7 Ways the Einstein Trust Layer Supports Enterprise AI Security

It’s safe to say that Artificial Intelligence (AI), particularly generative AI, is the next big technological revolution, similar to the launch of the internet and mobile. However, emerging technology can come with limitations and various problems, such as concerns about information sourcing, privacy and security, bias, and inequity, and accuracy. As businesses board the AI train and to achieve greater productivity and efficiency, it is critical that they give trust high attention. As implementers (and big fans) of AI within Salesforce since the inception of Einstein, ensuring our customers safety and security is top priority. This is why we were thrilled to adopt Salesforce’s Trust Layer alongside the release of Einstein 1 and new AI capabilities that are changing the game in so many ways for Salesforce customers across the board. Let’s dig into why Salesforce enterprise AI security is so important and how the Einstein Trust Layer is fostering confidence while ensuring data security, business integrity and personal safety.

The Risks of GPT

Even if they haven’t started knowingly using it yet, most everyone is starting to embrace AI technology, but many still have concerns. There are some risks that are preventing us from fully trusting AI. The GPT series, developed by OpenAI, represents a significant leap in natural language processing capabilities. However, its impressive abilities come with associated risks. Here’s a breakdown of these risks:

1. Hallucinations

GPT models, due to their generative nature, can sometimes produce answers that sound plausible but are not factual or are purely speculative. These “hallucinations” result from the model trying to generate a coherent response based on its training data, even when it might not have a factual answer. It can be problematic in decision-making or when factual accuracy is critical. Users could be misled by incorrect information. Therefore, users must verify critical information from trusted sources and not rely solely on GPT’s responses.

2. Toxicity

Sometimes, GPT models can generate harmful, offensive, or inappropriate content. This is because the models have been trained on vast areas of the internet, including places where toxic content exists. Such outputs could promote harmful ideologies or offend users. Therefore, user feedback is crucial for refining and improving the model over time.

3. Privacy

Users might be concerned that their queries or data input into GPT get stored and could be used later or be exposed to third parties. Breaches or misuse of data can lead to privacy violations, loss of trust, and potential legal ramifications. Therefore, users should be cautious about sharing sensitive personal information when interacting with such models.

4. Bias

GPT models, trained on a large portion of the internet, can contain inherent biases based on the data available. They might provide responses that reflect societal biases or stereotypes. Reinforcing existing prejudices can perpetuate harmful stereotypes and lead to skewed decision-making. Therefore, users should be aware of potential biases and consider them when interpreting responses.

5. Data Governance

Ensuring that sensitive or proprietary data is not inadvertently included in the model’s training set — potential exposure of confidential information, intellectual property, or data should remain private. However, users should be cautious and treat the model as only partially secure for extremely sensitive queries.

Open AI technologies are out of Salesforce’s control, so we must be careful about what we feed them. That’s why Salesforce has developed the Einstein Trust Layer, which will help customers trust AI in their Salesforce products. So, what is the Einstein Trust Layer and why is it essential for customers? Let’s have a look.

The Einstein Trust Layer

The Einstein Trust Layer is the backbone of Salesforce’s enterprise AI security measures. It functions as a “layer” between the GPT servers and your Salesforce organization’s data. Your data will be anonymized and muted by Salesforce before being sent to the AI server. The Trust Layer allows you to swap what technology is leveraged on the other side of the layer from your Salesforce organization. Therefore, you can also create and train your own AI model using this method.

7 Ways the Einstein Trust Layer Supports Enterprise AI Security

The Einstein Trust Layer is an essential aspect of Salesforce AI that addresses the risks of the incredibly advanced generative AI system. In addition to automated safety and security measures, it brings a human in the loop prior to final output. Let’s look at several features that contribute to this Trust Layer:

1. Prompt Templates

Develop pre-built templates that help accelerate daily repetitive tasks that your users will face. This supports further automation that allows you to chain together generative responses with flows and other automations. These templates provide a structure that makes user interaction more intuitive and reliable.

2. Secure Data Retrieval

Ensuring that the data used and accessed through the system is retrieved securely is crucial. It prevents unauthorized access, breaches, and potential misuse of sensitive information, and protects both the system’s integrity and the user’s privacy.

3. Dynamic Grounding

To “ground” a generative AI response is to dictate the context of the information used when generating a response. Dynamic Grounding allows you to specify your own company and brand context using your Salesforce data (such as Knowledge Articles). This will help the generated responses be more engaging and less generic.

4. Data Masking

Personal or confidential information mustn’t be inadvertently revealed when dealing with sensitive data. Data masking ensures that only non-sensitive data is shown or processed, thus safeguarding user privacy.

5. Prompt Defense

The system can detect and avoid manipulation attempts. Users, especially those with malicious intent, might try to “game” the system to produce biased, inappropriate, or misleading outputs. Prompt defense mechanisms detect and prevent such manipulations.

6. Toxicity Detection

Detecting and filtering out harmful or inappropriate content is crucial, especially on platforms that young or vulnerable users can access. It ensures that the system doesn’t inadvertently generate or promote potentially damaging content.

7. Audit Trail

An audit trail keeps a record of all interactions with the system. This is vital for accountability, transparency, and reviewing any incidents or issues. An audit trail can also help refine the design by analyzing its performance over time.

The Trust Layer facilitates a secure, transparent, and reliable interface between humans and technology. When users trust a system, they’re more likely to use it effectively and responsibly. Ensuring these safeguarding elements are in place is essential for the responsible development and deployment of AI systems.

The Path Forward

Salesforce’s efforts towards securing enterprise AI security is giving Salesforce customers the freedom to integrate AI into their Salesforce operations with confidence. The Einstein platform, which powers AI capabilities, exemplifies Salesforce’s commitment to transparent, ethical, and accountable AI. By sharing AI research with the broader community and actively participating in AI ethics and governance discussions, Salesforce is setting a precedent for the industry.

As we stand at the crossroads of an AI-driven future, we believe the collective goal should be to harness its immense potential of AI while protecting individual rights and societal values. The future of AI should be one that we can all trust, believe in and take advantage of.