Human Abstraction and Data Security

The Evolving Human-Information Relationship

From the time humans began to form our earliest civil societies, the exchange of information has been essential. Traditionally, these exchanges occurred face-to-face, relying heavily on personal trust. Often, there was less need to exchange sensitive personal information because the individuals either knew each other or were connected through personal associations. Regardless, even in traditional trusted exchanges, humans took in information and processed it in ways where they were either the only viable option, or the only practical one—and produced an output.

Fast forward to today: much of our activity still involves taking in information, processing it, and acting on it. However, a growing risk in this model stems from the fact that most transactions are no longer face-to-face. In traditional face-to-face exchanges, information is often shown to a person without leaving a permanent copy—it remains ephemeral. As we replace these interactions with system-based exchanges, moving information through digital systems too often creates persistent exposure and expanding risk.

Without built-in trust mechanisms, we must rethink how we manage trust, privacy, and risk in this new environment.

The Challenge of Digital Data Movement

In practical terms, we move data because humans need direct access to perform analysis or make decisions, and to serve as lasting evidence for the basis of those decisions. The most sensitive information is often not even necessary to the desired outcome, but very often accompanies the truly needed data elements anyway because separating them is costly. This creates unnecessary risk to the information when moved through digital systems.

One possible path forward—especially with the progress of AI—is to leave the data we are processing in place whenever possible and abstract it in such a way that it does not need to be accessed directly by anyone other than the owner, or mutually trusted third parties. Rather than relying on legacy models that involve moving data from system to system and securing it at every step, we should ask: How can we leave data in place, process it securely, and deliver outcomes that data owners and stakeholders can mutually trust?

If we can instead abstract the outputs—findings, conclusions, recommendations, decisions, actions, and outcomes..without exposing the underlying data to any but the data owner and their trusted operational third parties—we can eliminate a major source of risk.

Building a black-box abstraction layer to perform traditionally “human only” processing and analysis of the most sensitive information could be a solution to securely minimizing exposure.

The Knowledge, Cognition, and Reasoning (KCR) Layer

As AI evolves, humans are shifting upward in the value chain—moving away from routine processing toward focusing on uniquely human functions: Knowledge, Cognition, and Reasoning (KCR). This "human-only" layer is what AI systems are beginning to augment and replicate.

Imagine applications such as credit decisions, loan approvals, and transaction validations—already partially automated—fully transitioned into trusted black-box processes where only the abstracted outcome is visible. This approach would significantly reduce fraud, data leakage, infiltration, and exfiltration risks.

To make this work, we must push as much processing as possible into the KCR layer while keeping the human in the stream. I use "stream" rather than "loop" because many of these processes flow left-to-right—linear and repeatable, but not necessarily circular—requiring continuous human oversight across evolving sequences of actions and outputs.

The Supervision Challenge

Of course, building such a black box raises an essential challenge: How do we ensure that the findings, conclusions, recommendations, decisions, actions, and outcomes it produces are trustworthy and aligned with human intent? If we can develop structured, mathematical insights into how the KCR layers operate, we can safely apply this model at scale.

As we increasingly rely on AI systems operating within this KCR layer—abstracting outputs from data without exposing the data itself—we face a critical challenge: How do we supervise or monitor these black-box processes to ensure trustworthiness, accountability, and alignment with human values?

Traditional oversight often depended on human review of the underlying data, but that option disappears when data remains abstracted for privacy, security, or compliance reasons. Instead, we must build supervision frameworks rooted in Observability.

Observability: Enabling Trust Without Exposure

Observability serves as the overarching supervisory capability that enables structured insight into the what, why, and how of system behavior and decision-making, even without seeing the underlying data itself. It ensures that black-box activities are converted into trusted, structured signals that can be independently verified, continuously audited, and aligned with policy and ethical standards. I think observability is composed of four interdependent components.

1. Explainability: Understanding Why

Explainability provides insight into why a system produced a particular outcome. In the context of a black-box KCR layer, it takes the form of structured rationales or counterfactual justifications—descriptions that clarify the logic behind outputs without revealing the underlying data. These rationales are framed in ways that align with shared policies, domain expectations, and decision standards, making them understandable to both technical and non-technical audiences.

2. Interpretability: Understanding How

Interpretability reveals how a system arrived at its output. It surfaces internal model behaviors—such as attention patterns or activation pathways—that can be analyzed to understand the model's internal decision-making. These insights allow system designers and domain experts to evaluate reasoning consistency and policy alignment, without needing direct access to the sensitive source material.

3. Accountability: Documenting What

Accountability ensures that we can definitively catalog and attribute what the system has done—what findings, conclusions, recommendations, decisions, actions, and outcomes it has produced. It creates formal, reviewable records of system outputs over time, enabling robust auditing, traceability, and governance, even when the sensitive source data remains abstracted.

4. Telemetry: Observability Infrastructure

Telemetry provides the foundational mechanisms that capture, structure, and transmit the necessary signals for explainability, interpretability, and accountability. Effective telemetry ensures that behavioral data, reasoning traces, confidence scores, output records, and other critical metrics are reliably collected and exposed in a way that preserves privacy, supports continuous supervision, and enables actionable human oversight. Without robust telemetry, the observability framework would lack the trusted, structured insights required for reliable governance.

Shared Trust Through Mutual Oversight

Crucially, this supervisory insight must be available to both parties in the relationship:

1. The data owner, who retains control and accountability over how their data is used, and

2. The consumer of the output—the human relying on a finding, conclusion, recommendation, or decision derived from that data.

   
   

This shared visibility into system behavior, reasoning signals, and outcome justifications forms a mutual trust foundation. It enables both parties to validate that the KCR layer is functioning as intended, without either side needing to breach the abstraction that protects the data itself.

Looking to the Future

Although in a perfect world building a KCR abstraction layer for data security reasons might make sense for individual use cases, it portends the coming broader disruption to the workforce. And regardless of the reason, replacing humans in a process always surfaces valid concerns about the impact to employment, and to society as a whole. Increasing AI autonomy is inevitable, and whether it is a net good or bad for society will depend on being able to routinely monitor the intents and quality of autonomous KCR activities.

Disruption is inevitable but not new, and if we view AI as a tool—something to use, operate, supervise, and monitor—we remain in control. The essential question is: How do we supervise the KCR layer to ensure trust, accountability, and human benefit?

By implementing robust observability frameworks and keeping humans in the stream, we can find ways to replicate the ephemeral nature of face-to-face exchanges within digital frameworks, creating AI systems that enhance rather than replace our uniquely human capabilities.