Artificial Intelligence vs. Authentic Intelligence: Navigating the Pitfalls of AI in Modern Investigations

The promise of Artificial Intelligence (AI) has swept through the private investigation industry like a tidal wave. We are inundated with marketing pitches for tools that promise to automate background checks, write our reports in seconds, and find needles in digital haystacks with superhuman speed. For a profession that has historically traded on efficiency and information arbitrage, the allure is undeniable. Who wouldn't want an analyst that never sleeps, reads a million pages an hour, and costs twenty dollars a month?

However, as licensed professionals, we are not paid for speed; we are paid for accuracy, discretion, and admissibility.

The integration of Generative AI (GenAI) and Large Language Models (LLMs) into the investigative workflow represents the single greatest shift in tradecraft since the internet replaced the microfiche. However, it also introduces a minefield of liability that most agencies are ill-equipped to navigate. Unlike a database search that returns a "no record found" result when it lacks data, AI is designed to please. It is designed to complete the pattern. If the facts are missing, it will often politely, confidently, and disastrously invent them.

This is not a Luddite’s manifesto. AI is a powerful tool that, when used correctly, can enhance our capabilities. But we must recognize that the "easy button" comes with a hidden cost. We are witnessing the rise of a new threat vector in our industry: the reliance on probabilistic machines to do the work of deterministic professionals.

We explore the existential risks of AI in private investigations, from the shattering of attorney-client privilege to the fabrication of evidence. We outline how the modern investigator must adapt to survive the "Age of Synthetic Truth."

Part 1: The Confidentiality Breach (The "Public" Trap)

The first and most immediate danger to the private investigator is the misunderstanding of how AI models "learn."

In the traditional investigative model, data security was physical or server-based. If you had a case file, you locked it in a cabinet or saved it on an encrypted hard drive. You owned the data. You controlled the access.

Generative AI fundamentally changes this equation. When you type a query into a standard, public-facing Large Language Model (like the free versions of ChatGPT, Gemini, or Claude), you are not just using a calculator; you are effectively publishing that data to the model’s training environment.

The "Paste" Problem

Consider a common scenario: An investigator is working on a complex fraud case. They have obtained 500 pages of messy, disorganized witness transcripts. Looking to save time, the investigator copies the text and pastes it into an AI chatbot with the prompt: "Summarize the key inconsistencies in these statements and identify any mention of the offshore account in the Cayman Islands."

In that split second, three catastrophic things have potentially occurred:

1. Waiver of Privilege: If you are working under the direction of an attorney, your work product is privileged. However, privilege relies on the expectation of confidentiality. By voluntarily uploading sensitive case data to a third-party commercial server, whose Terms of Service often explicitly state that data may be used to train the model, you may have arguably waived that privilege. Opposing counsel could, in theory, subpoena the AI provider for the chat logs associated with your account, revealing your investigative strategy and raw findings.

   
   

2. The "Black Box" Retention: Even if you delete the chat history from your sidebar, the data may persist in the company’s training logs. Unlike a shredded document, digital data fed into a neural network contributes to the model's weights and parameters. While it is rare for a model to regurgitate a specific social security number verbatim to another user, it is not impossible. This phenomenon, known as "inference leakage," means that the secrets of your investigation are now mathematically embedded in a public tool.

   
   

3. Violation of Client Contracts: Most engagement letters and vendor agreements have strict clauses regarding data sharing. Using a public AI tool to process a client's proprietary data (financials, trade secrets, personnel files) is almost certainly a breach of contract, exposing your agency to liability far exceeding the retainer's value.

   
   

The "Enterprise" Mirage

Many investigators believe they are safe because they pay for a "Pro" or "Enterprise" subscription. While these tiers often offer better data privacy (e.g., "Zero Data Retention" policies), the burden of verification falls on you. Have you audited the settings? Did you manually toggle off the "Improve the model for everyone" setting buried in the data controls?

The takeaway is simple: If you wouldn't post the case file on a billboard, do not paste it into a chatbot.

Part 2: The Hallucination Engine (The Accuracy Crisis)

The term "hallucination" is a quaint anthropomorphism for what is essentially a statistical error. LLMs are not databases of facts. They are prediction machines. They do not "know" that the sky is blue; they know that in the vast corpus of human text, the word "blue" follows the word "sky" with a high statistical probability.

For a creative writer, this probabilistic generation is a feature. For a Private Investigator, it is a bug of catastrophic proportions.

The "Plausible Lie"

The danger of AI hallucinations is not that they are nonsensical; it is that they are plausible.

• Case Study (Hypothetical): You ask an AI to conduct a background check on "John Smith" in "Daytona Beach, FL." The AI scans its training data (which cuts off at a certain date) and finds a "John Smith" who was arrested for grand theft auto. However, it conflates two different John Smiths, or it misinterprets a news article about a victim named John Smith and labels him the perpetrator.

  
  

• The Result: The AI produces a coherent, well-formatted summary stating: "Subject John Smith has a documented history of vehicle theft (2019)." It looks real. It sounds professional. But it is entirely false.

  
  

If an investigator copies this finding into a Report of Investigation without retrieving the primary court document, they have committed libel.

The Forensic Genealogy Trap

For those of us in the heir searching and forensic genealogy space, the risks are even more subtle. AI tools are excellent at formatting family trees, but they struggle with the nuance of historical records.

• Gap Filling: When an AI encounters a gap in a lineage, for example, a missing marriage record between 1900 and 1910, it may "predict" a connection based on common surnames in the region to complete the tree. It doesn't "know" they are related; it just knows that statistically, people with these names in this town were often related.

  
  

• The Consequence: You may end up locating and contacting the wrong heirs, exposing the estate to litigation and your agency to reputational ruin.

  
  

The Citation Fabrication

Perhaps the most insidious habit of current AI models is the fabrication of sources. When pressed to provide a source for a claim, AI will often generate a URL that looks legitimate (e.g., www.courtclerk.org/records/case12345) but leads to a 404 error page. This is because the AI understands the structure of a citation, but it does not have access to the live internet to verify the link exists.

The Rule of Thumb: If you didn't see the primary document, the stamp on the deed, the signature on the affidavit, or the seal on the court order, it likely may not exist. AI is a signpost, never a destination.

Part 3: Algorithmic Bias & The "Minority Report" Risk

As PIs, we pride ourselves on objectivity. We follow the evidence wherever it leads. AI, however, follows its training data, which is inherently biased.

This becomes a critical liability issue when PIs use AI-driven tools for OSINT (Open Source Intelligence) profiling, social media scrubbing, or surveillance analysis.

The Feedback Loop of Bias

AI models are trained on the internet, a place rife with stereotypes, racism, and historical prejudices. When these models are asked to "flag risky behaviors" or "assess trustworthiness" based on social media activity, they often disproportionately flag individuals from marginalized communities.

• Automated Profiling: A PI running a background check for a corporate client might use a tool that assigns a "Reputation Score" to a candidate. If the tool penalizes the candidate for using slang, posting about political activism, or living in a specific zip code, the PI is effectively laundering discrimination through an algorithm.

  
  

The Regulatory Hammer: California’s ADMT

This is no longer just an ethical debate; it is a regulatory compliance emergency. As discussed in recent industry updates, the California Privacy Protection Agency (CPPA) is rolling out strict regulations on Automated Decision-Making Technology (ADMT) effective in 2026.

• The Impact: If you use an AI tool to help a landlord decide on a tenant, or an employer decide on a hire, and that tool uses biased logic to reject the applicant, you can be held liable. The excuse "the software told me to do it" will not hold up in court.

  
  

• The "Human in the Loop" Defense: To protect yourself, you must be able to demonstrate that a human being reviewed the raw data and made the final determination. The AI can be the retriever, but the human must be the judge.

  
  

Part 4: The Admissibility Nightmare (Courtroom Challenges)

The ultimate product of a private investigation is evidence that stands up in a court of law. AI is currently the greatest threat to the admissibility of that evidence.

Defense attorneys and opposing counsel are becoming increasingly savvy about the "AI Defense." They are filing Daubert motions (challenges to the scientific validity of evidence) whenever AI tools are suspected of being used.

The Chain of Custody Problem

Traditional chain of custody is linear: I took the photo, I saved the file, I printed the photo. With AI, the chain is broken.

• Video Enhancement: PIs often use software to "clean up" grainy surveillance footage or "clarify" audio. Old-school filters adjusted contrast and brightness. New AI filters use Generative Adversarial Networks (GANs) to reconstruct pixels that aren't there.

  
  

• The Argument: If you use AI to "unblur" a license plate, the AI is essentially guessing what the plate should look like based on millions of other license plates. It is creating new data, not revealing hidden data.

  
  

• The Ruling: A judge may rule that this is not a photograph of the suspect's car; it is a "synthetic image" created by a computer. The evidence is thrown out, the case collapses, and your credibility is destroyed.

  
  

The "Fruit of the Poisonous Tree"

If your lead generation was based on an AI hallucination, for example, an AI falsely linked a phone number to a suspect, leading you to surveil that suspect, any evidence you gather subsequently could be challenged as "fruit of the poisonous tree." If the probable cause was a hallucination, the resulting surveillance video might be inadmissible.

Professional Standard: We must be prepared to testify not just to what we found, but how we found it. If your answer to "How did you derive this conclusion?" is "The AI told me," you have already lost.

Part 5: Deepfakes & The War on Truth

While we worry about using AI, we must also worry about battling AI. The modern PI is now on the front lines of the "Deepfake War."

The PI as the Target (Pretexting 2.0)

The days of a pretext caller trying to smooth-talk a receptionist are evolving. Scammers are now using AI voice cloning to mimic clients, attorneys, or even family members.

• The Scenario: You receive a call from your "client." It sounds exactly like him. He’s frantic. He tells you the subject is moving now and you need to wire the retainer refund to a different account immediately, or he tells you to break protocol and enter a property.

  
  

• The Defense: We must implement "Challenge-Response" passphrases with our clients. Verbal verification is no longer sufficient proof of identity.

  
  

The PI as the Verifier

Conversely, this is a massive business opportunity. Clients are being blackmailed with AI-generated nude photos (sextortion) or fake audio recordings of them saying racial slurs.

• The New Service Line: PIs are increasingly being hired to prove a negative, to prove that a video is fake. This requires a high degree of technical competence. You cannot simply "look" at the pixels anymore; you need cryptographic verification tools and metadata analysis.

  
  

• Caution: Just as we shouldn't use AI to create evidence, we must be careful about using AI to detect deepfakes. AI detection tools are notoriously unreliable, often flagging real videos as fake and vice versa. Reliance on these tools without expert forensic analysis is a recipe for disaster.

  
  

Part 6: Actionable Best Practices (The Protocol)

So, how do we operate in this brave new world? We do not retreat; we adapt. We treat AI like a dangerous informant: useful, knowledgeable, but never to be fully trusted.

Here is a recommended Standard Operating Procedure (SOP) for the AI-aware agency:

1. The "Zero-Trust" Data Policy

• Rule: Never upload PII (Personally Identifiable Information)—names, SSNs, DOBS, addresses, case numbers—into a public or "free" AI model.

  
  

• Solution: Use "Anonymization" techniques. Replace "John Smith" with "Subject A." Replace "123 Main St" with "Location B." Ask the AI to analyze the pattern of the data, not the people in the data.

  
  

• Upgrade: If your agency has the budget, invest in "walled garden" AI instances (e.g., Microsoft Copilot with Commercial Data Protection) where the contract explicitly states your data is not used for training.

  
  

2. The "Sandwich" Method

Integrate AI into your workflow using the "Human-AI-Human" sandwich:

• Top Bun (Human): You define the scope, the strategy, and the specific query. You select the documents to be analyzed.

  
  

• Meat (AI): The AI performs the heavy lifting—summarizing the 500-page deposition, formatting the timeline, or translating the foreign language text.

  
  

• Bottom Bun (Human): You verify every single claim against the primary source. You click every link. You read the specific page cited. You own the final output.

  
  

3. The "No-Fly" List for AI

Establish clear policies on what AI can never do:

• No AI for Background Checks: Never ask an AI "Does this person have a criminal record?"

  
  

• No AI for Facial Recognition (without human verification): Never rely solely on a software match.

  
  

• No AI for Legal Conclusions: An AI cannot determine if a subject was "negligent" or "fraudulent." That is a legal conclusion reserved for the trier of fact.

  
  

4. Disclosure and Transparency

If you use AI to enhance audio or video, you must disclose this in your report.

• Sample Language: "Audio clarity was improved using noise-reduction software. No generative content was added. Original raw audio is preserved as Exhibit A."

  
  

Hiding the use of enhancement tools is the fastest way to get your evidence suppressed.

5. Vendor Vetting

Audit your sub-contractors and database providers. Ask them: "Are you using AI to generate these reports?"

If you buy a "Social Media Background Report" that claims to find every post a subject has ever made, it is likely using a scraper with a high error rate. You need to know the provenance of that data before you sell it to your client.

Conclusion: The Future Belongs to the Skeptic

The proliferation of Artificial Intelligence is paradoxically increasing the value of Authentic Intelligence.

In a world where anyone can generate a 50-page dossier in seconds, the commodity of "information" is becoming worthless. The premium product—the thing clients will pay top dollar for—is verification.

Clients do not hire Private Investigators to give them probabilities. They hire us to give them certainty. They hire us to stand in the witness box, look the jury in the eye, and say, "I saw this. I verified this. This is the truth."

An AI cannot take an oath. An AI cannot fear perjury. An AI cannot understand the weight of a ruined reputation or a lost child.

As we move forward into 2026 and beyond, let us embrace technology as a tool to sharpen our skills, not a crutch to replace our judgment. The pitfalls are deep, and the legal landscape is unforgiving. But for the investigator who remains vigilant, skeptical, and fiercely protective of the truth, there has never been a better time to be in the business of reality.