaches before they can cause harm. By using monitoring capabilities, you can proactively prevent data misuse and protect your AI-driven systems from vulnerabilities.

AI systems handle large amounts of sensitive information, and monitoring helps catch suspicious activities before they turn into serious breaches. One way to strengthen this monitoring process is through tools that offer visibility into user behavior and system activity. This helps you maintain a secure environment for your AI workflows.

Tools like Event Monitoring let you track user behavior and system activity, giving you full visibility into how data is used in your AI workflows.

2. Ensure compliance and maintain a traceable history of all data changes

Data integrity is essential in AI-driven environments, particularly when dealing with strict regulations like SOX. Organizations need to safeguard their AI workflows against unauthorized changes to maintain security and meet compliance standards. This oversight is especially important in regulated industries, where handling sensitive data correctly and ensuring every record change is fully traceable are non-negotiable.

But it's not just about compliance. Keeping a detailed record of data changes can also boost the value of AI workflows. It helps maintain long-term data accuracy and improves the reliability of AI models. By building a clear history of data modifications, organizations can ensure their AI processes remain secure, accurate, and consistently high-performing.

With tools like Field Audit Trail, you can easily maintain a detailed history of data changes, keeping records for as long as needed or until they are deleted.

3. Protect sensitive data used in AI models and workflows to prevent unauthorized access

AI applications often rely on sensitive data, such as personal customer information and financial records. Protecting this data is critical - for maintaining customer trust, but also for meeting regulatory requirements. Without strong security measures in place, organizations expose themselves to data breaches that can lead to serious reputational and financial damage.

To keep sensitive information safe in AI models, organizations should ensure data is encrypted to prevent unauthorized access. It's also important to have the flexibility to manage encryption keys effectively, allowing for better control and compliance with data security standards.

Solutions like Platform Encryption can play a key role in providing these protections to maintain data integrity while using AI.

4. Quickly identify sensitive data to apply proper security controls and avoid compliance risks

Effectively managing sensitive data in AI systems starts with understanding where it's stored and how it's used. Organizations need to identify and classify sensitive information - like credit card numbers or Social Security data - across their environments to make sure the right security and compliance measures are in place. This becomes even more critical in AI workflows, where working with large datasets can sometimes result in sensitive data being overlooked.

By focusing on identifying sensitive information upfront, organizations can proactively apply the necessary security controls to protect their data and prevent vulnerabilities. This not only helps maintain compliance with regulations but also strengthens customer trust.

Tools like Data Detect assist in scanning and classifying sensitive data, which can be invaluable as they allow organizations to strengthen their security posture and better protect their data landscape.

Prioritize secure AI for smarter innovation

As AI continues to transform industries and drive innovation, strong data security has never been more important. Organizations need to protect sensitive data, ensure compliance, and secure their AI workflows. This means using real-time monitoring, keeping detailed audit trails, encrypting data at rest, and proactively classifying information to protect both customer data and AI models.

But data security isn't just about meeting compliance requirements - it's about building trust and staying ahead of potential threats. With the right security measures in place, organizations can confidently develop Agentforce agents and AI apps, knowing customer data is safe. By adopting a proactive security strategy within Salesforce's AI ecosystem today, you can ensure your data and AI processes remain secure, compliant, and future-ready.

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Cynthia Huang is a Product Marketing Manager for Salesforce Shield.

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