Subjectivity in Privacy and AI

Privacy tools and LLMs are subjective software and professionals remain responsible for their outputs and adaptations.


December 2, 2025

Professional service providers must rely on their own judgment. White collar disciplines thrive in highly contextually nuanced gray areas, and that's exactly why employed privacy tools and AI services must be treated as subjective instruments requiring human oversight, not deferential deterministic machines.

Probabilistic AI systems and context-dependent privacy software share limitations as to reliability and latent dangers, due to accuracy subjectivity and future unknown risk vectors.


Subjectivity in Privacy and AI

Imprecision is Inherent in Both Privacy and AI

In reality, both systems are inherently probabilistic, and neither understands intent, liability, regulatory nuance, or client impact in an adequately nuanced nor future-aware manner. That understanding and responsibility still belongs to the professional for the foreseeable future.

It's a design reality of statistical systems: neither the likelihood of data being adequately protected by privacy tools ad infinitum given unknown correlative factors, nor an AI output being "correct" given generative AI's inherent subjectivity is truly binary. This means blind trust is dangerous; if an AI or privacy tool vendor is promising fully delegated services or fully automated outputs, red flags should be raised.

For Privacy Tools:

  • Data points taken alone can be inconsequential, but when combined with other data points, they can be used to identify an individual. For example, anonymized account information plus logged timestamps and IP addresses can establish identifying patterns in aggregate.
  • A partial anonymization can still allow re-identification through context, like general descriptors or other data points that can be used to infer identity when combined to provide a more narrow profile.
  • Data availability and collection practices can change over time, and may not be fully transparent to the user.

For AI:

  • Initial answers may appear correct or plausible, but later in a context window, latent errors or omissions can arise from misinterpretation or hallucination.
  • LLMs are known to "gaslight" in order to satisfy a prompt, often in subtly deceptive ways that are difficult to detect without verification of source material.
  • Different models have different datasets, and current information may be restricted to public source availability (and biases therein).

The result: privacy risks and LLM output reliability still require human oversight and should not be blindly automated.

Human professional review is critical because:

  1. Privacy tools can over-redact
    • Removing meaning, factual continuity, or disrupting user experience.
  2. Privacy tools can under-redact
    • Leaving linkable quasi-identifiers intact.
  3. AI reflects training data quality, bias, and cutoff date
    • Which can skew accuracy, recommendations, or tone.
  4. AI cannot adequately assess downstream liability or uncommunicated intent
    • LLMs work with context, historical datasets, and publicly available current information.

Latent Dangers

The most dangerous privacy failures are rarely obvious breaches, but rather the cumulative effect of many seemingly innocuous data points that can be used to reconstruct an identity. Each alone appears harmless. Together, they enable identity reconstruction, competitive intelligence leakage, or regulatory exposure.

At the AI layer, similar compounding occurs:

  • A vague prompt invites creative completion designed to satisfy the prompt rather than the intent of the user.
  • A missing prompt instruction gets auto-filled by an assumption that is incorrect.
  • Misinterpretations and incorrect data points propagate across outputs and potentially training data.

Manual output inspection on both the privacy side and the AI side is essential.

Professional Human-in-the-Loop AI Workflow

A repeatable, human-controlled workflow that aligns privacy protection with high-quality AI output:

1. Anonymize/Redact, Strip Metadata, and Obscure IP Addresses Locally


Load or paste your prompt files, case materials, or internal drafts into CamoText and anonymize or redact all sensitive fields. No metadata remains in the output, and you can save the anonymization key locally if you want to de-anonymize the output later.

Use a VPN to obscure your IP address from the AI service's webhost, and check your account settings to opt into the best available privacy settings.

You are defining the privacy boundary before any AI or other external service ever sees your data. This is your data minimization layer.

2. Prompt with Structure: Role + Task + Instructions + Context


Once anonymized, send the sanitized version into your AI system using a precise structured prompt framework:

  • Role – Who the AI is mimicking
  • Task – What it must produce
  • Instructions – Constraints, tone, format, acknowledgment of your anonymized placeholders/tags, verification rules
  • Context – The anonymized source material and other relevant information

Add two safeguards:

  • Require linked citations or explicit uncertainty
  • Require clarifying questions before execution

This forces the model to surface ambiguity instead of silently guessing.

3. Generate but Do Not Trust Automatically


AI output should be treated as a first draft, a pattern detector, and a reasoning accelerator, not as a finished professional artifact.

Review for logical and intent coherence, factual validity, industry-specific nuance, and implicit assumptions introduced by the model.

4. Adjust your Material Manually or De-Anonymize Locally


Once you approve the structure and reasoning of the output, you may use CamoText's De-anonymize function to safely re-insert original identifiers into the document. Alternatively, simply consult the AI output as instructional and edit the original document accordingly - this is often more efficient than checking every word of the AI output for unintentional changes, and ensures professional control over the output.

The human remains the final authority.

Why Local Privacy Software Is a Strategic Advantage

One of the most under-appreciated benefits of this workflow is model independence.

When privacy and AI are decoupled:

  • You can adopt the most cost-effective or powerful models at any time.
  • You avoid being locked into a single vendor's data policies, but still opt into the best available privacy settings for each service on top of your own local controls.

For example, professionals today can freely switch between Gemini 3 for reasoning-heavy synthesis and ChatGPT 5.1 for drafting and iteration while keeping the same privacy control plane in front of every interaction.

Privacy and AI Share the Same Core Weakness: Subjectivity

Both systems operate on statistical thresholds, even the near-deterministic patterns in privacy tools that still carry a confidence threshold for error, or certain prompts with "frozen" weights at inference time that can still be misinterpreted with other context.

  • Privacy tools must be reviewed for both false positives and false negatives, as well as the potential for aggregate de-anonymization risks and future risk vectors.
  • AI outputs must be reviewed for both accuracy and omissions, as well as the potential for latent errors or hallucinations that can propagate across drafts and future contexts.

Manual review is an essential habit to mitigate systemic risks and maintain professional accountability; the probabilistic nature of generative AI and designations of privacy are not changing.

When privacy tools and AI tools are combined correctly, the result is:

  • Faster iteration without exposure, broad model choice without data lock-in, better drafts without surrendering authorship, and stronger compliance without sacrificing speed.

AI and privacy tools infer, estimate, and approximate. Professionals preserve what truly differentiates their work: their unique understanding of the context and intent of the data, and judgment.