By 2025, the first major breach of a knowledge management generative artificial intelligence (Gen AI) solution chatbot will make global headlines. This will mark a turning point in cybersecurity for all industries.
The widespread adoption of Gen AI-based business solutions expands the prevalence of shadow AI. Shadow AI is a major security vulnerability for companies in any industry. A National Cybersecurity Alliance survey found that more than a third of employees share sensitive work information in these tools without an employer’s permission, most corporations have yet to experience—and fully prepare for—the devastating impact of an attack on a Gen AI-based business solution. These AI platforms that are deeply integrated into a business usually have unfettered access to knowledge management, business process automation, employee training, decision-making, and communication—turning organizations into wide-open books of data exposure. It’s the holy grail for cybercriminals.
The inevitable news-making breach of a business’ centralized AI solution will expose extensive vulnerabilities where critical business crown jewels, sensitive information, proprietary strategies, and intellectual property are stored. This future cyber incident will compromise much more than typical data stores since this type of data loss extends to valuable information which enables unprecedented levels of intellectual theft and sabotage. Its fallout will serve as a wake-up call, forcing companies to reevaluate how they secure generative AI-based solutions or subscriptions. This will spark a shift toward prioritizing Gen AI-specific safeguards within cybersecurity frameworks.
But first, how are such Gen AI models used, and why are they riskier?
Greater Convenience, Greater Risk
It sounds like a no-brainer. To enhance business efficiency, employ an enterprise Gen AI solution, perhaps one with a chatbot, that helps your team find any information they need to know to conduct their work or enhance it in any way. Plenty of Gen AI-based solutions, such as Glean and Elastic Search, connect a company’s institutional knowledge and data to help employees make better decisions every day. By using such solutions and their chatbots, employees can learn about project updates or various internal and external business operations, allowing them to access highly specific information. The catch–22 is a tough trade-off: those same Gen AI solutions that ease business flow are prime espionage targets on the brink of a major cyber incident.
Why are Gen AI models a bullseye? Because they store and process vast amounts of proprietary data, offering asingle point of access to vast amounts of critical business crown jewels, intellectual property, and trade secrets. AI’s central role in business operations, including decision-making, payment systems, SAP, customer databases, competitive strategy, etc., also makes it a goldmine for threat actors.
As such, the threat of a knowledge management chatbot breach is very different—and much more dangerous—for companies than typical vulnerabilities. When a platform contains everything that is specific to an organization, including all the sensitive PII data that the organization holds for both employees and customers/partners, suddenly the stakes to keep that data protected are of paramount importance.
Now that this cybersecurity prediction draws a significant line in the sand, where do organizations go from here? It’s critical to employ the following best practices in everything you do.
Lax or Deficient Security Awareness Cannot Be an Excuse
Most employees have developed a level of complacency about compliance rules that employers ask them to follow. For example, many have heard that they shouldn’t save or enter personal information on a work device or do company work on a personal device. Employees may find these rules to be overdone, or unnecessarily strict, and in thinking so, they interpret the guidelines generously to the point of ignoring them. How harmful could it be to quickly log in and check my email on my personal laptop anyway?
Perceiving these rules to be innocuous means that in the new world of Gen AI models, appropriate caution is thrown to the wind from the start. Add to that a lack of training on these new solutions and businesses now face unprecedented exposure risk.
According to findings from the same survey by the National Cybersecurity Alliance, over 50% of employees have never been trained on safe AI use. There is a disconnect within organizations when it comes to helping educate employees to recognize potential dangers and risks and then helping them possess the knowledge to mitigate those threats and know how to respond when facing those dangers. Organizations must ensure that employees understand the long-term consequences of all data used in the chatbot.
To become complacent is unacceptable. When a chatbot possesses all the internal information about an organization, it suddenly makes that asset the single most important target of any hacker. Access to chatbot data is orders of magnitude more valuable to a threat actor than even having access to the CEO’s inbox.
Complacency may have been harmless before, but it cannot be tolerated in today’s world of Gen AI models and prevalent shadow AI.
Develop a Checklist and Know Who’s Responsible
To ensure the security of an internal knowledge management chatbot, it is essential for every organization to develop a comprehensive security checklist. This checklist helps identify potential risks, safeguard sensitive information, and maintain overall data integrity. Equally important is knowing exactly who within the organization is responsible for managing the chatbot, monitoring its usage, and securing the data from both internal and external threats.
- What is on your security checklist? Develop a detailed list of all security protocols, including encryption methods, access controls, and data backup strategies. This checklist should cover everything from secure authentication methods to regular audits of data access logs to ensure compliance with organizational security standards.
- What are the guidelines for using enterprise gen AI solutions? It is crucial that everyone in the organization understands how AI tools should be used in a responsible manner. Create explicit guidelines on what types of information are permissible to share with the chatbot and which kinds of data should never be disclosed. Repeat training sessions quarterly or utilize security training services that consistently engage the workforce with reminders. This will prevent unintentional leaks of sensitive information and ensure that users know the boundaries for interacting with the tool.
- Who’s responsible for the data? Clarify the ownership of the data managed by the chatbot. Is the organization itself responsible for the data, or does it fall on the platform provider hosting the chatbot? Establishing clear accountability and doing so soon after implementation, if not before, will ensure that there is no ambiguity in the event of a data breach or security incident.
- Are the API endpoints locked down? Ensure that any external integrations with the chatbot, such as API calls, are secured. API endpoints should be protected by firewalls, encrypted communication channels, and proper authentication protocols to prevent unauthorized access.
- Have you updated your information security, data governance, and compliance posture? Implement a robust data governance framework to manage data security across the organization. This includes ensuring compliance with relevant regulations (such as GDPR, HIPAA, etc.) and creating a clear plan for how data is handled, stored, and disposed of. Ensure employees are regularly reminded of the protocols.
- Are your employees trained in the evolving security posture and threat landscape? Invest in comprehensive employee training on data security, privacy, and how to interact safely with AI tools. Educate employees that this is not the “same old same old”. The evolving landscape means heightened risk. As AI usage within the organization increases, security measures, compliance checks, and training efforts must evolve beyond standard procedures to meet the unique challenges posed by new technology.
By addressing these key points, organizations can ensure that their internal knowledge management chatbots are not only efficient but also secure, compliant, and aligned with best practices in data protection.
Conclusion
As 2025 begins, organizations must recognize that the security landscape surrounding Gen AI solutions’ shadow AI, particularly those used for knowledge management, is rapidly evolving and involves many serious security risks. The potential for devastating breaches of these systems is real. Because it’s highly likely the first major Gen AI model cyber incident will occur this year, businesses will, unfortunately, find out how vulnerable they are and realize that they have failed to take the correct proactive measures. Organizations can no longer afford to operate with complacency. The security of AI-driven knowledge management tools should be treated with the same level of vigilance as any other critical business system. A breach of these platforms is not just a loss of data; it’s a wide-open door to intellectual property theft, espionage, and irreversible damage to a company’s reputation.
By implementing a robust security checklist, clarifying responsibility, and committing to comprehensive employee education, businesses can mitigate these risks and better prepare for the future. The time to act is now—before that first headline is written. Security in the age of Gen AI is not optional; it is imperative. Organizations that invest in securing their AI systems today will be the ones that stand strong in the face of inevitable threats tomorrow.
Christian Geyer is the founder of Actfore, he brings over 18 years of experience in driving revenue growth and transforming organizations in cyber, defense, and data governance. Known as a seasoned operator, he excels at implementing measurable change in industries burdened by inefficiency and high costs. His impressive track record includes stepping into companies facing losses, analyzing operational data to identify inefficiencies, and deploying innovative technology solutions to restore profitability.
The opinions expressed in this post belongs to the individual contributors and do not necessarily reflect the views of Information Security Buzz.
