Rethinking Data Security in the Age of GenAI: Why Contextual Intelligence Matters More Than Ever

In the past year, business leaders have rushed to bring Generative AI into their organizations using tools like Microsoft Copilot, Claude, Google Workspace add-ons, or custom-built internal copilots. Today, most companies are beyond just testing GenAI and are actively putting it to work, streamlining daily tasks, speeding up service, reducing bottlenecks, and building new kinds of smart workflows driven by AI agents. There is an aspirational roadmap for deploying and rolling out GenAI that begins with achieving productivity goals and maturing to more complex use cases such as agentic workflows integrated with internal data stores to extract the most value out of such rollouts and demonstrating healthy ROI. More often than not, the clarity and specifics on how GenAI would mature within the enterprise is found lacking.  

 Most major analyst firms, from Gartner to Forrester to IDC, have highlighted that data and privacy are most at risk when deploying GenAI. The risks associated with enterprise data exposure, unmonitored AI interactions, and uncontrolled model access are now top of mind for CISOs, CIOs, and security architects. The challenge is not only preventing accidental data leakage or outsider threats; it is also dealing with a fast-growing new access paradigm created by non-human agents (GenAI models and LLM-powered tools) constantly and autonomously interacting with data. 

The Challenge in Rolling Out GenAI Across the Organization 

Most enterprises already own several data security controls: DSPM (Data Security Posture Management) for visibility, DLP (Data Loss Prevention) for enforcement, DDR (Data Detection and Response) for anomalies, and DAG (Data Access Governance) to reduce excess access.  

• The question security leaders are now asking is: Is GenAI data security simply an extension of these capabilities, or does it require an entirely new model? 
• The answer: Because GenAI devours data in bits and pieces from various data stores and repos, it introduces a fundamentally new set of risks that legacy architectures were not designed to handle. 

Why Existing Approaches Fall Short 

Traditional data security tools were created in an era where humans were the primary actors creating and accessing data. They assumed: 

• Static and well-defined data constructs (files, documents) with low variance modality.  

• Access patterns are relatively stable. 
• Data flows follow predictable business logic. 

GenAI breaks all these assumptions. 

AI copilots, autonomous agents, and LLM-powered apps: 

• Generate new content based on prompts that may contain sensitive information. 

• Operate across multiple tools and data stores simultaneously. 

• Are often granted broad contextual access to help users be productive. 
• Access and transform data at machine speed. 

This leads to new visibility and control gaps: 

• Excessive access becomes significantly more dangerous. 

• Sensitive data moves into new surfaces (chat logs, embeddings, prompts, summaries). 

• Shadow AI: employees using unapproved AI tools without oversight. 
• Insider risk is amplified because even small mistakes get amplified by AI automation. 

As highlighted in many Bonfy.ai research articles, including “Over-Permissioning and Data Leakage Risks With Microsoft Copilot” and “Shadow AI: The Hidden Cybersecurity Threat Lurking in Every Enterprise,” organizations now need to secure not only data itself, but the context of how and why that data is being used by both humans and machines. 

A New Approach: Contextual, Adaptive, Entity-Aware Data Security 

To secure data in a GenAI-powered enterprise, organizations need to shift from static rule-based controls to dynamic contextual understanding of: 

• What data is being accessed. 

• Who or what is accessing it (human, bot, agent, service) and what other entities are referenced (for e.g.). 

• Why the access is occurring (intent, workflow context). 
• How the data might be transformed or re-shared. 

A) Think in Terms of Contextual Intelligence at Rest, in Motion, and In Use

Legacy DSPM tools give you a map of where data lives or presence of static patterns in data-in-motion, but no awareness of how entities interact with it. Traditional DLP can block known patterns, but it cannot interpret purpose or context. 

In the GenAI era, data security tools must: 

• Understand data semantic meaning, not just classification labels. 

• Track relationships among identities, roles, groups, apps, and agents. 
• Model real-world usage patterns and detect deviations in context. 

This requires multi-contextual, entity-aware intelligence the ability to know: 

• When access is expected vs. suspicious. 

• When data movement is productive vs. risky. 
• When sharing is business-valid vs. accidental leakage. 

This is where both pure-play DSPM vendors and legacy DLP vendors fail. They are built around static policies, not adaptive intent-aware decisioning. 

B) Strengthen Insider Risk Defenses to Manage Shadow AI

Shadow AI is not malicious by default; it is convenience driven. Employees use whatever tools help them get work done faster. Productivity is top of mind. 

However: 

• Sensitive documents get pasted into public LLMs. 

• Model training pipelines accidentally include confidential data. 
• Teams share internal business context with external agents. 

Without a proactive insider risk program, security teams discover these exposures after the damage occurs.  

GenAI data security must therefore include: 

• Real-time detection of AI usage across sanctioned and unsanctioned tools. 

• Behavioral signals to detect anomalous or unsafe AI-assisted activity. 
• Guided interventions to educate and correct, not punish, end users. 

C) Your Data Security Strategy Must Evolve with Your AI Maturity Roadmap

Most enterprises are currently in Stage 1: Using copilots to boost productivity in email, meetings, search, and documentation. 

But your roadmap likely includes: 

• Stage 2: Department-specific copilots. 

• Stage 3: Autonomous agent workflows and internal LLM apps. 
• Stage 4: Fully agentic enterprise systems interacting with critical data. 

Your security solution must therefore: 

• Scale from basic usage oversight to applying controls at detection and enforcement points. 

• Provide controls that grow as your AI capabilities mature. 

• Support standards like MCP (Model Context Protocol) for safe internal model integration for more agentic and complex smart applications. 

Why Bonfy.ai is Purpose-Built for GenAI Data Security 

Bonfy.AI is designed from the ground up for the new AI-driven access landscape. It does not bolt GenAI add-ons onto old architectures as it starts with: 

• Contextual intelligence. 

• Entity relationship graphs. 

• Adaptive policy frameworks. 
• Multi-entity aware model which supports human as well as machines/agents as generators and accessors of data.  

Bonfy provides: 

• Unified semantic + behavioral understanding across data at rest, in motion, and in use. 

• Real-time monitoring and control of AI interactions (approved or shadow). 

• Automated insider and entity risk management tuned specifically for GenAI use cases. 
• A maturity-aligned approach that grows with your AI adoption roadmap. 

In other words: Bonfy gives you the control you need without slowing your organization down. Instead of blocking innovation, it enables safe, confident, and scalable GenAI adoption. 

TL;DR 

GenAI is not just another productivity layer; it is a new mode of interacting with enterprise knowledge itself. Securing this new mode requires context, intent, and entity awareness, not just more policies, more scans, or more alerts. 

Enterprises that adapt to this new security paradigm will unlock extraordinary value. Those that don’t will spend the next decade reacting to data incidents they never saw coming.  

Bonfy exists to make sure you are in the first category.