The employee view<\/h2>\n\n\n\n\n- In a survey<\/a> of more than 4,000 employees across sales, service, marketing, and commerce, 73% said they believe generative AI introduces new security risks.<\/li>\n\n\n\n
- 60% who plan to use AI said they don\u2019t know how to do so while keeping sensitive data secure. <\/li>\n\n\n\n
- These risks are of particular concern in highly regulated industries like financial services and health.<\/li>\n<\/ul>\n\n\n\n
What you can do now<\/h2>\n\n\n\n
Partner with companies that build safeguards<\/a> into the fabric of their AI systems. Why is this important?<\/p>\n\n\n\n\n- Large language models (LLMs) contain huge amounts of data, but they are unlike other data repositories like databases or Excel spreadsheets. They don\u2019t have the security safeguards and access controls of large databases, or the cell-lock privacy features of a spreadsheet. <\/li>\n\n\n\n
- \u201cYou\u2019re not going to take all your customers\u2019 \u2026 most sensitive and private and secure information and put it into a large language model that all of your employees are going to have access to,\u201d said Salesforce CEO Marc Benioff<\/a>.<\/li>\n<\/ul>\n\n\n\n\n\t\n\n\t\t\n\t\t\t
The State of IT<\/h2>\n\t\t\t\t\t\t\t
A new survey of 4,000 IT leaders reveals the potential, and the challenges, of generative AI. 86% say it will have a prominent role in their org, but 64% are concerned about the ethics of it all. Want to know more?<\/p>\n\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t
\n\t\t\t\t\tDownload the Report<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\n\t\t\n\t\t\t![\"\"](\"https:\/\/www.salesforce.com\/eu\/blog\/wp-content\/uploads\/sites\/14\/2023\/06\/blog-offer-generic-A.jpg\")
\t\t<\/div>\n\t<\/div>\n\n\t\n\t<\/div>\n\n\n\nHow to build AI data privacy into the fabric of your systems<\/h2>\n\n\n\n
Patrick Stokes, EVP, Product and Industries Marketing at Salesforce, explained that with AI Cloud<\/a>, you can leverage the productivity gains of AI without giving away your company\u2019s data. AI data privacy, he said, requires different safeguards: <\/p>\n\n\n\nDynamic grounding<\/strong> steers an LLM\u2019s answers using the correct and the most up-to-date information, \u201cgrounding\u201d the model in factual data and relevant context. This prevents AI hallucinations, or incorrect responses not grounded in fact or reality. <\/p>\n\n\n\nData masking<\/strong> replaces sensitive data with anonymised data to protect private information and comply with privacy requirements. Data masking is particularly useful in ensuring you\u2019ve eliminated all personally identifiable information like names, phone numbers and addresses, when writing AI prompts.<\/p>\n\n\n\nToxicity detection<\/strong> is a method of flagging toxic content such as hate speech and negative stereotypes. It does this by using a machine learning (ML) model to scan and score the answers an LLM provides, ensuring that generations from a model are usable in a business context. <\/p>\n\n\n\nZero retention<\/strong>\u00a0means that no customer data is stored outside of Salesforce. Generative AI prompts and outputs are never stored in the LLM and are not learned by the LLM. They simply disappear.<\/p>\n\n\n\nAuditing<\/strong>\u00a0continually evaluates systems to make sure they are working as expected, without bias, with high-quality data, and in line with regulatory and organisational frameworks. Auditing also helps organisations meet compliance needs by logging the prompts, data used, outputs, and end-user modifications in a secure audit trail.\u00a0<\/p>\n\n\n\nSecure data retrieval<\/strong> is how Einstein GPT<\/a> \u2013 the world\u2019s first generative AI for customer relationship management (CRM) \u2013 pulls data from Salesforce, including Data Cloud<\/a>, as part of the dynamic grounding process. This lets you bring in the data you need to build contextual prompts. In every interaction, governance policies and permissions are enforced to ensure only those with clearance have access to the data.<\/p>\n\n\n\nJulie Sweet, CEO of Accenture, said risks are mitigated when privacy and security safeguards like these are built right into the technology<\/a>.<\/p>\n\n\n\nTrust in AI data privacy starts with good governance<\/h2>\n\n\n\n
All the technology in the world isn\u2019t enough to ensure transparent, responsible, safe AI. It also requires good governance, starting at the top, and human oversight of AI systems. <\/p>\n\n\n\n
A KPMG report<\/a> found that 75% of respondents would be more willing to trust AI systems when assurance mechanisms are in place to support ethical and responsible use. <\/p>\n\n\n\nAccording to the report, \u201cThese mechanisms include monitoring system accuracy and reliability, using an AI code of conduct, oversight by an independent AI ethical review board, adhering to standards for explainable AI and transparent AI, and an AI ethics certification to establish governance principles.\u201d <\/p>\n\n\n\n
At Accenture, the audit committee of its board of directors oversees an AI compliance program for the company\u2019s 55,000 employees. <\/p>\n\n\n\n
\u201cIf you are not able to call someone in your company, and have them tell you where AI is being used, what the risks are, how they\u2019re being mitigated, and who is accountable, you do not yet have responsible AI,\u201d said Sweet. <\/p>\n\n\n\n
Trusted AI is especially crucial in regulated industries<\/h2>\n\n\n\n
Leaders in highly regulated industries like financial services and health are cautious about how to implement AI without sacrificing customer trust and safety. To ensure compliance with regulations, these industries must explore use cases that don\u2019t put data privacy and security at risk.<\/p>\n\n\n\n
\n- For banking, this might include using automation<\/a> to make routine tasks and processes more efficient, such as transaction disputes or using AI to power more intelligent chatbots that personalise customer interactions and improve self-services.<\/li>\n\n\n\n
- For health providers, AI can help them segment patient populations more efficiently<\/a> so they can send more personalised, timely communications like tips for diabetes patients to lower blood sugar when their glucose levels spike.<\/li>\n<\/ul>\n\n\n\n
Benioff said recently that generative AI could be \u201cthe most important technology of any lifetime.\u201d For sure, generative AI could make e-commerce, which forever changed consumer behaviour, industries, and business models, look like small potatoes. McKinsey recently estimated<\/a> that AI\u2019s impact on productivity could add trillions, annually, to the global economy. But we must have a high degree of trust in these systems for that to happen. <\/p>\n\n\n\n\u201cTrustworthy AI matters,\u201d KPMG wrote in its survey. \u201cIf people perceive AI systems as trustworthy and are willing to trust them, then they are more likely to accept them.\u201d<\/p>\n\n\n\n
What you can do now<\/h2>\n\n\n\n
Partner with companies that build safeguards<\/a> into the fabric of their AI systems. Why is this important?<\/p>\n\n\n\n A new survey of 4,000 IT leaders reveals the potential, and the challenges, of generative AI. 86% say it will have a prominent role in their org, but 64% are concerned about the ethics of it all. Want to know more?<\/p>\n\t\t\t\n\t\t\t\n\t\t\t\t\t\t\t Patrick Stokes, EVP, Product and Industries Marketing at Salesforce, explained that with AI Cloud<\/a>, you can leverage the productivity gains of AI without giving away your company\u2019s data. AI data privacy, he said, requires different safeguards: <\/p>\n\n\n\n Dynamic grounding<\/strong> steers an LLM\u2019s answers using the correct and the most up-to-date information, \u201cgrounding\u201d the model in factual data and relevant context. This prevents AI hallucinations, or incorrect responses not grounded in fact or reality. <\/p>\n\n\n\n Data masking<\/strong> replaces sensitive data with anonymised data to protect private information and comply with privacy requirements. Data masking is particularly useful in ensuring you\u2019ve eliminated all personally identifiable information like names, phone numbers and addresses, when writing AI prompts.<\/p>\n\n\n\n Toxicity detection<\/strong> is a method of flagging toxic content such as hate speech and negative stereotypes. It does this by using a machine learning (ML) model to scan and score the answers an LLM provides, ensuring that generations from a model are usable in a business context. <\/p>\n\n\n\n Zero retention<\/strong>\u00a0means that no customer data is stored outside of Salesforce. Generative AI prompts and outputs are never stored in the LLM and are not learned by the LLM. They simply disappear.<\/p>\n\n\n\n Auditing<\/strong>\u00a0continually evaluates systems to make sure they are working as expected, without bias, with high-quality data, and in line with regulatory and organisational frameworks. Auditing also helps organisations meet compliance needs by logging the prompts, data used, outputs, and end-user modifications in a secure audit trail.\u00a0<\/p>\n\n\n\n Secure data retrieval<\/strong> is how Einstein GPT<\/a> \u2013 the world\u2019s first generative AI for customer relationship management (CRM) \u2013 pulls data from Salesforce, including Data Cloud<\/a>, as part of the dynamic grounding process. This lets you bring in the data you need to build contextual prompts. In every interaction, governance policies and permissions are enforced to ensure only those with clearance have access to the data.<\/p>\n\n\n\n Julie Sweet, CEO of Accenture, said risks are mitigated when privacy and security safeguards like these are built right into the technology<\/a>.<\/p>\n\n\n\n All the technology in the world isn\u2019t enough to ensure transparent, responsible, safe AI. It also requires good governance, starting at the top, and human oversight of AI systems. <\/p>\n\n\n\n A KPMG report<\/a> found that 75% of respondents would be more willing to trust AI systems when assurance mechanisms are in place to support ethical and responsible use. <\/p>\n\n\n\n According to the report, \u201cThese mechanisms include monitoring system accuracy and reliability, using an AI code of conduct, oversight by an independent AI ethical review board, adhering to standards for explainable AI and transparent AI, and an AI ethics certification to establish governance principles.\u201d <\/p>\n\n\n\n At Accenture, the audit committee of its board of directors oversees an AI compliance program for the company\u2019s 55,000 employees. <\/p>\n\n\n\n \u201cIf you are not able to call someone in your company, and have them tell you where AI is being used, what the risks are, how they\u2019re being mitigated, and who is accountable, you do not yet have responsible AI,\u201d said Sweet. <\/p>\n\n\n\n Leaders in highly regulated industries like financial services and health are cautious about how to implement AI without sacrificing customer trust and safety. To ensure compliance with regulations, these industries must explore use cases that don\u2019t put data privacy and security at risk.<\/p>\n\n\n\n Benioff said recently that generative AI could be \u201cthe most important technology of any lifetime.\u201d For sure, generative AI could make e-commerce, which forever changed consumer behaviour, industries, and business models, look like small potatoes. McKinsey recently estimated<\/a> that AI\u2019s impact on productivity could add trillions, annually, to the global economy. But we must have a high degree of trust in these systems for that to happen. <\/p>\n\n\n\n \u201cTrustworthy AI matters,\u201d KPMG wrote in its survey. \u201cIf people perceive AI systems as trustworthy and are willing to trust them, then they are more likely to accept them.\u201d<\/p>\n\n\n\n\n
The State of IT<\/h2>\n\t\t\t\t\t\t\t
\t\t<\/div>\n\t<\/div>\n\n\t\n\t<\/div>\n\n\n\nHow to build AI data privacy into the fabric of your systems<\/h2>\n\n\n\n
Trust in AI data privacy starts with good governance<\/h2>\n\n\n\n
Trusted AI is especially crucial in regulated industries<\/h2>\n\n\n\n
\n
![\"blog-offer-trailhead\"](\"https:\/\/www.salesforce.com\/eu\/blog\/wp-content\/uploads\/sites\/14\/2022\/11\/blog-offer-trailhead.jpg\")
\t\t<\/div>\n\t<\/div>\n\n\t\n\t<\/div>\n","protected":false},"excerpt":{"rendered":"