Despite encryption protocols improving, access controls strengthening, and compliance frameworks maturing, there is a key emerging vulnerability that traditional managed file transfer (MFT) controls weren’t designed to address. In a recent survey, it was found that over a quarter (26%) of organisations have experienced AI-related data incidents in their MFT environments over the past year. Not-coincidentally, three-in-ten (30%) permit their employees to use AI tools with sensitive files without formal controls, and 12% have not yet assessed AI-related data security risks.
Clearly, there is a disconnect between MFT security capabilities and how employees handle data after it leaves the managed transfer environment and before it returns. Something needs to be done.
Implications of moving data
Whilst MFT systems control data movement through defined channels with strong security controls, they still face vulnerabilities in both directions of data flow. Typically, once an authorised user downloads a file to their endpoint device, MFT systems have limited visibility. At that point, the data exists outside the managed environment, subject only to whatever endpoint security controls the organisation has deployed. Employees who download files from secure MFT portals may subsequently upload those files to publicly available AI platforms to assist with summarisation, analysis, or content creation.
Equally concerning is data entering MFT systems that has been processed, generated, or potentially poisoned by AI tools. When employees use AI tools to create content, generate reports, or analyse data − then upload those AI-generated outputs back into MFT systems − organisations may be ingesting something they shouldn’t. This could be poisoned data designed to corrupt datasets, hallucinated information that AI models present as fact, embedded malicious content hidden within seemingly legitimate files, or manipulated analysis subtly altered to benefit threat actors.
This bidirectional exposure means MFT systems can become conduits for both data exfiltration and data contamination. Unfortunately, traditional content inspection tools may not detect these risks because AI-generated content often appears legitimate in format and structure, even when the substance is compromised.
The regulatory implications of this data movement pattern are significant across multiple frameworks. For healthcare organisations, uploading patient information to public AI platforms likely constitutes a HIPAA violation. Financial services firms, meanwhile, face potential violations of Regulation FD if material non-public information reaches AI systems. Government agencies must consider NIST SP 800-171 requirements and data sovereignty concerns. For defence contractors subject to CMMC requirements, CUI (Controlled Unclassified Information) reaching commercial AI platforms represents a direct violation of DFARS clauses and could result in loss of certification, contract suspension, and civil penalties.
Why traditional controls are ineffective
There are several gaps in how organisations approach the threat posed by MFT systems in the AI era. The first is the fact that there is commonly a lack of integration with the wider security estate. So much so, that only37% of organisations have integrated their MFT systems with security information and event management (SIEM) or security operations center (SOC) platforms. This means 63% cannot correlate MFT download events with subsequent AI platform access, even if both activities are individually logged.
The second is that whilst almost half (48%) of businesses report conducting regular AI risk reviews, a significant percentage (40%) rely on manual enforcement through training and periodic audits rather than technical controls.
Then there is the fact that only just over a quarter (27%) of organisations have deployed content disarm and reconstruction (CDR) capabilities that could detect anomalies in AI-generated content. Traditional antivirus and DLP tools were simply not designed to identify hallucinated information, poisoned datasets, or subtly manipulated content that AI tools might introduce.
How to control the problem
So, what needs to be done? There are common characteristics among those organisations that report no AI-related incidents. The first is that they commonly deploy data loss prevention (DLP) systems configured to recognise AI platforms as potential data exfiltration risks. Rules monitor not just file uploads but also clipboard operations and API calls to AI services. They also implement validation processes for content entering MFT systems from external sources.
By connecting MFT audit logs with endpoint detection systems and DLP platforms there is safety in numbers. When someone downloads a file from the MFT system and subsequently accesses an AI platform, this correlation triggers alerts for investigation. Similarly, anyoneuploading something to an MFT systems following AI platform usage receive additional scrutiny.
It is always best to provide staff with the tools they need. To help illuminate the scourge of shadow AI, savvy organisations provide approved AI tools with enterprise agreements. These tools’ contracts typically include stronger privacy protections, prohibit training on customer data, and may include business associate agreements meeting HIPAA requirements. Importantly, these enterprise tools maintain better audit trails of AI interactions with corporate data.
Finally, those organisations fully aware of the risks of AI implement processes to verify AI-generated content before it enters MFT systems. This may include human review of AI outputs, automated fact-checking against authoritative sources, or metadata tagging identifying content as AI-generated for downstream recipients.
A question of timing
Unfortunately, the risk of using MFTs will only intensify as AI tools become more capable and widely adopted, threat actors develop more sophisticated data poisoning techniques and regulators develop enforcement frameworks.
The 26% who have experienced incidents represent an early warning that it is important to heed. For organisations that have invested in MFT security, closing this gap extends existing controls rather than requiring fundamental redesign. Luckily, the needed capabilities of endpoint monitoring, system integration, AI-specific DLP rules, and content validation are all available with current technology.
The question facing security leaders is really one of timing. Whether to address this vulnerability through planned implementation or incident response. Yet, with AI adoption accelerating across the workforce, the time is now to add it to your to do list.
