Every day, more people are reporting what looks like a plainclothes kidnapping: no uniforms, no badges, just men in regular clothes picking someone up—sometimes in courthouses or jails—and leading them away in unmarked vehicles. It’s unsettling. But what you’ve likely witnessed isn’t a scene from a thriller—it’s part of a growing and highly controversial practice: ICE using private security contractors like G4S to detain immigrants. In this post, we explore how this happened, why it’s legally questionable, and how communities are pushing back.

1. Who’s Really Making the Arrest?
By law, only ICE, CBP, or DOJ officers can carry out immigration arrests. But if you look online, it’s often unmarked operatives or private security contractors who are doing it—standing in for ICE without credentials or uniforms. Under 8 U.S.C. § 1357(a), nobody else is legally authorized.

Are These “Bounty Hunters”? No—but It Sure Feels That Way
Viral videos describe them as “bounty hunters grabbing immigrants in public,” but that’s misleading. Licensed bail agents (“bounty hunters”) work under state criminal law and have no authority over immigration arrests. What we’re seeing is private contractors—like G4S (now part of Allied Universal)—originally hired for transport or surveillance, but now often physically detaining people in public spaces.

ICE’s own internal messaging has warned that these contractors were performing “arrest-like activities”, entering real legal grey areas. (Solano v. ICE complaint (Feb 2021))

Legal Grey Zone: Why This May Be Flat‑Out Illegal
These aren’t just bad optics—they may break the law:

• No proper arrest authority: Private contractors aren’t federal law enforcement and cannot operate under 8 U.S.C. § 1357(a).
• Dangerous lack of due process: Detainees often receive no Miranda warning, no access to counsel, and no warrant—violating the Fourth and Fifth Amendments.
• Impersonation risk: When contractors imply they’re federal officers and detain people, they may break 18 U.S.C. § 912.
• Non‑delegation risk: The Constitution doesn’t allow federal arrest power to be delegated to private actors.

How Did We Get Here?
Starting around 2016, ICE began outsourcing detainee transport to private firms like G4S—especially in states with sanctuary policies. Contractors now sometimes arrest people days after jail release, without ICE agents visibly present. (AP News on ICE contracts and detention surge )

Legal Battles You Should Know About
Courts are pushing back—and slowly defining the limits:

• Solano v. ICE (Feb 2021): Pushed by the ACLU and Asian Law Caucus, this case demanded ICE stop contractor arrests in CA jails.
• Sept 2021: A judge refused to dismiss the case, noting ICE continued contractor arrests after internal concerns.
• July 2022 Settlement: ICE must only use ICE agents—not contractors—if arrests happen in L.A. or S.F. jails. (LA Times on settlement)

Any reforms won through these cases are geographically limited, and practices continue nationwide.
.
Plainclothes & Ruse Tactics
It's not just contractors—undercover ICE agents have started blending in during routine court and check-in operations.

In May 2025, several plainclothes agents detained at least four asylum-seekers at San Francisco’s immigration court—wearing badges but using unmarked vehicles while accompanied by G4S personnel. (San Francisco Standard)

These operations have been widely condemned as fear tactics that undermine due process. (Tennessee Courthouse Raid – Action5 News)

Why We Should All Care
When non-uniformed agents conduct high-impact detentions:

• Communities lose trust. Witnesses may avoid courts or aid out of fear.
• Everyday spaces become threatening. Jails, courts—even immigration check-ins feel dangerous.
• Even legal residents or citizens have been mistakenly targeted.
• Civil rights advocates describe these events as “shadow abductions” that attack constitutional guarantees. (Immigrant Defense Project)

Why Nothing Has Changed

• ICE maintains these contractors are simply “transporting,” not “arresting” — despite mounting evidence.
• Staffing and deportation quotas push ICE toward efficiency over legality.
• Contracts lack transparency, and FOIA can’t penetrate private agreements.

Solutions on the Table
To restore trust and legality, we need:

1. A national ban on contractor arrests—modeled after California’s legal wins.
2. Mandatory identification of all ICE agents—visible badges and plain-language notices.
3. Transparency mandates requiring ICE to report contractor use and operations.
4. Legal oversight expansion—extending California’s AB 937 protections nationwide.

What You Can Do
If you witness a suspicious detention:

• Ask calmly for credentials or a warrant.
• Record video/audio (observe local laws).
• Call local authorities or 911 if it appears illegal.
• Share your evidence with groups like the ACLU, Asian Law Caucus, or Immigrant Defense Project.

Support legal reforms like California’s AB 937 and urge your representatives to protect immigrant communities.

Final Thought
These are not random incidents—they’re part of a systematic shift toward outsourcing enforcement and operating in shadows. But if more individuals, lawyers, and communities speak up, push for transparency, and insist on constitutional integrity, we can shine a light on these practices—and curb them for good.

Sources

• Solano v. ICE Lawsuit (2021) – ACLU/Asian Law Caucus
• 8 U.S.C. § 1357 – Immigration Arrest Authority
• 18 U.S.C. § 912 – Federal Officer Impersonation
• ICE Settlement in California (2022)
• SF Standard Coverage of May 2025 Arrests
• Action5 News Coverage of Tennessee Taco Truck Incident
• Immigrant Defense Project - ICEWatch
• Asian Law Caucus
• ACLU of Northern California
• Share Your Story – IDP

​One of the first things I learned in high school computer class—back when we sat behind Atari 800s and green-screen Commodore PETs—was the simple but enduring truth of “GIGO”: Garbage In, Garbage Out. Feed a computer bad code or unreliable data, and you’ll get nonsense—or worse—back. That concept has stuck with me throughout my career in technology consulting, and I’m reminded of it now more than ever, watching the rise of generative AI [1].

As the father of three college-age kids, I’ve also had a front-row seat to the rise of AI-generated content in education. In the past few years, I’ve seen my kids’ papers flagged for “AI plagiarism” by tools like Turnitin, even when they hadn’t used AI at all. False positives have gone up, not down. And they’re not alone: Stanford researchers have shown that most AI detection tools produce biased and unreliable results, often penalizing non-native English speakers [2].

But this issue goes far beyond academia. The same kinds of unreliable input-output loops are creeping into business AI systems that we trust to make decisions, generate insights, or interact with customers. It’s worth asking: what happens when AI systems become just as prone to garbage outputs—not because of human error, but because the AI platforms themselves are degrading over time?

The Lifecycle of AI Decay
A growing number of technologists and users have observed a pattern in digital platforms that Cory Doctorow colorfully calls enshittification—a process where systems initially serve users well, then shift to prioritize monetization and control, ultimately degrading the user experience [3].

While not all AI platforms are headed there, there are some that follow a recognizable pattern:
​

1. Promising Start: Early models impress with accuracy and openness (e.g., GPT-3, Claude 1.0).
2. Monetization Phase: Access gets restricted or paywalled. Free users get throttled or lower-quality results.
3. Guardrails & Tuning: To avoid legal or reputational risk, platforms introduce filters, often opaque to users.
4. Ecosystem Lock-in: Companies get tied into proprietary stacks, where auditing AI decisions is difficult.
5. Degradation: Over-tuned models become bland, inaccurate, or biased. Trust erodes.

We’re already seeing proactive efforts from the major players. Salesforce has introduced its Trust Layer to control what goes in and out of LLMs in enterprise settings, aiming to preserve security and data lineage [4]. Anthropic is embedding legal and ethical boundaries into its models through Constitutional AI, a framework inspired by democratic safeguards [5]. Apple is reportedly building private, on-device AI systems with an emphasis on user control and data sovereignty [5]. And Google has taken steps to address bias and misinformation in its Gemini and Bard platforms, adding transparency tools and content verification methods [6].

​On the flipside, changes to OpenAI’s GPT models have triggered concerns about data quality, transparency, and trustworthiness [7]. But the risk isn’t limited to big names. Smaller AI vendors—especially those packaging commercial LLMs behind minimal safeguards—are already facing scrutiny. Italy’s antitrust regulator has launched an investigation into DeepSeek, a lesser-known AI firm accused of misleading users with hallucinated results and inadequate disclosures [8]. As these examples show, reputational fallout comes fast when systems are rushed to market without enough rigor.

What Can Go Wrong Already Has
There are now real-world examples of business AI going sideways:
​

• AI-generated resumes and job descriptions that reflect discriminatory biases from training data.
• Customer service chatbots giving outdated or legally risky answers (think hallucinated refund policies).
• AI-generated code that violates licensing terms or creates vulnerabilities.
• Marketing content that misquotes sources or fabricates statistics.

As Gartner notes, organizations that fail to validate AI outputs and establish clear accountability frameworks will see risk exposure increase sharply by 2026 [9].

What We Can Do About It
This isn’t a call to panic. It’s a call to remember what we already know:

• Scrutinize the Inputs: Know where your AI models get their training data and who is curating it.
• Monitor the Outputs: Validate important results. Human-in-the-loop processes still matter.
• Diversify Your Stack: Don’t overcommit to one vendor’s AI. Maintain flexibility to pivot.
• Document Assumptions: Keep track of model prompts, settings, and data lineage.
• Ask Questions: Not all AI strategies are as rigorous as they claim. Transparency is a differentiator.

We should also support efforts to standardize ethical AI practices—whether that’s through internal governance, emerging industry certifications, or broader coalitions like the AI Incident Database [10].

A Final Thought
AI isn’t magic. It’s code plus data plus business pressure. And unless we actively push back on the drift toward degraded, opaque, and lock-in-heavy models, we’ll see the same GIGO cycle play out—only this time with greater stakes.

I think it’s worth reflecting on what we, as professionals, owe our organizations and ourselves when it comes to building trustworthy AI. It’s not just about technical excellence. It’s about remembering that clarity, honesty, and context matter—even if the tools we use speak in fluent paragraphs.

Note!
I’m not a policy maker or an AI ethicist—but I’ve worked long enough in consulting, systems integration, and data-driven programs to know what can go wrong. My next post will explore what professionals like us can actually do to protect our organizations—and our clients—from the risks we’re just beginning to understand.

Sources Cited
[1] AI – Artificial Intelligence – at Davos 2024: What to Know – World Economic Forum (January 14, 2024)
https://www.weforum.org/stories/2024/01/artificial-intelligence-ai-innovation-technology-davos-2024/

[2] Turnitin admits frequent false positives when AI writing is below 20%. K12 Dive. June 7, 2023.
https://www.k12dive.com/news/turnitin-false-positives-AI-detector/652221/

[3] What Is AI Enshittification? A New Term for a Growing Problem, VentureBeat
https://venturebeat.com/ai/what-is-ai-enshittification-a-new-term-for-a-growing-problem

[4] Inside the Einstein Trust Layer – Salesforce Developers Blog
https://developer.salesforce.com/blogs/2023/10/inside-the-einstein-trust-layer

[5] How Anthropic’s “Constitutional AI” teaches values through model feedback, Anthropic News (Dec 15, 2022)
https://www.anthropic.com/news/constitutional-ai-harmlessness-from-ai-feedback

[6] Responsible AI: Our 2024 report and ongoing work, Google Blog https://blog.google/technology/ai/responsible-ai-2024-report-ongoing-work

[7] Is ChatGPT getting worse over time? Study claims yes—but others aren’t sure, Ars Technica https://arstechnica.com/information-technology/2023/07/is-chatgpt-getting-worse-over-time-study-claims-yes-but-others-arent-sure/

[8] IItaly regulator probes DeepSeek over false information risks, Reuters (June 16, 2025)
https://www.reuters.com/world/china/italy-regulator-opens-probe-into-chinas-deepseek-2025-06-16/

​[9] Gartner predicts 30% of generative AI projects will be abandoned after proof-of-concept due to poor data quality and lack of trust – AI Business, citing Gartner (August 1, 2024) https://aibusiness.com/automation/gartner-predicts-30-of-generative-ai-initiatives-will-fail-by-2025

​[10] AI Incident Database – a shared space to report, track, and learn from real-world AI failures and harms
https://incidentdatabase.ai

I didn’t set out to write a white paper. I set out to understand why I felt so damn uneasy. Maybe it started with seeing peaceful protestors met by armored vehicles. Maybe it was the endless chaos in Washington. Or maybe it was just me—an immigrant, veteran, and parent—wondering how much longer our institutions could bend before they break.

What began as frustration turned into research. What became research turned into structure. What emerged is now something I hope contributes meaningfully to the public record: a documented, reasoned critique of how executive power has drifted from constitutional constraint toward normalized chaos.


📄 Read the full white paper here!

Salesforce’s acquisition of Informatica isn’t just another data platform merger — it’s a big strategic bet on fixing a problem GTM teams have faced for years: bad data kills good AI.

We’ve all seen the promise of AgentForce, Einstein Copilot, and Salesforce Data Cloud. But what happens when those tools rely on siloed, dirty, untrusted data? You don’t get insight — you get noise.

Here’s what this acquisition really means:

• MuleSoft connects systems.
• Informatica curates and governs the data inside them.

Together, they offer a complete path from messy data to explainable AI.

Why it matters to GTM, alliances, and vertical strategy teams:

• Healthcare and financial services are in play.
  Informatica is already strong with payers and banks — not so much with providers. That’s whitespace we can explore.
• Total cost of ownership is rising.
  Salesforce isn’t cheap. Add Informatica, and enterprise licensing gets even more complicated. Mid-market will need thoughtful bundling.
• New selling plays are needed.
  This is more than a platform story. It’s a GenAI-readiness story. It’s a compliance story. It’s a data trust story.

💡 I’ve published a deeper white paper unpacking the GTM implications, integration gaps, industry angles, and risks — all from the perspective of someone who sells, supports and works closely within the partner ecosystem.

📄 Get it here 

Salesforce will define its roadmap in time — but as GTM leaders and partners, we have a chance to shape the conversation early. Let’s get ahead of the curve.