AI Agents, Chatbots, and the Emerging Data Security Challenge
AI agents and chatbots have evolved from experimental pilots or side projects into integral components of customer service and business workflows. They now handle a variety of critical tasks, such as answering customer inquiries, retrieving account information, summarizing support tickets, and routing cases efficiently. As these AI-driven interactions become more widespread and complex, companies face a significant blind spot in monitoring the constant stream of conversations and workflows involving sensitive data.
A recent study underscores this risk: 90% of security leaders have admitted to using unapproved AI tools at work, with 69% of Chief Information Security Officers (CISOs) incorporating these tools into their daily routines. The report also reveals that approximately 80% of employees use AI tools without formal approval. Despite 52% of employees being aware of their organization’s AI usage policies, 70% acknowledged sharing sensitive data with AI applications in the workplace.
To address this growing security concern, SendSafely offers an end-to-end encryption infrastructure that acts as a foundational trust layer. This solution sits between sensitive customer data and the expanding ecosystem of AI applications interacting with it, ensuring robust protection without disrupting existing workflows.
Limitations of Traditional Security Tools in the AI Era
Conventional security tools are built around straightforward models of data transmission and storage. They typically monitor file transfers and storage locations using controls such as Data Loss Prevention (DLP) rules, email gateways, secure FTP alternatives, and access permissions — all designed with a perimeter-focused approach in mind.
However, AI-enabled workflows blur traditional boundaries between messaging and file sharing. For instance, a customer may upload data during a conversation that an AI agent subsequently routes to different internal systems. Human agents may later take over the interaction, often without full visibility into how many systems the data has traversed. This complexity makes legacy security controls inadequate for tracking and securing data flows in modern environments.
Moreover, forcing users to switch tools to comply with security protocols often leads to friction and inefficiency.
“Instead of slowing down the experience with another file transfer tool, today’s companies need encryption infrastructure,” explains SendSafely’s Co-founder and Chief Product Officer, Brian Holyfield. “When we embed end-to-end encryption into the systems where work happens, teams don’t need to change behavior because security travels with the workflow.”
Practically, this means data is encrypted locally on the sender’s device and can only be decrypted by authorized recipients’ devices. This approach prevents any intermediary party — including AI bots or SendSafely itself — from accessing the content without explicit authorization. This differs fundamentally from encrypting data only after it reaches a server or using vendors that retain decryption capabilities.
Why AI Data Security Requires a New Layer of Protection
AI has exponentially increased the number of touchpoints where sensitive data appears. According to industry projections, the volume of enterprise data flowing through AI and machine learning applications is expected to reach 18,033 terabytes by 2025. Alarmingly, 39.7% of AI interactions expose sensitive information, marking a 93% year-over-year increase that raises serious concerns for data security practitioners.
In many cases, customers and employees share sensitive details during chats to obtain support or verify their identity. To expedite processes such as claims or onboarding, users often bypass recommended security best practices.
“Customers and employees will take the path of least resistance,” notes Holyfield. “Security teams can’t train that away, but they can redesign the workflow to protect sensitive content by default.”
SendSafely addresses this with its HALO platform, which integrates a layer of encrypted data collection specifically for AI chatbots. Compatible with popular platforms like Ada, Forethought, Intercom, Zendesk, ServiceNow, and Agentforce, HALO keeps conversations flowing smoothly while encrypting data in real time. End-to-end encryption occurs before any data reaches a server, ensuring that neither the chatbot platform nor SendSafely itself can access the contents.
Additionally, SendSafely MCP, currently in limited Beta, offers a local Model Context Protocol server. This allows Large Language Model (LLM)-driven agents to operate on encrypted data through an emerging standard. Since these agents already retrieve and move data as part of daily operations, integrating SendSafely MCP’s encryption layer upfront enhances security proactively rather than reactively.
Seamless Enterprise AI Security Without Workflow Disruption
Holyfield emphasizes, “Security that disrupts work gets bypassed. Anything that feels separate from the workflow often prompts employees to cut corners, copying sensitive details into chats or using consumer-grade file-sharing links. Modern enterprise security has to be judged on both strength and usability.”
SendSafely’s product integrations are designed to feel native within the tools employees already use. For example, in Zendesk, agents can collect and send encrypted files directly inside tickets, while customers upload files through a secure widget without leaving the conversation. Salesforce users benefit from encrypted Dropzones embedded in Cases or portals. Intercom’s encrypted file collection integrates smoothly into the Messenger experience, preserving protection across AI-driven and human-assisted support. Even in Gmail, encryption in the compose window makes sending secure messages a natural part of everyday communication. Importantly, recipients never need to create an account to access encrypted content.
For organizations requiring tailored implementations, SendSafely provides a REST API with SDKs and a Dropzone widget that can be embedded into any webpage. This flexibility covers internal tools and rapidly developed, low-code applications managing real customer data. Encryption always happens client-side, maintaining strict control over sensitive information.
“The most effective security strategies add an encryption layer so teams can move fast without compromising on security,” Holyfield concludes. “If AI is becoming embedded in your daily operations, encryption infrastructure needs to be embedded there too.”
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