Ethics Lost: When Investigators Become Criminals

The investigation of the Silk Road drug trafficking operation was meant to bring down the online black marketplace. Concurrently, it also exposed something more troubling: two of the agents entrusted with dismantling the criminal enterprise secretly abandoned their sworn code of ethics and committed serious crimes of their own. A Drug Enforcement Administration (DEA) special agent and a U.S. Secret Service special agent used their access to undercover operations, digital evidence, and cryptocurrency accounts to enrich themselves, obstruct justice, and betray the public trust.

Their misconduct, eventually uncovered and prosecuted, highlights not only personal corruption but also systemic vulnerabilities in how digital evidence and virtual currency are handled. Even as the government pursued the operators of Silk Road, it had to turn inward and prosecute its own investigators for extortion, money laundering, and related offenses. The story of these two agents is a cautionary tale about the risks that arise when powerful investigative tools meet weak internal controls. 

Background: Silk Road and the Baltimore Task Force

Silk Road operated as an online marketplace where users could buy and sell illegal drugs and other contraband, typically using bitcoin or other digital currencies to conceal their identities and the flow of funds. The site’s creator, Ross Ulbricht, operated under the alias “Dread Pirate Roberts” and became the central target of a broad federal investigation into the platform’s activities and infrastructure. Federal authorities viewed Silk Road as a pivotal test case for enforcing drug and financial laws in the emerging world of dark‑web markets and cryptocurrencies.

To meet that challenge, the government formed the Baltimore Silk Road Task Force, a multi‑agency team that included the DEA, Secret Service, FBI, IRS‑Criminal Investigation, and other components. The task force relied heavily on undercover personas, digital forensics, and the management of bitcoin wallets used in controlled transactions with Silk Road. Agents had to navigate both traditional investigative techniques and the technical demands of tracing, seizing, and safeguarding virtual currency. In that environment, two agents, Carl M. Force and Shaun W. Bridges, saw opportunities for personal gain.

The DEA Agent: Carl M. Force

Assignment and Undercover Role

Carl M. Force was a veteran DEA special agent with roughly 15 years of service when he joined the Baltimore Silk Road Task Force around 2012. In that role, he operated a sanctioned online persona known as “Nob,” a supposed criminal intermediary who communicated directly with Ross Ulbricht. Through “Nob,” Force was authorized to engage with Ulbricht, gather intelligence, and orchestrate controlled transactions on Silk Road. His position gave him direct access to Ulbricht’s trust and to significant amounts of digital currency passing through the investigation.

As “Nob,” Force’s communications with Ulbricht were part of the official investigative strategy. However, the same access that enabled law‑enforcement actions also gave Force the ability to manipulate information, solicit payments, and divert funds if he chose to disregard the rules. That is ultimately what he did, blurring the line between undercover work and personal profiteering.

Extortion Schemes and Covert Personas

Using his “Nob” persona, Force offered Ulbricht purported inside information about the government’s investigation and other services that would be valuable to someone operating an illicit marketplace. He represented that he had access to sensitive law‑enforcement data and that he could provide Ulbricht with updates and warnings about the case. In exchange, Force obtained bitcoin payments that, under the law and the terms of the investigation, constituted government property and evidentiary funds.

Force did not stop at the sanctioned persona. He created a second, unauthorized online identity known as “French Maid,” which he did not disclose to his superiors. Under this covert persona, he again approached Ulbricht and offered investigative information in return for payment, this time positioning “French Maid” as an independent source within law enforcement. Through “French Maid,” Force secretly solicited and received additional bitcoin—amounting to hundreds of thousands of dollars’ worth—outside any approved investigative plan. 

These schemes effectively turned the undercover operation into a vehicle for extortion. Ulbricht believed he was paying law‑enforcement insiders for protection and intelligence; in reality, he was paying a corrupt agent who was concealing the transactions from prosecutors and fellow investigators.

Misuse of Position at a Digital Currency Exchange

Force’s misconduct was not confined to the Silk Road undercover environment. Without DEA authorization, he took on an outside role as the chief compliance officer for CoinMKT, an online digital currency exchange. In that capacity, he leveraged his status as a DEA agent to pressure the company and its customers, while also positioning himself to control accounts with substantial holdings.

At one point, Force directed CoinMKT to freeze accounts containing approximately 337,000 dollars in cash and digital currency on the asserted basis of law‑enforcement authority. He then transferred roughly 300,000 dollars’ worth of the digital currency from those accounts into an account he personally controlled. By doing so, he turned a purported enforcement action into a direct act of theft. His dual roles—public agent and private compliance officer—created a glaring conflict of interest and a pathway to misappropriation.

Obstruction of Justice and Sentencing

When questions arose about his conduct, Force compounded his wrongdoing by obstructing justice. He admitted that he lied to federal prosecutors and other agents investigating his activities, attempting to conceal the bitcoin he had received from Ulbricht and the funds he diverted from CoinMKT. His false statements and omissions hindered efforts to understand what had happened to significant amounts of digital evidence and government property.

Ultimately, Force pleaded guilty to charges including extortion under color of official right, money laundering, and obstruction of justice. He was sentenced to a term of imprisonment—reported as 78 months—along with an order to pay substantial restitution and to serve a period of supervised release after completing his prison term. His case demonstrated that an agent’s lengthy tenure and prior service record offered no protection from prosecution when the evidence showed deliberate abuse of authority.

The Secret Service Agent: Shaun W. Bridges

Role in the Silk Road Task Force

Shaun W. Bridges served as a special agent with the U.S. Secret Service and was also assigned to the Baltimore Silk Road Task Force. His responsibilities included handling aspects of the digital evidence and virtual currency involved in the investigation. That role gave him access to bitcoin wallets, transaction records, and accounts associated with Silk Road and its users, including funds seized or controlled as part of the government’s operations.

In a complex investigation where virtual currency played a central role, Bridges’s technical responsibilities placed him in a position of significant trust. He had both the knowledge and the technical capability to move funds, reconfigure accounts, and initiate transfers under the cover of legitimate investigative needs—conditions that can be exploited when oversight is weak.

Theft and Laundering of Digital Currency

Bridges used his position to steal large quantities of digital currency that had been placed under government control. In an earlier case, he pleaded guilty to charges of money laundering and obstruction of justice for diverting more than 800,000 dollars’ worth of bitcoin that he accessed through his official duties. Those bitcoins were government property and critical pieces of evidence, but Bridges treated them as personal assets.

To conceal his theft, Bridges engaged in money‑laundering tactics designed to obscure the origin of the funds. He transferred the digital currency through various accounts, including intermediaries and exchanges, with the goal of breaking the audit trail that would tie the funds back to seized Silk Road assets. Such conduct undermined both the integrity of the investigation and the evidentiary chain‑of‑custody for digital assets.

Additional Proceedings and Sentencing

After his initial guilty plea and sentencing—a 71‑month prison term—Bridges became the subject of further scrutiny as additional transactions and accounts came to light. In a subsequent proceeding, he pleaded guilty to another count of money laundering related to the handling of digital currency connected to the Silk Road investigation. This second case reflected a broader pattern of misconduct rather than a single isolated incident.

Bridges thus faced multiple criminal judgments, each reinforcing the conclusion that he systematically abused his access to digital currency and investigative tools. The repeated proceedings also underscored how difficult it can be to fully reconstruct the flow of virtual assets once a trusted insider has manipulated the records and moved funds through multiple channels.

Parallels Between the Two Cases

The cases of Carl M. Force and Shaun W. Bridges share striking similarities. Both men were embedded in the same task force, dealing with the same investigation, and handling many of the same digital assets and undercover operations. Both used government‑controlled bitcoin wallets and online personas as gateways to personal enrichment, rather than as tools solely for evidence gathering and law enforcement.

In each case, the agents treated digital currency that was clearly government property—funds seized, controlled, or received during official operations—as if it belonged to them. They routed bitcoins into accounts they personally controlled, attempted to disguise the transfers, and failed to report the funds to prosecutors or supervising agents. Their schemes were not merely opportunistic; they involved deliberate planning, exploitation of investigative tools, and concealment. 

Both Force and Bridges also engaged in deception when their conduct came under scrutiny. Force lied to federal prosecutors and colleagues, while Bridges implemented money‑laundering tactics and obstructed justice to hide the origin and destination of the stolen funds. Their behavior demonstrates how insider misconduct can compromise not only financial integrity but also the broader pursuit of justice in high‑profile cases.

Institutional Response and Oversight

The exposure of these crimes did not happen by accident. Multiple investigative bodies became involved in uncovering the misconduct, including the FBI’s San Francisco Division, IRS‑Criminal Investigation, and internal oversight units such as the Department of Justice Office of the Inspector General and the Department of Homeland Security Office of Inspector General. Their efforts were critical in identifying irregularities in digital‑currency movements and in following the trail back to the agents responsible.

Prosecution of the cases was handled by the Department of Justice’s Criminal Division, including the Public Integrity Section, in coordination with the U.S. Attorney’s Office for the Northern District of California. That allocation of responsibility underscores how seriously the government treats corruption by its own officials, particularly in matters that involve complex financial systems and high‑impact investigations. The institutional response demonstrated that even within law‑enforcement circles, misconduct can lead to significant criminal penalties when properly investigated. 

These events also highlighted the importance of internal controls and external oversight in operations involving digital assets. Without the involvement of inspector general offices and specialized investigative teams, irregularities in bitcoin transfers and account manipulations might have gone unnoticed or remained unexplained. The eventual prosecutions show that oversight mechanisms can function effectively, but they also raise questions about how early warning signs might have been missed.

Vulnerabilities in Digital‑Evidence Handling

The Force and Bridges cases expose particular vulnerabilities in how digital evidence—especially cryptocurrency—is managed within law‑enforcement operations. When a single agent or a small group of agents can initiate transfers, create or control wallets, and operate undercover personas with limited real‑time oversight, the risk of misappropriation increases dramatically. Digital currency is both easily movable and, if handled through anonymizing tools or layered transactions, difficult to trace after the fact.

In the Silk Road investigation, agents controlled multiple bitcoin wallets and accounts used for undercover purchases, seizures, and storage of evidence. Those accounts needed to be both accessible for operational purposes and secured against misuse. However, the cases show that access control alone was not sufficient. Agents could exploit their technical knowledge and the trust placed in them to move funds without immediate detection, then alter records or provide misleading explanations later. 

The technical complexity of cryptocurrency systems also creates challenges for auditors and supervisors who may not share the same level of expertise as frontline agents. If supervisors lack detailed understanding of blockchain transactions, wallet management, and exchange interfaces, they may rely heavily on the reporting of the very agents they are tasked with overseeing. That knowledge gap can delay detection of irregular transfers or inconsistencies in reported balances. These factors suggest that robust oversight in digital‑currency investigations requires both technological competence and structural safeguards, such as multi‑signature wallets, independent reconciliation of blockchain records, and periodic external audits.

Ethics, Public Trust, and Consequences

When law‑enforcement officers commit crimes under cover of official investigations, the impact extends far beyond the immediate financial losses. Cases like those of Force and Bridges erode public confidence in the fairness and integrity of the justice system. Members of the public may question whether evidence has been handled properly, whether prosecutions are impartial, and whether other instances of misconduct remain undiscovered.

In the context of the Silk Road prosecution, defense teams and observers have pointed to the agents’ misconduct as a complicating factor in evaluating the overall fairness of the process. Even when courts determine that the core evidence against a defendant remains strong, knowledge that investigators stole funds or lied to prosecutors can alter the public narrative and fuel skepticism about high‑profile convictions. The integrity of digital evidence is especially critical in cases that depend heavily on complex technical records and financial trails.

There are also direct victims beyond the abstract concept of public trust. The government itself, as the custodian of seized assets and evidence, suffered losses when digital currency was diverted into private accounts. Individuals whose accounts were frozen under questionable pretenses, as in the CoinMKT episode, may have experienced significant financial harm. The consequences of such misconduct therefore include both institutional damage and concrete harm to specific parties. 

Lessons and Policy Recommendations

The misconduct of Force and Bridges has prompted broader reflection on how to safeguard
investigations from insider abuse. One key lesson is the need for stronger internal controls over virtual assets. Mechanisms such as multi‑signature wallets, in which multiple authorized parties must approve any transfer, can reduce the risk that a single agent can move funds undetected. Regular reconciliation of blockchain records against internal logs, conducted by personnel independent of the investigative team, can also help identify anomalies early.

Another lesson concerns conflicts of interest and outside engagements. Force’s unapproved role as a compliance officer at a digital‑currency exchange illustrates how external positions can create powerful incentives to misuse law‑enforcement authority. Clear, enforced prohibitions on unapproved outside financial roles—especially in industries related to an agent’s investigative portfolio—are essential. Agencies may need to update their ethics policies and training to address the specific risks posed by digital‑asset markets and emerging financial technologies. 

Finally, the cases underscore the value of strong oversight units within the justice system. Proactive monitoring of high‑risk operations, targeted audits of digital‑asset handling, and robust whistleblower protections can all contribute to early detection of misconduct. As technology evolves, oversight structures must evolve with it, ensuring that the power to investigate complex crimes is matched by equally sophisticated mechanisms to prevent and detect corruption.

Conclusion

The story of Carl M. Force and Shaun W. Bridges is a reminder that even those tasked with enforcing the law can succumb to the temptations created by new technologies and powerful investigative tools. In the midst of the effort to dismantle Silk Road and hold its operators accountable, two agents abandoned their sworn ethics, exploited their unique positions for personal gain. Their crimes—extortion, money laundering, obstruction of justice, and theft of government property—undermined the integrity of the investigation and public confidence in the institutions.

Yet the eventual detection, prosecution, and sentencing of these agents also demonstrate that accountability is possible. The challenge for leaders, policymakers and agencies is to learn from these cases and implement oversight and safeguards that reduce the opportunity for similar misconduct in future investigations. As online markets and virtual assets continue to evolve, the integrity of those who investigate them—and the systems that oversee their work—will be just as important as technical expertise in ensuring that justice is done.

References

Department of Homeland Security, Office of Inspector General. (2017, August 15). Former Secret Service agent pleads guilty to money laundering [Press release]. U.S. Department of Homeland Security. https://www.oig.dhs.gov/news/press-releases/2017/08152017/former-secret-service-agent-pleads-guilty-money-laundering

U.S. Department of Justice, Office of Public Affairs. (2015, October 19). Former DEA agent sentenced for extortion, money laundering and obstruction of justice related to Silk Road [Press release]. U.S. Department of Justice. https://www.justice.gov/archives/opa/pr/former-dea-agent-sentenced-extortion-money-laundering-and-obstruction-related-silk-road

U.S. Department of Justice, Office of Public Affairs. (2015, August 31). Former Secret Service agent pleads guilty to money laundering and obstruction of justice [Press release]. U.S. Department of Justice. https://www.justice.gov/archives/opa/pr/former-secret-service-agent-pleads-guilty-money-laundering

U.S. Department of Justice, Office of Public Affairs. (2015, March 30). Two former federal agents charged with bitcoin theft and related crimes [Press release]. U.S. Department of Justice. https://www.justice.gov/opa/pr/two-former-federal-agents-charged-bitcoin-theft-and-related-crimes

U.S. Department of Justice, Office of Public Affairs. (2015, February 4). Former Secret Service agent pleads guilty to money laundering [Press release]. U.S. Department of Justice. https://www.justice.gov/archives/opa/pr/former-secret-service-agent-pleads-guilty-money-laundering

National Security Archive. (2019, December 12). Silk Road: Investigation of a DEA and Secret Service agents’ involvement with an online black market site [Electronic briefing book]. The George Washington University. https://nsarchive.gwu.edu

AI Prompts to Help Identify and Correct False-Positive "Hallucinations" and Assist with APA Compliance

Some AI services are notorious for confidently providing false-positive "hallucinations". 

Your mission as a writer is to identify and correct those AI issues, avoid plagiarism and bolster your academic integrity.

Students:

Below is a long list of prompts that should be used in AI to check your work.

· Copy/paste all of the prompts below into your AI resource.

 

· Attach the draft file version of your work with the prompts.

· Let AI review the prompts with the draft file.

· Review, verify and correct any errors or issues that AI check reveals. 

Chatrie v. United States: Implications for Law Enforcement

Core holding and immediate implications

In Chatrie v. United States (2026), the Supreme Court held that police officers conduct a Fourth Amendment “search” when they obtain detailed cell‑phone location data, such as Google’s Location History, via geofence warrants, because individuals have a reasonable expectation of privacy in records of their physical movements even if those records are held by a third‑party company (Kagan, 2026; The Guardian, 2026). The Court concluded that this applies even when the time window is short and the data is obtained from a technology provider, and remanded for the Fourth Circuit to evaluate whether the particular geofence warrant at issue was reasonable in terms of probable cause and particularity and how the good‑faith exception to the exclusionary rule applies (Kagan, 2026; Justia, 2024).

Practically, this means that law enforcement must treat geofence‑based access to smartphone location data as a constitutionally significant search, with warrant requirements and exceptions similar to those recognized in Carpenter v. United States for cell‑site location information (Kagan, 2026; Paul, Weiss, Rifkind, Wharton & Garrison LLP, 2026).

Ramifications for police and law enforcement practices

Geofence warrants as high‑scrutiny tools

Geofence warrants compel a provider like Google to disclose location data for every device estimated to be within a defined geographic area during a specified time frame, typically through a multi‑step process that begins with anonymized device data and proceeds to disclosure of subscriber identities (Kagan, 2026; American Civil Liberties Union, 2026; Paul, Weiss, Rifkind, Wharton & Garrison LLP, 2026). The Court’s opinion emphasizes the breadth of such warrants and their potential to resemble “general warrants,” particularly when they encompass residences, churches, schools, and hospitals within the radius (Kagan, 2026; American Civil Liberties Union, 2026).

For police practice, this implies:

• Agencies will need to narrow the geographic scope, time window, and criteria for device selection at each step of a geofence warrant to withstand probable‑cause and particularity scrutiny (Kagan, 2026). 
•  Internal review by legal advisors or prosecutors before seeking geofence warrants will likely become standard to reduce the risk of suppression and civil liability (Paul, Weiss, Rifkind, Wharton & Garrison LLP, 2026).

Limits on short‑term and “targeted” location requests

The government had argued that accessing two hours of Location History data around a crime scene should be treated differently from long‑term tracking; the Court rejected this distinction, observing that even short‑term monitoring can reveal highly sensitive movements, such as visits to medical, legal, or political locations (Kagan, 2026). The opinion also states that Fourth Amendment protections do not hinge on the quantity of information obtained once the category of information is protected, and that the ability to select limited time slices from a comprehensive database does not diminish the constitutional intrusion (Kagan, 2026).

Consequently, law enforcement cannot justify bypassing warrant requirements solely by limiting the duration of location data requested, and must be prepared to show probable cause and necessity even for relatively narrow time windows (Kagan, 2026; The Guardian, 2026).

Restriction of the traditional third‑party doctrine

The Court explicitly declines to apply the traditional third‑party doctrine from United States v. Miller and Smith v. Maryland to Location History data, echoing its earlier approach in Carpenter (Kagan, 2026). It reasoned that location records are “qualitatively different,” uniquely revealing, and not truly “voluntarily shared” in the conventional sense, because modern smartphone use and repeated prompts to enable Location History make the generation of such data a pervasive feature of daily life rather than a discrete business transaction (Kagan, 2026; Paul, Weiss, Rifkind, Wharton & Garrison LLP, 2026).

For police practice, this indicates that subpoena‑like tools directed to providers for historical smartphone location data will generally be constitutionally inadequate absent a warrant, and agencies must distinguish carefully between traditional business records and digital diaries of movement (Kagan, 2026; American Civil Liberties Union, 2026).

Multi‑step digital warrants and judicial oversight

The geofence warrant in Chatrie used a three‑stage process: (1) anonymized data for all devices in the geofence during the one‑hour window; (2) extended movement data inside and outside the geofence for selected devices; and (3) disclosure of identifying information for a further subset of devices (Kagan, 2026; Justia, 2024). Justice Jackson’s concurrence stresses that steps two and three were not subject to detailed judicial standards in the warrant and allowed officers to broaden the search and de‑anonymize users without additional judicial findings of probable cause, which she characterizes as granting a “roving commission” inconsistent with warrant requirements (Jackson, 2026).

Police departments will therefore need to ensure that:

• Warrants explicitly define the criteria for narrowing devices at each stage and the basis for moving from anonymized data to identifying information (Kagan, 2026; Jackson, 2026).
•  Magistrates, not officers alone, determine the scope and sequence of data access, potentially by requiring new or amended warrants for subsequent stages in complex digital searches (Jackson, 2026).

Exigent circumstances and recognized exceptions

The Court notes that its holding does not foreclose warrantless access to location data in true emergencies, consistent with existing exigent‑circumstances doctrine (Kagan, 2026). However, the opinion suggests that such situations must involve compelling needs of law enforcement and be narrowly tailored to the emergency at hand, which implies that routine investigations will rarely qualify (Kagan, 2026; American Civil Liberties Union, 2026).

Ramifications for prosecutions and criminal cases

Suppression litigation and the good‑faith exception

In the underlying federal case, the district court and the Fourth Circuit both upheld admission of the geofence‑derived evidence based on the good‑faith exception, even while expressing serious concerns about the warrant’s breadth and constitutional validity (United States v. Chatrie, 2024; American Civil Liberties Union, 2026). Justice Alito’s dissent in the Supreme Court emphasizes that many past cases involving geofence warrants are likely to survive suppression challenges because officers relied on warrants in an unsettled legal environment (Alito, 2026).

For prosecutors, this means:

• Past convictions based on similar geofence warrants will likely be defended by arguing that officers acted in objective reliance on judicially issued warrants and pre‑Chatrie case law (United States v. Chatrie, 2024; Alito, 2026). 
•  New investigations after Chatrie will face tighter standards; the ability to invoke good‑faith will weaken over time as the constitutional limits become settled and widely known (Alito, 2026).

Strategic use of location evidence

Because the Court describes Location History as akin to a personal journal—similar to emails, documents, photos, and calendars stored in the cloud—such evidence will attract strong privacy‑based challenges and may be perceived by juries as intrusive surveillance (Kagan, 2026; American Civil Liberties Union, 2026). Prosecutors may respond by using geofence‑derived information primarily to generate leads and corroborate other evidence, rather than as the sole or central proof of identity or guilt (Paul, Weiss, Rifkind, Wharton & Garrison LLP, 2026).

Broader implications for policing and surveillance

Shift away from dragnet surveillance techniques

The Court’s reasoning, along with advocacy from organizations such as the ACLU and EFF, signals skepticism toward investigatory methods that start with an area and time and then identify potential suspects from the entire population present, rather than focusing on specific individuals for whom probable cause already exists (American Civil Liberties Union, 2026; Electronic Frontier Foundation, 2026). This has implications not only for geofence warrants but for other “reverse” techniques, such as reverse keyword searches and broad social‑media data pulls.

Law enforcement agencies are likely to:

• Emphasize suspect‑specific investigative methods and use reverse‑location or reverse‑keyword tools only when narrowly tailored and backed by strong justifications that can satisfy courts under Chatrie’s framework (Paul, Weiss, Rifkind, Wharton & Garrison LLP, 2026). 
•  Develop minimization procedures to discard information about non‑suspects quickly after initial screening whenever reverse‑search methods are used (American Civil Liberties Union, 2026).

Data architecture and provider practices

Google has indicated in its Supreme Court filings and public statements that, as of mid‑2025, it moved Location History storage from centralized servers to user devices and no longer maintains data in a form that would allow responding to geofence warrants for Location History (Kagan, 2026; Bloomberg Law, 2026). Commentary notes that other technology and telecommunications companies may consider similar approaches, such as on‑device storage and shorter retention periods, to reduce exposure to broad law‑enforcement demands and privacy criticism (Paul, Weiss, Rifkind, Wharton & Garrison LLP, 2026).

This evolution will force police and prosecutors to:

• Rely more heavily on carrier‑held cell‑site data (still governed by Carpenter) and on traditional device searches, rather than cloud‑based geofences (Kagan, 2026; Bloomberg Law, 2026). 
•  Maintain ongoing dialogue with provider legal and compliance teams to understand what data exists, how it is stored, and what legal processes can realistically reach it (Paul, Weiss, Rifkind, Wharton & Garrison LLP, 2026).

References

American Civil Liberties Union. (2026, March 4). United States v. Chatrie. ACLU. https://www.aclu.org/cases/united-states-v-chatrie

Alito, S. A. (2026). Dissenting opinion in Chatrie v. United States, 609 U.S. ___ (No. 25‑112). (Included in slip opinion PDF attached by the Court Reporter.)

Bloomberg Law. (2026, April 26). Supreme Court weighs warrants tied to phone location data. Bloomberg Law. https://news.bloomberglaw.com/us-law-week/justices-weigh-legality-of-warrants-tied-to-phone-location-data

Electronic Frontier Foundation. (2026, March 2). Brief of Amicus Curiae Electronic Frontier Foundation in support of petitioner, Chatrie v. United States (No. 25‑112). Electronic Frontier Foundation. https://www.eff.org/files/2026/03/02/chatrie-v-us-eff-scotus-brief.pdf

Jackson, K. B. (2026). Concurring opinion in Chatrie v. United States, 609 U.S. ___ (No. 25‑112). (Included in slip opinion PDF attached by the Court Reporter.)

Justia. (2024, July 8). United States v. Chatrie, No. 22‑4489 (4th Cir. 2024). Justia. https://law.justia.com/cases/federal/appellate-courts/ca4/22-4489/22-4489-2024-07-09.html

Kagan, E. (2026). Opinion of the Court in Chatrie v. United States, 609 U.S. ___ (No. 25‑112). (Slip opinion, October Term 2025, as provided in 25‑112_0am4.pdf.)

Paul, Weiss, Rifkind, Wharton & Garrison LLP. (2026, February 2). Supreme Court to address constitutionality of geofence warrants for the first time. Paul, Weiss Publications. https://www.paulweiss.com/insights/client-memos/supreme-court-to-address-constitutionality-of-geofence-warrants-for-the-first-time

The Guardian. (2026, June 29). US supreme court rules geofence warrants require constitutional privacy protections. The Guardian. https://www.theguardian.com/us-news/2026/jun/29/supreme-court-geofence-warrants-case-decision

United States v. Chatrie, 590 F. Supp. 3d 901 (E.D. Va. 2022). (District court decision discussing geofence warrant, Fourth Amendment, and good‑faith exception.)

Perplexity AI. (2026). Perplexity AI system documentation and capabilities (GPT‑5.1). Perplexity AI. https://www.perplexity.ai (general product and system information page).

 

Use of Artificial Intelligence (Perplexity AI)

Artificial Intelligence, specifically Perplexity AI (powered by GPT‑5.1), was used to assist in assembling, synthesizing, and summarizing the information above. The AI system accessed, read, and integrated content from the official Supreme Court opinion in Chatrie v. United States, lower‑court decisions, and reputable secondary analyses (news, advocacy organizations, and law‑firm commentaries). The final text was then structured to conform to APA‑style in‑text citations and references.

IoT and Stolen Cars: Thieves May Use Tech to Grab Your Ride

The 10 Most Stolen Vehicles in America in 2025

Vehicle theft declined in the United States in 2025, but several models remained frequent targets because of their popularity, availability, and persistent appeal to thieves (National Insurance Crime Bureau [NICB], 2025; Car and Driver, 2026). According to NICB data, the most stolen vehicle in America in 2025 was the Hyundai Elantra, followed by the Honda Accord and the Hyundai Sonata (NICB, 2025; Car and Driver, 2026).
Ranked Vehicles 

 

The 10 most stolen vehicles in America in 2025 were the following:

• Hyundai Elantra (21,732)
• Honda Accord (17,797)
• Hyundai Sonata (17,687)
• Chevrolet Silverado 1500 (16,764)Honda Civic (12,725)
• Kia Optima (11,521)
• Ford F-150 (10,102)
• Toyota Camry (9,833)
• Honda CR-V (9,809)
• Nissan Altima (8,445)

 

Theft Trends

NICB reported that U.S. vehicle thefts experienced a decline in 2025, yet the same report still identified specific models that were stolen far more often than others. NICB specifically stated that the Hyundai Elantra remained the most stolen vehicle model in 2025 with 21,732 thefts, followed by the Honda Accord with 17,797 thefts and the Hyundai Sonata with 17,687 thefts (NICB, 2025).

Car and Driver’s report on the NICB figures confirms the same ranking and shows that the list included a mix of sedans, pickups, and one SUV, suggesting that theft risk in 2025 was not limited to one body style or market segment (Car and Driver, 2026). The presence of the Chevrolet Silverado 1500 and Ford F-150 on the same list as the Honda Civic, Toyota Camry, and Nissan Altima shows that both high-volume passenger cars and high-demand trucks remained attractive targets (Car and Driver, 2026). 

Internet of Things

Modern connected vehicles increasingly function like Internet of Things systems because they rely on wireless connectivity, cloud services, software-driven features, and communication between in-vehicle components and outside networks (Trend Micro, 2021). Trend Micro explains that connected cars use technologies such as 5G, cloud-connected applications, over-the-air updates, and vehicle-to-network communication, all of which expand what vehicles can do while also increasing cybersecurity exposure (Trend Micro, 2021).

Trend Micro also notes that the modern connected car is becoming similar to a “smartphone-on-wheels,” with applications communicating through middleware, gateway electronic control units, and cloud services. These systems can introduce risks such as denial-of-service attacks, man-in-the-middle attacks, hijacking of services, data privacy issues, authentication and management issues, incorrect data, and misconfiguration problems (Trend Micro, 2021). As the costs and resale values for car computers, collision avoidance systems, and other tech devices increase, thieves now also disassemble vehicles to steal those items.

Radio frequency interception and transmission devices are sometimes used to capture the codes transmitted by key fobs on some vehicles. The codes are then used to reprogram devices that the thieves emply to unlock, start, and steal a vehicle. 

This means connected technology can create additional pathways that thieves or cybercriminals may try to exploit. If remote services, app connections, or cloud-linked vehicle functions are not properly secured, the same connectivity designed for convenience may also increase opportunities for unauthorized access or interference (Trend Micro, 2021). 

Prevention Methods

NICB advises vehicle owners to use layered theft-prevention practices rather than relying on a single measure. Its prevention guidance recommends removing keys or fobs from the vehicle, locking doors and windows, parking in well-lit areas, and using anti-theft technology such as steering wheel locks, audible alarms, and aftermarket tracking devices (NICB, n.d.).

These recommendations support the use of steering wheel locking devices, alarm systems, and location-tracking tools as practical deterrents. They also fit well with additional protective measures such as ignition kill switches or fuel-system shutoff devices, which are commonly used to make a stolen vehicle harder to start or move, although the verified NICB source specifically highlights locks, alarms, and tracking devices rather than detailing those other devices individually (NICB, n.d.).

For connected vehicles, owners must  recognize that digital security matters alongside physical security. Because connected cars increasingly depend on cloud services, APIs, and networked vehicle systems, keeping manufacturer software current and treating app-based vehicle access cautiously are sensible precautions consistent with the risks described by Trend Micro (Trend Micro, 2021). 

References

Car and Driver. (2026, March 23). What cars were stolen most last year? The top 10 may surprise you. https://www.caranddriver.com/news/a70833130/top-10-most-stolen-cars-2025/

National Insurance Crime Bureau. (2025). U.S. vehicle thefts experience historic decline. https://www.nicb.org/news/news-releases/us-vehicle-thefts-experience-historic-decline

National Insurance Crime Bureau. (n.d.). Prevent vehicle theft. https://www.nicb.org/prevent-vehicle-theft

Trend Micro. (2021, February 15). Connected cars, 5G, the cloud: Opportunities and risks. https://www.trendmicro.com/en_us/research/21/b/connected-cars-5g-the-cloud-opportunities-and-risks.html

IoT Internet of Things: Checklist for First Responders and Investigators

1. Initial safety and legal checks

• Confirm scene safety (weapons, hazards, live electricity, gas, fire, chemical risks). interpol
• Verify legal authority: warrant, consent, exigent circumstances; note any device that may be altering or deleting data in real time (e.g., cameras, cloud‑connected devices). ojp
• Limit unnecessary handling of electronic/IoT devices until guidance from a digital evidence specialist is obtained. nij.ojp

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2. Global scan for IoT indicators

• Look for network infrastructure: wireless routers, mesh nodes, range extenders, cellular hotspots, powerline network adapters. swgde
• Identify hubs and bridges: smart home hubs (e.g., branded home automation boxes), Zigbee/Z‑Wave hubs, security system control panels, smart TV boxes, game consoles. ojp
• Note voice assistants and smart speakers: cylindrical or puck‑shaped devices with microphones and LEDs, often near kitchens, living rooms, bedrooms, or offices. nij.ojp

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3. Exterior and perimeter (near the scene)

• Survey exterior for cameras: doorbell cameras, floodlight cameras, bullet/dome cameras under eaves, in trees, or on fences; check neighboring properties with line‑of‑sight. swgde
• Look for vehicle‑related IoT: connected vehicles, aftermarket GPS trackers under bumpers/dash, OBD‑II plug‑in devices, dashcams, telematics boxes in fleet or rental vehicles. interpol
• Note infrastructure and environmental sensors: smart meters, irrigation controllers, connected thermostats on exterior walls, access control panels, smart locks and gates. ojp

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4. Interior premises – obvious IoT

• Smart TVs and streaming boxes: TVs with network ports or Wi‑Fi, streaming sticks/boxes near HDMI ports or power outlets. nij.ojp
• Security and automation: alarm keypads, wireless motion sensors, door/window sensors, glass‑break sensors, smart locks, garage door openers, smart light switches and bulbs. swgde
• Voice/video devices: smart displays, nanny cams, baby monitors, intercoms, talking toys, pet cams or feeders that connect via Wi‑Fi. nij.ojp

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5. Interior premises – less obvious IoT

• Household appliances: smart refrigerators, ovens, microwaves, washing machines, dryers, robotic vacuums, smart air purifiers, connected HVAC thermostats and vents. swgde
• Health and fitness IoT: smart scales, connected blood pressure cuffs, glucometers, pulse oximeters, pill dispensers, CPAP/BiPAP machines with Wi‑Fi/cellular modules. interpol
• Other embedded devices: smart plugs, power strips, light strips, smart picture frames, connected coffee makers, smart blinds/curtain controllers. nij.ojp

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6. On the victim and suspect – body‑worn IoT

• Wearables: smartwatches, fitness bands, smart rings, body‑worn GPS trackers, health monitoring patches or pendants. interpol
• Medical devices (if present and safe to handle): insulin pumps, neurostimulators, cardiac devices with companion hubs, fall‑detection pendants; coordinate with medical personnel before seizure. interpol
• Clothing and accessories: Bluetooth‑enabled headphones, smart glasses, smart helmets, connected work gear, key fobs for vehicles with telematics apps. swgde

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7. Personal devices that control IoT

• Smartphones and tablets: these often serve as the main controller for home or vehicle IoT; identify all phones and tablets in the environment. ojp
• Laptops and computers: desktops, laptops, mini‑PCs, and NAS devices that may run automation software or store logs/video from IoT devices. nij.ojp
• Remote controls and dedicated controllers: proprietary handheld controllers for drones, alarm systems, garage doors, home automation, and industrial equipment. interpol

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8. Network and connectivity information to document

• Network identifiers: SSID names seen on labels of routers, mesh nodes, or written on notes; any visible default passwords or QR codes for Wi‑Fi setup. swgde
• Hardware identifiers: photograph and record make, model, serial number, and MAC address for routers, hubs, cameras, and other IoT devices. ojp
• Connectivity types: note whether devices use Wi‑Fi, Ethernet, cellular, Bluetooth, Zigbee, Z‑Wave, LoRa, or proprietary RF; photograph any external antennas or gateway boxes. interpol

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9. Quick documentation at the scene

• Overall scene: wide photographs and video showing locations of IoT devices relative to key areas (entry points, victim, suspect, evidence). nij.ojp
• Device close‑ups: power state, status lights, display screens, connected cables, network labels, ports, and any visible notifications or alerts. ojp
• Configuration clues: screenshots or photos of posted passwords, QR codes, written router settings, printed user manuals, or quick‑start guides left near devices. swgde

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10. Handling and seizure considerations (high‑level)

• Do not power off or disconnect IoT devices until consulting with a digital forensics point of contact, unless necessary for safety (fire, shock, life‑threatening risk). crime-scene-investigator
• Preserve volatile data when authorized and trained personnel are available: consider photographing live screens and indicators before any power change. interpol
• Package IoT devices carefully: label power supplies, cables, and associated controllers; avoid stacking items that may damage small sensors or alter switches. crime-scene-investigator

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11. Questions first responders should answer for investigators

• What IoT‑capable devices are present, where are they located, and who appears to control or own them (victim, suspect, third party, business)? nij.ojp
• What networks are visible (names, apparent ISP, presence of guest networks, visible extenders or hotspots)? ojp
• Are there neighboring or third‑party devices (next‑door cameras, commercial systems, vehicle telematics, employer‑owned devices) that might capture relevant data or logs? swgde

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12. Information to capture for follow‑up subpoenas/warrants

• Account‑level info: usernames, email addresses, phone numbers, and service provider names visible in device interfaces or paperwork. interpol
• Service providers: identify cloud platforms (e.g., camera, home automation, health, or vehicle OEM services) linked to devices and note any visible subscription info. nij.ojp
• Time references: capture any device timestamps, time‑zone settings, or indications of last sync/last activity visible on screens at the scene. ojp

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Technology in Public Administration and Justice Studies

OECD: ‘institutional incoherence’ undermining digital ambitions

The Organisation for Economic Co-operation and Development (OECD) Digital Government Outlook report finds that governments have made progress in building digital capabilities, but a gap remains between ambition and actual performance. The core issue is “institutional incoherence,” meaning digital initiatives exist but are not well connected, coordinated, or reinforced across government systems.

The Five Key Challenges

The report highlights five challenges that explain why progress stalls:

• Data systems that cannot be shared: Weak data foundations and poor interoperability prevent effective data reuse across agencies.
• Infrastructure that is built but not adopted: Governments invest in digital platforms that are underused due to fragmentation or lack of integration.
• Investments that are approved but not evaluated: Funding decisions are made, but follow-through, performance tracking, and adaptability are weak.
• AI that is deployed but not governed: AI adoption is widespread, but governance (risk assessment, audits, oversight) is inconsistent and underdeveloped.
• Services that are designed but not connected: Digital services exist but are not integrated into seamless, user-centered systems.

A common thread across all five is lack of coordination—individual components exist, but they do not work together effectively.

Other Important Findings

• Data strategy gaps: While 94% of countries have data strategies, many function as policy statements rather than operational tools. Data quality and long-term, cross-border planning remain weak.
• AI governance imbalance: Governments often have “guardrails” (rules) but lack “enablers” (tools and infrastructure), which slows innovation and adoption in high-impact areas like policymaking.
• Digital identity unevenness: Most countries have governance structures, but adoption and integration vary widely. Trust, usability, and inclusion remain barriers.
• Skills shortage: Only 17% of countries have a dedicated digital skills strategy for public servants, limiting the ability to execute digital transformation.
• Investment rigidity: Governments struggle with inflexible funding, weak evaluation mechanisms, and procurement systems not suited to modern digital development.

Bottom Line

The OECD’s central message is that digital transformation is no longer about building new tools—it is about making systems work together coherently. Without this alignment, even well-funded and well-designed initiatives will underperform.

Implications

For justice systems and public administration, these findings carry particular weight. Fragmented data, disconnected services, and weak interoperability can directly affect access to justice, case management, law enforcement, and public trust. Digital transformation in these sectors must prioritize integration, accountability, and user-centered design—not just technology deployment.

Reference

Aldane, Jack. (6, June 2026). OECD: ‘institutional incoherence’ undermining digital ambitions https://www.globalgovernmentforum.com/oecd-warns-institutional-incoherence-undermining-members-digital-ambitions/

Infographic by Perplexity AI
 

IoT: Growth and Projections

IoT crossed the trillion‑dollar threshold: 

Recent analyses value the IoT industry at 1.3 trillion dollars in 2026, with 20–25 billion connected devices worldwide. One research firm projects the dedicated IoT market segment growing from about 547 billion dollars in 2025 to around 865 billion dollars by 2030.

Device counts are rising steadily: there were about 16.6 billion connected IoT devices at the end of 2023, growing to an estimated 18.8 billion by the end of 2024 and around 21.1 billion by the end of 2025, with forecasts of roughly 39 billion devices by 2030. In industrial and infrastructure settings, IoT is expected to contribute up to 14.2 trillion dollars to the global economy by 2030.

Sources: 
https://iot-analytics.com/number-connected-iot-devices/
https://fabrity.com/blog/industrial-internet-of-things-iot-trends-for-2026/

 

Infographic from Notebook LM Google

Infographic from Perplexity AI

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https://kardasz.blogspot.com/2026/06/iot-growth-and-projections.html 

AI: Executive Order: Promoting Advanced Artificial Intelligence Innovation and Security

The June 2026 executive order on “Promoting Advanced Artificial Intelligence Innovation and Security” doubles down on AI‑enabled cyber defense while signaling that existing criminal statutes will be used against AI‑enabled cybercrime. For law enforcement and digital forensics, it turns AI into both a critical investigative tool and a clearly identified aggravating factor in computer crime prosecutions. whitehouse

Why this matters for policing and forensics

• The order frames AI as a strategic asset that must be protected through stronger cyber defenses rather than broad new AI‑specific criminal statutes. connectontech.bakermckenzie
• Instead of creating new AI crimes, it directs DOJ to treat AI as a means or modality—prioritizing enforcement of existing laws when AI is used to breach systems, steal data, or facilitate other offenses. whitehouse
• This approach preserves doctrinal continuity for prosecutors and courts while still addressing AI‑enabled intrusion, fraud, and data exfiltration scenarios. lawfaremedia

Upgrading systems: more digital evidence, more complex cases

• The order requires rapid cyber‑hardening for national security, defense, and civilian systems and pushes agencies to deploy AI‑enabled defensive tools at scale. bankingjournal.aba
• CISA is instructed to issue Binding Operational Directives and guidance to accelerate protection of civilian federal systems and to expand programs that make AI‑powered cybersecurity tools available to agencies, state and local governments, and critical infrastructure operators. bankingjournal.aba
• For investigators, that means: more pervasive logging, anomaly‑detection telemetry, and machine‑generated alerts that become discoverable evidence in intrusion, fraud, and insider‑threat cases. exterro

Practical implications for digital forensics:

• Expect more cases where the key artifacts include model‑generated alerts, AI‑driven risk scores, and automated incident‑response decisions—each of which may need to be interpreted and explained in reports and testimony. europol.europa
• AI‑assisted intrusion‑detection systems will increasingly shape timelines (first detection, escalation, containment), which must be carefully preserved and correlated with traditional host and network logs. marymount

AI cybersecurity clearinghouse: a new intelligence feed

• The Treasury Department, working with NSA and CISA, is directed to establish an “AI cybersecurity clearinghouse” in voluntary collaboration with the private sector and critical infrastructure operators. airia
• This clearinghouse will coordinate vulnerability scanning, vulnerability validation, remediation prioritization, and patch distribution—using AI to scale detection and response. connectontech.bakermckenzie

For law enforcement and forensics:

• Shared vulnerability intelligence and patching timelines will strengthen attributions around “known exploited vulnerabilities,” which can be important in proving negligence, willful blindness, or deliberate exploitation of disclosed flaws. hstoday
• Coordinated AI‑driven scanning and remediation produce standardized logs and artifacts, which can improve cross‑case pattern analysis and make it easier to link intrusions that reuse the same techniques, infrastructure, or tooling. exterro

Frontier models and pre‑release access: opportunities and risks

• The order instructs Treasury, the Department of War (via NSA), and DHS (via CISA) to develop a classified benchmarking process that determines when an AI system is a “covered frontier model” based on advanced cyber capabilities. airia
• Developers can opt into a voluntary framework where government gets up to 30 days of early access to these high‑risk models under strict cybersecurity, confidentiality, and IP‑protection conditions. whitehouse

From an investigative and forensic perspective:

• Pre‑release access to powerful cyber‑capable models gives federal experts an opportunity to understand offensive capabilities before criminals weaponize them, potentially informing proactive threat models, signatures, and training sets for defensive tools. gigazine
• If criminals later use these models to automate discovery of vulnerabilities, generate payloads, or script lateral movement, law enforcement may be able to draw on government test data and red‑team findings when building expert testimony on foreseeability and risk. gigazine
• Because the framework is explicitly voluntary and the order rejects any mandatory licensing or preclearance regime, law enforcement should assume some highly capable models will remain opaque, increasing the value of independent forensic reverse‑engineering and open‑source intelligence. axios

DOJ enforcement focus: AI as an aggravating factor

• Section 4 directs the Attorney General to prioritize enforcement of 18 U.S.C. 1028 (identity documents and related fraud), 18 U.S.C. 1030 (Computer Fraud and Abuse Act), 18 U.S.C. 1343 (wire fraud), and other applicable federal laws against anyone who uses AI to illegally access or damage a computer, or who uses AI while engaged in such illegal access to further any other crime. lawfaremedia
• The order explicitly covers breaches of public or private systems and the use of AI “agents” to unlawfully access data or information that is then used for criminal purposes. aha

Implications for prosecutors and digital forensics:

• AI‑assisted activity (for example, using an LLM to generate exploit code or an agent to automate scanning and lateral movement) is now clearly within the enforcement crosshairs, even though the underlying charges are traditional CFAA, fraud, and identity‑crime counts. lawfaremedia
• Forensic reports that can distinguish between human‑authored and model‑generated scripts or communications—based on artifacts like API call logs, prompt histories, or distinctive code patterns—will become important in showing the role AI played in scale, sophistication, or concealment of the offense. marymount
• Demonstrating that a suspect deployed AI to industrialize attacks (e.g., mass credential‑stuffing, automated spear‑phishing, or high‑volume scanning) may help DOJ argue for higher culpability, upward departures, or leadership enhancements at sentencing. counciloncj

Evidence, chain of custody, and explainability

• As agencies deploy AI‑enabled defensive tools, more “evidence” will originate as model outputs: anomaly scores, automated risk classifications, and suggested response actions. exterro
• For digital forensic practitioners, this raises questions about validation, reproducibility, and explainability of AI‑driven detections used to justify warrants, arrests, or prosecutions. facebook

Key points:

• You will increasingly need to document not only what a system recorded, but also how its embedded models were configured at the time: model version, training/finetuning status, confidence thresholds, and any custom rules layered on top. marymount
• Chain of custody should explicitly include configuration snapshots of AI‑based tools, since model updates or retraining could change outputs later and undermine reproducibility of forensic claims. europol.europa
• When AI tools are used inside the lab (e.g., for triage, pattern detection, or media analysis), logs of prompts, parameters, and outputs should be preserved as part of the forensic record to allow peer review and cross‑examination. exterro

State and local implications

• The order explicitly calls out support for critical infrastructure such as community banks, rural hospitals, and local utilities, and directs agencies to facilitate access to AI‑enabled defensive tools for these entities. bankingjournal.aba
• For state, local, and tribal law enforcement, that means more investigations where small agencies face sophisticated, AI‑enabled intrusions into local critical infrastructure, but also more access to federal tools, threat intelligence, and training. hstoday

Digital forensics impact at the local level:

• Local cases (e.g., ransomware at a rural hospital or compromise of a small city’s SCADA network) are more likely to involve federal–local joint investigations, coordinated incident response, and shared evidence derived from federally provided AI‑security platforms. papers.govtech
• Harmonizing policies on log retention, evidence export, and privacy across federal and local systems will be increasingly important to avoid losing critical AI‑generated indicators in the gap between IT operations and criminal investigations. papers.govtech

What this means for your work

For law enforcement and digital forensics professionals, this order:

• Confirms that AI‑enabled intrusions and fraud will be prioritized for prosecution under existing federal laws, not a new AI‑criminal code. whitehouse
• Signals a sharp increase in AI‑generated security telemetry, alerts, and reports that must be preserved, validated, and explained as evidence. bankingjournal.aba
• Creates a new federal interface with frontier AI developers that can feed threat intelligence and expert knowledge into complex investigations involving advanced AI tools. airia
• Raises the bar on forensic methodology, requiring explicit consideration of how AI models used in both crime and defense affect the reliability, interpretability, and admissibility of digital evidence. facebook