Secure AI Improves Efficiency and Customer Trust

Organizations are quick to adopt artificial intelligence (AI) for their automation, decision-making, customer support, and growth strategy needs. However, increased AI workloads come with important security and privacy considerations. Here’s how businesses can benefit from AI in a secure way with the Einstein 1 Platform. Its built-in Einstein Trust Layer addresses security concerns by ensuring robust data security, privacy compliance, transparent AI operations, strict access controls, and comprehensive audit trails.

Why secure AI workflows for business matters

AI represents advanced technology that empowers machines to mimic human-like behaviors, such as learning and problem-solving processes. AI is a field of research which includes Large Language Models (LLMs), complex algorithms, and large datasets which leverage machine learning.

While the concept of AI has been around for decades, the recent acceleration and adoption of growing amounts of data has grown its computational power exponentially. A recent study conducted by Salesforce reports that over two-thirds (68%) of Analytics and IT teams anticipate data volumes to increase over the next 12 months, making securing data fed into language model prompts even more challenging.

What is AI for business?

In a corporate setting, AI systems depend on trusted and accurate input data to generate recommendations for decision-making. When dealing with customer data, it’s important to understand the benefits of two types of AI models when it comes to applying them to business needs: 

1.  Predictive AI uses advanced algorithms and pattern recognition to analyze past data and forecast trends in a given scenario. Predictive AI has many business use cases that improve customer experiences and decision-making. Some examples include forecasting demands for sales teams, triaging support through chatbots for service teams, providing shopping recommendations for commerce conversions, and so on. 
2. Generative AI (Gen AI) creates new content on any given topic from an input prompt. It can generate content, images, and even software code. The business use cases extend across many types of domains–from generating call summaries for sales teams, messaging scripts for service agents, to personalizing marketing material for targeted campaigns, and more!

Leverage AI for business efficiency

Many businesses are using AI to gain a competitive advantage over their competitors, by improving efficiency, innovation, and customer experience. There are many use cases for applying AI, including but not limited to:

• Improve time management: AI improves time management by streamlining processes and automating time-consuming tasks. A few examples of such benefits are automated scheduling, prioritization, and task automation.
• Make smarter decisions: AI enhances decision making by quickly analyzing large datasets and revealing insights or providing predictions.
• Instant support help: AI-powered chatbots offer 24/7 support and personalized recommendations, enhancing customer satisfaction through response remediation.
• Increase revenue: AI can boost revenue by helping marketing teams optimize campaigns with tailored growth strategies, sales teams close deals faster by identifying best next steps, and IT teams deploy faster with performance and operational insights.

4 strategies for secure AI systems

It’s crucial to consider security and privacy when using sensitive data in AI datasets. Inputting inaccurate data can create security risks which include data privacy violations, data security breaches, and algorithmic biases.

With LLMs, the model also learns the input data, which becomes part of the output via the continuous learning algorithm. In other words, the information provided is directly related to the generated output. The model uses patterns it has learned from its training data to generate a response that’s contextually relevant to the input. So, it’s important to manage access controls of these models by creating measures that maintain a trusted AI system.

Here are some tips to begin securing AI systems:

• Data security: AI systems should have robust security measures in place to prevent data breaches. This includes encryption of data at rest and in transit, secure APIs, secure access controls, and restricting data retention beyond its intended use.
• Data privacy: AI systems process vast amounts of data that may contain personally identifiable information (PII). Ensure that you handle this data securely, considering all applicable data privacy laws and regulations. De-personalizing and masking data where possible is an important method to prevent leaking sensitive user information.
• Algorithmic bias: Train AI systems on diverse datasets to avoid biases. According to Salesforce’s recent study, 63% of customers surveyed are concerned about bias in AI. Take into account the quality of the data, size of a dataset, and the relevance of data for the use case. It’s also important to regularly audit AI systems for bias and fairness.
• Transparency: AI systems should be transparent and provide explanations for their predictions and decisions. Transparency starts with acknowledging that the system output uses AI. Therefore, it’s important to maintain documentation on how AI systems are used. This helps build trust with users and is increasingly becoming a regulatory requirement.

Einstein 1 Platform handles secure workflows

The Einstein 1 Platform is a suite of AI-enabled products that bring the power of AI to every Salesforce user handling large datasets within their organization. Einstein 1 Platform brings intelligence to workflows using both predictive and generative capabilities to drive the most impact. The platform includes a feature like Einstein Co-Pilot, which assists users by automating tasks and providing recommendations, thereby enhancing productivity and decision-making.

Securely grounded prompts

Using the power of AI can involve numerous risks. The Einstein Trust Layer is designed to detect and mitigate common security risks when using this powerful tool. The Trust Layer ensures that responsible AI practices are followed, user data is handled with the utmost care, and the integrity of the AI system is maintained.

• Data security: The Einstein Trust Layer ensures that all data processed by Einstein is protected by Salesforce’s robust security measures. This includes encryption at rest and in transit. Think of this as your intermediary between your data stored in Salesforce or your own data lake.
• Privacy by design: Einstein Trust Layer ensures that the AI engine respects user data and supports compliance with global privacy laws and regulations. It’s designed to handle data in a way that respects user privacy and consent.
• Access control and permissions: Einstein Trust Layer ensures that Einstein inherits the robust access control mechanisms of the Salesforce platform. This prevents unauthorized access to sensitive data.
• Audit trails: Einstein Trust Layer ensures that all actions taken by Einstein are recorded in Salesforce’s comprehensive audit trails. This provides visibility and accountability, helping to detect and respond to any unusual activity.

Overall, the adoption of AI in business processes, while offering significant advantages, brings with it certain security risks. The Einstein 1 Platform, with its well architected Einstein Trust Layer, addresses these concerns effectively and ensures data security, respects privacy, provides transparent AI operations, and maintains robust access controls and audit trails.

Learn more:

• Resources: Einstein Trust Layer and Einstein 1 Platform
• Video: Protect Data Security and Privacy with Einstein Trust Layer
• Help: About Einstein Generative AI
• Trailhead: Drive Productivity with Einstein AI
• Salesforce Report: Six Strategies to Prepare for Trusted Generative AI
• Forrester Report: Leverage Generative AI Without Compromising Security