IT security teams have spent decades defining trust based on where systems sit and who can access them. For instance, Zero Trust architecture and related policies such as Identity Access Management (IAM) and Least Privilege are aimed at restricting access to networks and systems to prevent unauthorized access or data breaches.
But modern data rarely stays in one place, and access decisions are no longer the moment when risk is introduced. In today’s environments, content now moves continuously across SaaS apps, collaboration tools, automation, and AI systems, long after initial access is granted.
For CISOs, this creates a growing disconnect when strong identity controls and policies, including Zero Trust, exist, as trust failures still occur downstream, where traditional controls stop evaluating risk. As a result, traditional trust boundaries have shifted, and approaches now need to adapt.
Traditional trust boundaries and policies, such as IAM and Zero Trust, were intended for environments and systems with clearly defined applications, predictable users, and limited content transformation.
A recent commentary on the changing dynamics of trust in the AI age came from PwC, which noted that “today’s rapid transition to a real-time, interconnected, AI-driven network, operating across corporate and national borders, is straining that trust.” The article further adds that digital-era solutions are reaching their limits and “quantum leaps in hardware, software, and data all coming together at once, new vulnerabilities will further undermine” trusted systems.
Today’s operating reality regarding content is fundamentally different than even a decade ago. In modern, AI-driven systems, content is created, reused, reshaped, and repurposed across tools. In addition, workflows have grown substantially and span internal teams, partners, and customers. Finally, AI systems ingest, generate, and redistribute content at scale.
But the strategic driver for the changes in trust boundaries isn’t simply cloud adoption or AI acceleration. It’s the fact that trust decisions now occur at the content layer, not at login, network, or application boundaries.
When trust boundaries are inferred instead of understood, organizations experience several types of friction and gaps that can cause disruptions, including the following:
Context-blind detection – Occurs when controls are able to identify sensitive patterns, but not whether content use aligns with entity-specific obligations.
Overly blunt enforcement – Without trust context, security policies default to blocking or alerting, which ends up disrupting legitimate business activity, often resulting in lags, delays, and user and team frustration.
AI-driven amplification of exposure – Copilots, agents, and automations can surface or reuse content outside its original trust context, without malicious intent.
Alert fatigue without accountability – Alert fatigue is already a significant issue for security teams. One consequence is that although the alerts allow security teams to see what happened, they don’t understand why the alerts matter or which users or actions actually represent risk.
These gaps are not execution failures but are instead symptoms of trust boundaries that no longer map to how data flows.
Considering how content has evolved and expanded, the modern trust boundary is no longer a perimeter, a system, or a static control point; traditional systems that operate on these assumptions no longer work. Indeed, the modern trust boundary is defined by the relationship between who the content is about, who or what is acting on the content, and whether or not that interaction aligns with both business intent and obligations.
Further, trust context is also crucial due to the flow of content from creation and generation to when it’s shared and repurposed. For example, identical content can represent different risk in different contexts, such as unauthorized sharing among platforms. It also applies to workflows; for instance, in many cases, the same action can be acceptable in one workflow and dangerous or risky in another. In addition, AI systems must be evaluated as actors, not just tools.
Modern security must move beyond “Should this user access this system?” to “Does this interaction preserve trust for the entities involved?”
Trust boundaries were once tied to access and location, but new, modern risks have emerged due to how content now moves and is reused and repurposed by various actors.
Plus, adding AI to the mix has accelerated trust boundary failures that legacy controls can’t see. In modern environments, trust must now be evaluated dynamically at the content level.
And as organizations redefine trust boundaries for the AI era, the immediate challenge isn’t tooling, it’s visibility. But most security leaders don’t yet have a clear view of where trust boundaries exist today, where they’re breaking, or which actors represent the highest risk.
Start by assessing your current exposure.
Bonfy’s Data Security Risk Assessment helps you identify where sensitive content is moving, which humans and systems interact with it, and where modern trust boundaries are already being violated…before those gaps turn into incidents.
Take the Data Security Risk Assessment to understand your trust boundary risk in today’s AI-driven environment.