With the average data breach cost expected to reach $5 million in 2025, organizations have every right to be cautious about integrating AI into their operations. New technology brings new security and data privacy risks, after all.
However, AI is not something to be overlooked. It will transform the very fabric of work—in many organizations it already is. With McKinsey estimating that AI can unlock trillions in value across sectors, companies can’t afford to sit still.
The answer, then, is to move forward strategically—for organizations to look before they leap, and carefully plan for AI integration. But where to start? Here are three things companies must consider as they kick off their AI journey.
Prioritize data governance
AI models thrive when they have access to high-quality, well-managed datasets. However, without a robust data governance framework, even the most sophisticated AI systems can fall short of expectations. Poor data quality, inconsistent policies, and weak security controls can compromise AI performance, leading to inaccurate outputs and undermining broader business objectives.
Take Google, for example. Stronger AI governance would have prevented its chatbot, Bard, from hallucinating the answer to a question on live TV—an error that saw the company’s stock prices plummet in a matter of seconds.
This is why data governance is essential for AI success. Clear policies on data quality, ethical AI use, and regulatory compliance are key to ensuring transparency, reducing bias, and improving the accuracy of AI outputs. When companies have visibility into how data is classified, used, and monitored throughout its lifecycle, they can scale their AI efforts confidently, safe in the knowledge that their data is reliable, accurate and secure.
While AI technology may be new, the foundational principles of data governance have long been established. Here are the steps companies must take:
- Discover, classify, and monitor: Implement real-time data monitoring and classification processes for sensitive data within AI applications. This allows businesses to maintain complete visibility and control, ensuring no data falls through the cracks and that sensitive information stays secure.
- Granular access controls: Take a proactive approach to data security by setting user-specific permissions. Regulate who has access to AI-driven insights and sensitive data, ensuring only those with the right level of clearance can interact with critical information.
- Support security with clear policies: Security is not just about technology; it’s about the processes and policies that govern it. Back your security measures with clear, enforceable policies and regular employee training. This helps ensure that everyone across the organization understands the importance of responsible AI use, the potential risks, and their role in safeguarding data integrity.
- Monitor user behavior: Regularly track and analyze AI interactions to identify potential misuse or policy violations. Creating accountability at every level fosters a culture of responsibility and transparency, ensuring that employees and systems align with governance standards.
Harness frontier data
Modern businesses run on SaaS platforms, but buried within these tools is an underutilized goldmine: frontier data. Unlike structured data stored in traditional enterprise systems, frontier data lives across scattered applications—customer interactions in Salesforce, project updates in Asana, or internal discussions on Slack.
While often overlooked, this unstructured data holds immense potential for organizations along their AI journey. For example, AI models trained on frontier data will unlock the ability to analyze internal workflows and historical patterns, providing employees with sharper, context-aware insights.
More than that, frontier data will enable organizations to break down internal silos, enhance AI’s automation prowess and boost innovation. The potential benefits are truly endless.
However, implementation comes with its hurdles—one of the most significant of which is data fragmentation. Frontier data lives in disjointed SaaS applications, making it inherently difficult for organizations to pool and classify this data in one, unified place.
This problem is further compounded by the unstructured nature of frontier data. Unlike neatly organized databases, frontier data is difficult to categorize and analyze using traditional tools. Without AI-driven solutions to process and interpret this ‘raw’ information, businesses will struggle to extract meaningful insights.
Last but certainly not least, there is the issue of security. Merging frontier data with AI can do wonders for productivity, but these endeavors need to be carefully managed from a compliance perspective. Frontier data, after all, is typically rich with IP, customer and employee information. Without robust security controls, unlocking frontier data’s value can introduce more risk than reward.
Thankfully, specialist tools exist to extract, unify and secure frontier data, enabling organizations to leap ahead in their AI journey. Your chosen solution should:
- Integrate and centralize: Aggregate data from multiple SaaS platforms into a unified repository for better visibility and cross-functional insights.
- Leverage AI and machine learning: Process and analyze unstructured data, transforming raw information into actionable intelligence.
- Enable real-time analytics: Provide instant insights, allowing for faster, data-driven decision-making.
- Classify and organize: Use structured classification methods to streamline AI-driven analysis without slowing down operations.
Plan for AI misuse
Whether sanctioned or not, your employees are already using Generative AI tools like ChatGPT and Bard to enhance their productivity. But without strong safeguards, these tools will put your sensitive data at risk. After all, these systems learn by processing large volumes of input. Any information your employees enter could end up being stored and used in ways you don’t expect. Without clear oversight, that confidential information—whether it’s proprietary data, customer details, or intellectual property—could be exposed.
Take Samsung’s recent AI exposure as a cautionary tale. Employees unknowingly input sensitive company data into ChatGPT while optimizing workflows, resulting in a major data leak. This happened despite internal efforts to warn staff about the risks. The trouble is, employees may not understand, forget about, or even overlook security policies for the sake of productivity.
You cannot leave it up to your people to follow AI procedures and policies. You need to complement these guidelines with guardrails: education methods and security measures that prevent employees from breaching security policies in real-time.
Here’s how to do so:
- Set clear guidelines for AI use: Create simple, clear policies about what data can and can’t be entered into AI systems. Make it clear that sensitive information like customer details and internal documents should never be shared. Also, ensure employees know which platforms are approved for business use, and require approval before using AI tools for work.
- Provide ongoing AI training: Equip employees with the training they need to use AI responsibly by offering regular training. Cover risks like data retention, and provide actionable tips for safely using AI tools, such as anonymizing sensitive information. Keep the training updated to reflect new AI developments and emerging threats.
- Control access to AI tools: Limit access to AI systems to authorized users only. Set permissions based on roles, and monitor activity to ensure only the right people are using AI for the right tasks. By controlling access, you maintain better oversight and reduce the risk of misuse.
- Choose secure AI vendors: Partner with AI providers that offer robust security, including data encryption and strong privacy practices. Look for options that allow private deployments, so you keep your data within your organization’s control. Regularly review contracts to ensure the AI tools meet evolving security standards.
Laying the foundations for AI success
As enterprises embrace AI, making strategic decisions around data governance, frontier data, and AI misuse will lay the foundation for success. By integrating the right tools, setting clear policies, and fostering a culture of responsibility, organizations can harness the full power of AI while protecting their most valuable assets.
Ready to secure your AI future? Watch our video to discover how our Polymer protects your data, ensures proper governance, and enables you to safely scale your AI initiatives.