How to Mitigate AI Risks
Artificial Intelligence (AI) has ushered in a new era of business innovation. By 2026, more than 80% of enterprises will have used generative artificial intelligence (GenAI) application programming interfaces (APIs) or models, and/or deployed GenAI-enabled applications in production environments, up from less than 5% in 2023, according to Gartner, Inc. Beyond productivity, the economic potential of AI is also significant; a report by McKinsey estimates that AI could generate the equivalent of $4.4 trillion annually to the global economy by 2030.
Microsoft Copilot, for example, has a significant impact across various industries aiming for productivity improvement. In healthcare, Copilot can efficiently organize and analyze patient data, aiding in quicker diagnosis and personalized treatment plans. It can also handle scheduling appointments, sending patient reminders, and optimizing healthcare providers’ schedules. Additionally, Copilot can be used for in-depth medical research, analyzing vast datasets to uncover trends and insights in patient care and treatment.
GenAI tools are transforming the way we work, making tasks easier and more efficient. AI is making the digital workplace smarter and more productive. But there is a catch. While AI brings many benefits, it also comes with risks.
Identifying the Risks of Generative AI
"In the 2023 Gartner Microsoft 365 Survey, 60% of respondents stated that oversharing, data loss, and content sprawl were among the biggest risks to their Microsoft 365 deployment."
- Data Oversharing: The main question all organizations using generative AI must ask themselves is whether their data remains safe from inappropriate disclosure and use when employing GenAI tools. It is common practice to use tools like Microsoft’s Copilot by providing some data to the tool and waiting for an answer. But let’s take a step back. How sure are we about the safety of the data we provide, and where do we draw the line when sharing? Data oversharing is the most common risk GenAI users face: There is a chance that we are oversharing data that external parties or tools shouldn't have access to. During the GTP Client Webinar, entitled Improve Information Governance to Manage Microsoft 365 Copilot Risks, 72% of the attendees answered that oversharing/exposing sensitive information to employees is the biggest risk with deploying M365 Copilot.
- Data Sprawl: This refers to the uncontrolled spread of data across various systems. In a digital workplace, this can happen when multiple AI tools are used to create, modify, and store data without a centralized management strategy. As a result, data can become scattered, making it difficult to track, manage, and secure. Generative AI tools like Copilot create a large volume of data by generating documents, emails, code, and other digital assets. Without proper governance, this data can end up in various locations, including cloud storage, local drives, email attachments, and collaboration platforms. Apart from the difficulty in data management that it brings, there are also two more crucial concerns to keep in mind. Spread data is harder to protect. It increases the risk of data breaches, as sensitive information may reside in less secure locations. In addition, ensuring compliance with data protection regulations becomes more challenging when data is not centralized. This can result in non-compliance penalties and legal issues.
- Inaccuracy/AI Misinformation: AI systems are only as good as the data they are trained on, and if that data is flawed, biased, or incomplete, the AI's outputs can be misleading or incorrect. During the GTP Client Webinar, entitled Improve Information Governance to Manage Microsoft 365 Copilot Risks, 35% of the attendees indicated that inaccurate information will eventually lead to poor decision-making, which is a constant challenge when deploying M365 Copilot.AI Hallucinations: GenAI could get things wrong and produce outputs that are false or misleading. These hallucinations occur when the AI generates information that appears credible but is actually inaccurate or completely fabricated. This poses significant risks, especially in situations where accuracy is paramount. It’s essential to implement robust checks and validations to ensure the reliability of AI outputs and to mitigate the impact of these hallucinations on decision-making processes.
Summarizing the above, organizations today face a new challenge related to AI safety. Following this approach, the goal is to achieve a comprehensive AI governance and data protection solution.
In the end, it all comes down to this: Ensuring the right people have access to the right data at the right time, seamlessly and securely managing both internal and external sharing needs. This not only simplifies access but also provides peace of mind and assurance, from employees to C-level executives, that data usage is secure and automated, and productivity is accelerated.
Artificial Intelligence (AI) has ushered in a new era of business innovation. By 2026, more than 80% of enterprises will have used generative artificial intelligence (GenAI) application programming interfaces (APIs) or models, and/or deployed GenAI-enabled applications in production environments, up from less than 5% in 2023, according to Gartner, Inc. Beyond productivity, the economic potential of AI is also significant; a report by McKinsey estimates that AI could generate the equivalent of $4.4 trillion annually to the global economy by 2030.
Microsoft Copilot, for example, has a significant impact across various industries aiming for productivity improvement. In healthcare, Copilot can efficiently organize and analyze patient data, aiding in quicker diagnosis and personalized treatment plans. It can also handle scheduling appointments, sending patient reminders, and optimizing healthcare providers’ schedules. Additionally, Copilot can be used for in-depth medical research, analyzing vast datasets to uncover trends and insights in patient care and treatment.
GenAI tools are transforming the way we work, making tasks easier and more efficient. AI is making the digital workplace smarter and more productive. But there is a catch. While AI brings many benefits, it also comes with risks.
Identifying the Risks of Generative AI
"In the 2023 Gartner Microsoft 365 Survey, 60% of respondents stated that oversharing, data loss, and content sprawl were among the biggest risks to their Microsoft 365 deployment."
- Data Oversharing: The main question all organizations using generative AI must ask themselves is whether their data remains safe from inappropriate disclosure and use when employing GenAI tools. It is common practice to use tools like Microsoft’s Copilot by providing some data to the tool and waiting for an answer. But let’s take a step back. How sure are we about the safety of the data we provide, and where do we draw the line when sharing? Data oversharing is the most common risk GenAI users face: There is a chance that we are oversharing data that external parties or tools shouldn't have access to. During the GTP Client Webinar, entitled Improve Information Governance to Manage Microsoft 365 Copilot Risks, 72% of the attendees answered that oversharing/exposing sensitive information to employees is the biggest risk with deploying M365 Copilot.
- Data Sprawl: This refers to the uncontrolled spread of data across various systems. In a digital workplace, this can happen when multiple AI tools are used to create, modify, and store data without a centralized management strategy. As a result, data can become scattered, making it difficult to track, manage, and secure. Generative AI tools like Copilot create a large volume of data by generating documents, emails, code, and other digital assets. Without proper governance, this data can end up in various locations, including cloud storage, local drives, email attachments, and collaboration platforms. Apart from the difficulty in data management that it brings, there are also two more crucial concerns to keep in mind. Spread data is harder to protect. It increases the risk of data breaches, as sensitive information may reside in less secure locations. In addition, ensuring compliance with data protection regulations becomes more challenging when data is not centralized. This can result in non-compliance penalties and legal issues.
- Inaccuracy/AI Misinformation: AI systems are only as good as the data they are trained on, and if that data is flawed, biased, or incomplete, the AI's outputs can be misleading or incorrect. During the GTP Client Webinar, entitled Improve Information Governance to Manage Microsoft 365 Copilot Risks, 35% of the attendees indicated that inaccurate information will eventually lead to poor decision-making, which is a constant challenge when deploying M365 Copilot.AI Hallucinations: GenAI could get things wrong and produce outputs that are false or misleading. These hallucinations occur when the AI generates information that appears credible but is actually inaccurate or completely fabricated. This poses significant risks, especially in situations where accuracy is paramount. It’s essential to implement robust checks and validations to ensure the reliability of AI outputs and to mitigate the impact of these hallucinations on decision-making processes.
Summarizing the above, organizations today face a new challenge related to AI safety. Following this approach, the goal is to achieve a comprehensive AI governance and data protection solution.
In the end, it all comes down to this: Ensuring the right people have access to the right data at the right time, seamlessly and securely managing both internal and external sharing needs. This not only simplifies access but also provides peace of mind and assurance, from employees to C-level executives, that data usage is secure and automated, and productivity is accelerated.
