25 Nov AI Tools and Data Security: A Closer Look
Jack Fletcher, Senior Director at FTI Consulting, shares insights on AI’s growing presence in workplaces and its implications for data security, while our Ambassadors contribute their perspectives.
Artificial intelligence tools, like ChatGPT, have rapidly become a part of daily tasks, from crafting the ideal vacation plan to composing challenging emails. As organizations increasingly adopt AI technologies, there are noticeable improvements in efficiency and organizational effectiveness. However, the emergence of unregulated “shadow AI” in workplaces poses significant risks to data security that can jeopardize compliance efforts and expose sensitive or proprietary information.
The Compliance Challenge of Shadow AI
Many data privacy regulations emphasize the importance of data reuse and transparency. One of the core principles is that individuals should be informed about how their personal data might be used beyond the original purpose for which it was collected. Additionally, several laws impose limitations on where this data can be stored. Some AI tools depend on data storage in countries with subpar data protection standards, which may lead organizations to unintentionally violate regulations if sensitive data is input into such systems.
Identifying Security Risks with AI Tools
While reputable AI tools often implement robust security measures, there are significant risks that persist. Sensitive information, including intellectual property, can still be inadvertently disclosed or mishandled. This situation becomes even more complex when employees use AI tools outside of company devices, making it harder for security teams to track and prevent potential data breaches.
Encouraging Responsible AI Usage
To address these challenges, organizations should focus on fostering a culture of responsible AI use. Training programs can help employees understand the risks associated with shadow AI and educate them about the fundamental principles of acceptable use policies. Whenever possible, staff should be directed to utilize in-house AI tools and encouraged to conduct corporate activities on official devices.
Voices from the Community
One of our ambassadors remarked, “For organizations to use AI responsibly, they need clear policies outlining usage rules, especially regarding the protection of client data. It’s crucial to harness AI’s potential while safeguarding our core values and intellectual property, particularly when using public or external AI tools.”
Following best practices can significantly reduce the risks tied to external AI tools. Advised guidelines include: refraining from entering any sensitive or personal data into AI systems, being aware of the information generated, ensuring transparency, obtaining consent where necessary, and confirming the accuracy of AI-generated outputs before publication. Strong respect for third-party intellectual property rights is also essential to avoiding copyright issues.
Another expert emphasized the dual-edged sword of AI adoption, stating, “Utilizing AI tools presents numerous benefits, such as enhanced operational efficiency and enriched data analysis. Yet, it also brings complex security and privacy challenges, particularly concerning the risk of exposing sensitive data and the difficulties that arise when trying to delete data.”
Navigating the Complex Landscape of AI Security
As AI becomes more woven into both security frameworks and broader business operations, the challenge of maximizing value while protecting data intensifies. AI’s capability to scrutinize massive data sets allows for improved decision-making but simultaneously creates vulnerabilities. Organizations must protect sensitive information throughout the data lifecycle — from its collection and storage to processing and output.
The use of third-party AI platforms introduces additional issues, including compliance with data protection standards and risks of unauthorized access. Moreover, addressing algorithmic bias is critical to maintaining equity and trust in AI-driven decision-making processes.
One professional noted the imperative of embedding security and privacy within AI design. He stated, “Robust governance frameworks and a commitment to responsible AI deployment are vital. The successful implementation of AI tools hinges not just on their technological capabilities, but also on our vigilance in safeguarding the data that they process.”
From a security standpoint, the integration of AI into corporate environments adds layers of complexity. Each interaction with an AI system inevitably generates data artifacts that might persist longer than intended, making traditional security measures insufficient. The ongoing legal implications of AI usage — such as those highlighted in the recent NYT v. OpenAI case — signal the need for stronger access controls, logging, and transparency regarding privacy disclosures.
As organizations increasingly leverage AI technologies, the importance of pairing these innovations with robust security protocols cannot be overstated. Maintaining a balance between innovation and responsibility is essential for effective data governance and compliance in the age of AI.
