Artificial intelligence (AI) and generative AI technologies like Copilot for Microsoft 365 and ChatGPT have been dominating conversations in the business world. While the initial buzz may have quietened since the end of last year, the focus on how AI can add real value to business operations has only grown stronger. From improving efficiency to unlocking new creative possibilities, AI holds enormous potential – but one significant challenge continues to loom large over these innovations: data security.
The potential risks associated with AI – particularly around data security and compliance – remain a top concern for many organisations. According to a recent survey by portal26, these are the top AI-related issues currently keeping CIOs up at night:
- Shadow AI: The unauthorised use of generative AI tools, often referred to as “Shadow AI”, is a major concern, with 58% of organisations worried about employees using AI tools that haven’t been approved by their IT departments. This lack of oversight increases the risk of data leaks and compliance violations.
- Data Privacy and Security: Data security is a critical issue, with 60% of organisations expressing concerns. This is especially pressing for large organisations, where 93% reported heightened anxiety about the potential for data breaches due to the wide use of AI. Data privacy concerns are also significant, with 56% of respondents highlighting this as a key issue.
- Governance and Compliance: AI governance remains a challenge, with 63% of businesses acknowledging the need for better oversight to prevent misuse and ensure compliance with both internal and external regulations.
- Intellectual Property (IP) Protection: Protecting intellectual property in the AI era is another major concern, with 62% of companies worried about the potential for AI to compromise sensitive information or create vulnerabilities in their IP strategies.
- Bias in AI Training: Over half (55%) of the respondents are concerned about the potential for bias in AI training to lead to unethical outcomes and damage to the organisation’s reputation.
- Employee Training and Use of AI: Despite the rapid adoption of AI, many organisations have been lax in training their employees, with 58% of companies providing less than five hours of annual education on AI-related issues. Small wonder 63% are concerned about how employees use AI prompts, fearing unintended data exposure or misuse.
The proactive approach to AI-readiness
Microsoft has responded to AI concerns with a whitepaper exploring a proactive approach to secure AI adoption. In it, they outline four critical steps to prepare your data for AI.
Here’s a quick summary of what those steps entail.
- Know Your Data
Understanding what data you have is the first step towards protecting it. Microsoft recommends using data discovery tools like Microsoft Purview Information Protection to locate sensitive data and identify risky activities. Sensitive data can then be classified and labelled to prevent accidental exposure once AI is deployed.
- Govern Your Data
Data governance is a key component in ensuring AI is used compliantly. Microsoft recommends reviewing and adjusting permissions across your organisation before deploying AI – particularly in shared environments like SharePoint. Deleting old or obsolete data, remediating open permissions and applying organisation-wide content management policies makes it much easier to minimise the risk of unauthorised data access and ensure your AI tools are only able to interact with data that has been properly secured.
- Protect Your Data
Protecting sensitive data is crucial before AI deployment. AI tools like Copilot for Microsoft 365 are designed to respect the sensitivity labels assigned to data, ensuring that any AI-generated outputs inherit the security controls of the original data. This step is vital to maintaining control over how sensitive information is used and shared within your organisation.
- Prevent Data Loss
Microsoft stresses the importance of implementing robust Data Loss Prevention (DLP) measures to prevent unauthorised sharing or exfiltration of sensitive data through AI applications. Extending DLP capabilities across platforms ensures that all channels of data flow are secure, reducing the risk of security breaches and regulatory penalties.
Overcoming the challenges
Implementing these security measures may seem daunting, especially when it requires business-wide decision-making about sensitive information, risk attitudes, and compliance requirements. However, with the right approach and expertise, these challenges can be managed effectively.
Our multi-disciplinary team at Cloud Essentials is experienced in engaging with both business and technical stakeholders to facilitate and drive data security projects forward. By taking a risk-based approach, we can help you develop a roadmap of activities tailored to your organisation’s needs, ensuring you not only meet your security and compliance obligations but also unlock the full potential of AI.
If you’re exploring Copilot adoption and need to get your data security in shape, now is the time to act. Talk to us about how we can assist in setting up a robust framework that protects your data and unleashes AI’s transformative power in your business.
