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The California Court of Appeal, Second Appellate District, has issued its tentative ruling in Variety Media, LLC v. Superior Court, the closely watched writ proceeding that asks whether the pen register provisions of the California Invasion of Privacy Act (“CIPA”) apply to common website tracking technologies. The tentative decision would grant Variety’s petition in part and direct the trial court to sustain Variety’s demurrer with leave to amend. The court’s reasoning cuts in both directions. The panel would hold that CIPA’s pen register statute reaches internet communications, rejecting the threshold defense that has anchored many motions to dismiss. But it would also hold that a pen register captures only metadata identifying the destination of an outgoing communication, and that a website visitor’s IP address identifies the source of a communication rather than its destination. Under that construction, the complaint before the court fails to state a claim.

Background

Penal Code section 638.51 prohibits installing or using a pen register without a court order or the user’s consent. Over the past three years, plaintiffs and pro se litigants have filed hundreds (if not thousands) of lawsuits, arbitration demands, and pre-suit letters alleging that cookies, pixels, analytics tools, and similar technologies are unlawful pen registers because they collect visitors’ IP addresses and device information. Trial courts have divided on whether the statute, enacted with telephone surveillance in mind, reaches these tools at all. No California appellate court has answered the question in a published decision.

Continue Reading California Court of Appeal Tentatively Holds That Collecting a Website Visitor’s IP Address Alone Does Not Constitute Pen Register Activity Under CIPA
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Senior United States District Judge William H. Orrick, sitting in the Northern District of California, denied a motion to dismiss last week in an Automated License Plate Recognition (“ALPR”) matter, McGinty v. Reimagined Parking LLC, d/b/a Imperial Parking.[1] Judge Orrick held that the plaintiff plausibly alleged actionable harm based on his “right to know” about the use of ALPR systems in two garages. The order follows Bartholomew v. Parking Concepts, Inc.[2] and the guidance of Mata v. Digital Recognition Network, Inc.,[3] concluding that the California Supreme Court would likely recognize violation of a consumer’s “right to know” as actionable harm under California’s ALPR law if presented with the question.

The decision is significant because it marks the first time a federal judge adopted the state-court trajectory created in Bartholomew and Mata. Bartholomew treated the alleged failure to make required ALPR disclosures in an ALPR Privacy Policy as an actionable injury to a consumer’s right to know under the law. Though Mata did not deal with the failure to present an ALPR Privacy Policy, the decision in Mata did offer further guidance on how courts should analyze asserted ALPR harms.

Continue Reading Federal Court Follows Bartholomew Reasoning in Denying Motion to Dismiss ALPR Lawsuit
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On July 20, 2026, the California Court of Appeal, Fourth Appellate District issued a notable decision in Mata v. Digital Recognition Network, Inc.,[1] which addresses the standing requirements for private claims under California’s Automated License Plate Recognition (“ALPR”) law. At the heart of the decision lies the question whether a violation of the ALPR statute with no articulated resulting harm other than the subjective belief that the plaintiff’s privacy has been invaded confers standing to sue under the ALPR law.  The California Court of Appeal said no.

The Allegations

Mata alleges that Digital Recognition Network (“DRN”) collected and stored license plate images and related date, time, and location information from vehicles in public places. However, it was undisputed by the parties that DRN actually had a ALPR privacy policy in place and made the policy publicly available. Rather, Mata alleged that DRN’s implemented privacy policy was done “to maintain the appearance of adhering” to the law and that DRN “does not really mean what it says in the policy.” It was further undisputed that Mata’s ALPR data was never accessed without authorization, was only ever accessed by his own attorneys in connection with the litigation, and he suffered no physical or monetary harm.  Instead, Mata’s alleged harm theory was what he called a “collection-based invasion of privacy” harm – namely, the harm he suffered arose from the very collection of ALPR data that the ALPR law specifically permits.

The Court’s Conclusion: Actual Harm is Required

Looking to the statutory text and the legislative history of the ALPR law, the Court held that Mata’s alleged harm was not enough to confer standing. In so doing, it agreed with Bartholomew v. Parking Concepts, Inc.,[2] where the Fifth Appellate Division of the California Court of Appeal “rejected the plaintiff’s argument ‘that harm results from any violation of the ALPR law’ and held that standing ‘require[s] harm beyond a mere statutory violation.” Conversely, in dicta it indicated skepticism towards Bartholomew’s holdingthat the absence of a publicly displayed ALPR policy was sufficient to confer standing under a “right to know” theory of harm at the pleading stage. Significantly, however, the Mata court did not reach that question as one of the key differentiators between the matters was that DRN undisputedly implemented and displayed its ALPR policy, while the defendant in Bartholomew allegedly did not have one posted at all.

Take Aways & Action Items

For companies that operate or use ALPR systems, Mata is a helpful standing decision, but not a catch-all or safe harbor. The ruling solidifies that being able to demonstrate an implemented ALPR privacy policy that is adequately publicly displayed will make it more difficult for plaintiffs to pursue claims under the law. Under Mata, plaintiffs will be forced to allege policy or procedural deficiencies connected to the ALPR law’s requirements. Plaintiffs will also need to demonstrate actual harm resulting from those violations, such as unauthorized access, improper use, inadequate security, over-retention, or other tangible consequences flowing from alleged noncompliance.

Companies using or receiving ALPR information in California should continue to treat ALPR compliance as a priority. Recommended steps include confirming whether the organization is an ALPR operator or end-user; maintaining a current, publicly available ALPR usage and privacy policy; reviewing vendor and customer agreements for appropriate limits on collection, access, use, sharing, retention, security, audit rights, and deletion; and documenting operational compliance through training, access controls, retention schedules, and incident response procedures. These facts may be central to defending future claims following the decisions in Mata and Bartholomew.

[1] Mata v. Digital Recognition Network, 2026 WL 2085579 (July 20, 2026)

[2] Bartholomew v. Parking Concepts, Inc., 118 Cal.App.5th 438 (Feb. 5, 2026)

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It has been a busy spring for data privacy in the Southeast. On April 17, 2026, Alabama Governor Kay Ivey signed the Alabama Personal Data Protection Act (HB 351). Weeks later, on May 11, 2026, Governor Kemp signed Georgia’s SB 111. There is an important caveat there: although the Senate-passed version of SB 111 carried the title “Georgia Consumer Privacy Protection Act,” the House substituted the bill’s entire text with unrelated amendments to the rural hospital tax credit. The Senate agreed to the substitute on April 2, and the version Kemp ultimately signed has nothing to do with consumer privacy. Legislative tracking services continue to display the original title, which has caused understandable confusion, but Georgia did not enact a comprehensive privacy law this session.

That leaves the Southeast with three states currently operating under a comprehensive privacy statute: Florida (in effect since 2024), Tennessee (in effect since 2025), and Alabama (taking effect in 2027). Georgia remains a state to watch, with sponsors expected to introduce a successor measure when the new General Assembly convenes in 2027. And in keeping with the national trend, each state’s “omnibus” law (or proposed law) takes a slightly different approach with qualifying thresholds and defined terms. This article provides a short summary of what businesses operating in the region need to know and what they should be working on today.

Who Is Covered: Three Enacted Laws and Three Thresholds (and a Note on Georgia)

The biggest difference among the three enacted statutes is the way each defines businesses that must comply.

Florida’s Digital Bill of Rights (FDBR), which took effect on July 1, 2024, has the narrowest scope by a wide margin. The FDBR imposes obligations on controllers with annual global revenue of more than $1 billion that also meet one of three additional criteria: derive 50% or more of annual revenue from selling online ads, operate a consumer smart speaker with an integrated virtual assistant, or operate an app store with at least 250,000 applications. By design, the majority of the FDBR’s controller obligations apply only to the largest tech and platform companies. As a practical matter, most Southern businesses will never need to worry about Florida’s controller obligations, though enforcement has now begun. The Florida AG’s October 2025 action against Roku is a useful reminder that the FDBR is no longer dormant for the companies that do qualify.

Continue Reading Southeastern Privacy Laws Taking Shape: Current and Upcoming Omnibus Laws for Alabama, Georgia, Florida, and Tennessee
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When Colorado enacted the first comprehensive state AI law in 2024, it imported the conceptual architecture of the EU AI Act: a risk-based regime built on duties of care, risk management programs, and impact assessments. Two years later, and within a matter of weeks, the state has dismantled that legislation. On May 14, 2026, Governor Jared Polis signed Senate Bill 26-189, which repeals SB 24-205 and replaces it with a disclosure-and-rights framework focused on automated decision-making technology (“ADMT”). The new framework takes effect January 1, 2027.

The substance of the rewrite has been well-covered already. Less examined is how Colorado got here, and what the speed and direction of the pivot signal for the rest of the state AI regulatory landscape. The new bill was introduced and signed within two weeks of its introduction. The Governor’s AI Policy Working Group did the heavy lift in advance: roughly six months of stakeholder consultation produced the draft framework released on March 17, 2026. But the final two-week sprint reflects pressure to land the rewrite before the original AI Act’s June 30, 2026 effective date and amid escalating federal headwinds.

The Federal Backdrop

On December 11, 2025, the White House issued an executive order (“EO”) titled, “Ensuring a National Policy Framework for Artificial Intelligence.” The EO directs federal agencies to challenge conflicting state AI laws through litigation and coordinated federal action, and urges development of a preemptive national framework. It specifically named Colorado’s AI Act as an example of a state law that, in the administration’s view, would compel AI systems to “produce false results in order to avoid a ‘differential treatment or impact’ on protected groups.”

Continue Reading Colorado’s AI Reset: Two Weeks, a White House Callout, and a Pivot Away from the EU Model
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Legal500 featured an article by Seyfarth partners Kathleen McConnell and Lauren Gregory Leipold, and associate Daniel Riley, “AI Governance In (and Beyond) Privacy: Regulatory Tensions in Automated Decision‑Making, the Digital Authenticity Crisis, and Restrictions on Professional Use.”

The piece, published as a part of the Legal500 Country Comparative Guides, examines the rapidly evolving legal landscape governing artificial intelligence and its intersection with privacy, consumer protection, employment law, and professional responsibility.

The article highlights how US AI regulation is emerging through a fragmented mix of state privacy laws, AI‑specific statutes, ethics rules, and intellectual property doctrines, creating significant compliance challenges for organizations deploying AI at scale. The authors outline three key regulatory fronts—automated decision‑making, synthetic content and digital authenticity, and profession‑specific governance—and emphasize the need for proactive, enterprise‑wide AI governance strategies that extend beyond traditional privacy compliance.

As McConnell, Leipold, and Riley explain:

“Organizations cannot rely on any single legal regime, whether privacy, cybersecurity, or professional ethics, to define the boundaries of responsible AI use.”

The full article is available here.

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The lesson from the PocketOS database deletion is not that agentic AI is dangerous. It’s about governance and controls.

You have probably seen some version of the headline by now: “AI Agent Deletes Company’s Entire Database in 9 Seconds.” It is a compelling story. But the headline, while technically accurate, obscures the far more important lesson buried in the details.

So what actually happened? PocketOS, a small SaaS company that makes software for car rental businesses, was using a popular AI-powered code editor running on Anthropic’s Claude Opus 4.6 model. The AI agent was tasked with resolving a routine issue in a staging environment. When it hit a credential mismatch, the agent decided on its own initiative to “fix” the problem by deleting a volume on Railway, the company’s cloud hosting provider. The agent found a password in an unrelated file and used it to execute a deletion command. Because of permissions made available to the agent and the way access to the infrastructure was configured, that single command using a password which was valid across all systems wiped both the production database and all associated backups.  

The agent, when asked to explain itself, produced what multiple outlets described as a “confession,” acknowledging it had violated its own safety instructions. The story has gone viral. The framing in most coverage puts the AI squarely at the center of the narrative: the agent “went rogue,” it “confessed,” it acted autonomously and destroyed a business. But the reports are not entirely accurate and usually miss the point.

Continue Reading The AI Didn’t Go Rogue. Guardrails Were Never There.
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Now in its sixth year, Seyfarth’s Commercial Litigation Outlook provides a clear view into the forces reshaping business disputes in 2026. This year’s analysis highlights a risk landscape defined by accelerating technological change, an increasingly fragmented regulatory environment, and growing economic pressures across multiple industries.

According to the Outlook, artificial intelligence is creating new categories of legal risk, from the challenges of authenticating AI‑generated content to navigating the use of algorithmic tools while courts and regulators rapidly reset expectations around emerging technology. At the same time, state‑level regulation continues to expand, particularly around non‑competes, privacy, and biometrics, creating a compliance patchwork that requires businesses to adapt strategies by jurisdiction. Coupled with elevated interest rates, rising debt, and post‑pandemic strain, especially in real estate, health care, and franchise sectors, the commercial litigation environment remains fluid, fast‑moving, and resistant to neat predictions. Against this backdrop, eDiscovery, information governance, and cybersecurity response functions play increasingly central roles in managing litigation risk and staying ahead of shifting expectations.

Authored by Jay Carle, Matthew Christoff, and Danny Riley, this year’s eDiscovery & Innovation article spotlights one of the most significant and fast‑moving risks in the discovery landscape: the rise of AI‑enabled notetaking and meeting‑summarization tools. As generative AI capabilities become embedded directly into videoconferencing platforms, these tools now routinely record meetings, create transcripts with speaker attribution, and auto‑generate summaries—often by default. The result is a sudden proliferation of new, unvetted records that can capture sensitive, strategic, or privileged conversations. The article warns that these tools exponentially increase the risk of inadvertent disclosure, while also creating evidentiary challenges when transcripts or summaries are later used to establish what was said, by whom, and with what intent.

The article also highlights that litigation risk is expanding beyond the developers of these tools to the organizations deploying them. AI notetakers raise overlapping consent, privacy, wiretap, and biometric concerns, and courts will increasingly scrutinize whether companies can demonstrate how meeting data was captured, stored, and controlled. As with prior waves of privacy litigation, the differentiator will be operational discipline: organizations that implement clear governance around meeting recording, restrict distribution of AI‑generated outputs, and define authoritative versions of records will be far better positioned to defend against disclosure missteps, authenticity disputes, and statutory claims.

Click here to download the 2026 Commercial Litigation Outlook.

Continue Reading The Changing Discovery Landscape: Takeaways from Seyfarth’s 2026 Commercial Litigation Outlook
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When Judge Jed Rakoff ruled in United States v. Heppner (S.D.N.Y. Feb. 17, 2026)  that documents a criminal defendant created through exchanges with Anthropic’s Claude platform weren’t protected by attorney-client privilege or the work product doctrine, the decision generated significant attention across the legal community. Many practitioners read that ruling as a sweeping statement: using AI tools waives privilege. While great for headlines, that is an overstatement of what Heppner actually holds, and the Warner case, which was decided a week earlier in the Eastern District of Michigan, shows why the distinction matters.

The Heppner Decision: Narrower Than It Appears
In Heppner, the trial judge ruled that documents a criminal defendant created through his own exchanges with Anthropic’s Claude platform and sent to his attorney afterwards were protected by neither attorney-client privilege nor the work product doctrine. The ruling rested on several specific facts. Heppner used a public consumer AI tool that explicitly disclaims providing legal advice and whose privacy policy authorizes data collection, model training, and disclosure to third parties including government authorities. He did so on his own initiative, without direction from his counsel. And the government had already seized the documents pursuant to a search warrant before the privilege question even arose.

On privilege, the court identified three independent deficiencies: Claude is not a lawyer, so there was no attorney-client communication; the platform’s terms defeated any reasonable expectation of confidentiality; and Heppner’s purpose was not to obtain legal advice from Claude, which disclaims that capacity. On work product, the court found the documents were not prepared by or at the direction of counsel and did not reflect counsel’s strategy. Judge Rakoff noted the analysis might differ if counsel had directed the AI use because the platform could then arguably function as an agent of counsel.

Most importantly, Heppner doesn’t hold that using AI tools automatically waives privilege. It holds that a non-lawyer querying a public AI tool which isn’t a lawyer and offers no confidentiality, doesn’t satisfy the foundational requirement for attorney-client privilege in the first place. Legal privilege requires confidential communication with a lawyer for the purpose of obtaining legal advice. Heppner is important and worthy of some attention, but it is not the final word on lawyers (and those acting at the direction of lawyers) and the content of AI prompts and results. There is still a lot more to analyze on an individual application and individual basis. But as a bottom line, if a party or witness is talking to a machine and not a lawyer, the privilege analysis doesn’t even get off the ground.

Warner: The Civil Counterweight
Look back one week. In Warner, a federal magistrate judge reached a different result in a civil case. A pro se party had used ChatGPT to prepare legal briefs in anticipation of litigation. When opposing counsel sought discovery of those materials, the court the court denied the request, holding the materials were not discoverable work product under Rule 26(b)(3) and independently not relevant or proportional under Rule 26(b)(1). Critically, the court also held that using AI didn’t waive work product protection, because AI tools are “tools, not persons,” and waiver requires disclosure to an adversary or in a way likely to reach one – a standard that AI use alone doesn’t meet. The court didn’t mince words with defense counsel either, stating that their “preoccupation with Plaintiff’s use of AI needs to abate” and agreeing with the plaintiff that the request was a “fishing expedition” that, if endorsed, “would nullify work-product protection in nearly every modern drafting environment, a result no court has endorsed.”

One key difference here involves civil procedure vs. criminal procedure rules. Rule 26(b)(3) protects materials prepared in anticipation of litigation by a party or its representative – it doesn’t require that a lawyer prepare the materials, only that they were created in anticipation of litigation. The pro se litigant’s use of AI fell squarely within that protection, and the court saw no reason to treat AI-assisted drafting differently from any other tool a litigant might use to prepare her case.

The Real Distinction: It’s Not the AI, It’s How You Use It
This is the critical point most commentary misses. Heppner and Warner reach opposite conclusions not because one case says AI can never be privileged while the other says it always is. They reach opposite conclusions because of the specific circumstances in which the AI tools were used and the materials were sought. In Heppner, a represented defendant used a public AI platform on his own initiative, without counsel’s direction, through a service whose terms disclaimed both legal advice and confidentiality. Those materials were then seized by the FBI pursuant to a search warrant. In Warner, a pro se litigant used AI as part of her own litigation preparation, and opposing counsel tried to compel production through a discovery request.

Mr. Heppner’s computer and AI activity information was already seized and in the hands of the government, while Ms. Warner was resisting a written discovery request for information in her possession, custody or control. The procedural context matters enormously, and lawyers discussing AI privilege need to know the circumstances under which the materials were created and how they ended up in dispute.

Extrapolating from Warner: Lawyers Using AI Tools
If a pro se party’s use of ChatGPT to prepare litigation materials qualified for work product protection in a civil case, the same logic should apply – and arguably applies even more strongly –  when a lawyer uses AI tools.  A lawyer directing the use of an AI tool as part of legal representation has more deliberation and control than a pro se litigant. As long as the materials are created in anticipation of litigation and not disclosed to an adversary, they should receive the same protection Warner afforded.

The use of the AI tool itself doesn’t waive privilege or work product protection. What matters is whether the materials are created in anticipation of litigation and kept confidential. This is the real area where practitioners need to focus, because waiver is a real concern.

The Real Risk: Public and Commercial AI Tools
There is genuine waiver exposure when using public or commercial-level AI tools. That’s because, as the Heppner decision went at lengths to mention, the platforms and their terms make it clear that user information is not private or secured, and users have no privacy guarantee. Essentially, when you input confidential client information into ChatGPT or similar consumer tools, you’re disclosing that information to a third party without any contractual protection or confidentiality agreement. If that information is later exposed through a data breach, logging, or litigation (like the ongoing OpenAI New York class action litigation that has resulted in preservation obligations for massive volumes of ChatGPT prompts and results for millions of users) you’ve potentially waived privilege through disclosure, not through the mere act of using an AI tool.

The distinction is crucial: using an AI tool doesn’t waive privilege. Disclosing confidential client information through an unsecured channel does.

Practical Implications
Lawyers and businesses using AI in their practice should focus on:

  1. Using enterprise AI tools or tools with explicit confidentiality agreements rather than public consumer tools.
  2. Implementing siloed or secure instances where AI interactions involving legal matters are segregated from general business operations.
  3. If AI is part of the litigation workflow, counsel should direct its use and maintain clear documentation that materials were created in anticipation of litigation, especially in civil matters where work product protections are broader.
  4. Not assuming that sharing AI outputs with counsel after the fact creates privilege. Heppner held that non-privileged materials don’t become privileged merely because they are later shared with an attorney. The time to protect information is before it enters the AI platform, not after.
  5. Avoiding disclosure of confidential client information to public AI platforms where you cannot control downstream use or exposure.
  6. Updating AI governance and acceptable use policies to specify which platforms are approved, what information may be entered, and what protocols apply when AI-generated materials touch on litigation, investigations, or regulatory matters.

The one undeniable realization in both decisions is that AI prompts and results are undeniably ESI, and therefore subject to preservation, civil discovery, criminal search and subpoena production.

Neither case ends the conversation about whether AI use is categorically safe or unsafe for privilege. It’s that privilege analysis turns on the same factors it always has: whether there’s a confidential communication with a lawyer for the purpose of obtaining legal advice, whether materials are created in anticipation of litigation, and whether confidentiality is maintained. The AI tool itself is neutral, and AI is not a lawyer – it’s a powerful technology, but it is still a technology application like Westlaw or Google or an email or text messaging platform. How you use it, who is using it, and why determines whether privilege applies. Then, assuming it IS privileged, the efforts you take to secure the content from publication or disclosure determines whether your privilege is waived.

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Introduction

Robotics and artificial intelligence are converging at an unprecedented pace. As robotics systems increasingly integrate AI-driven decision-making, businesses are unlocking new efficiencies and capabilities across industries from manufacturing and logistics to healthcare and real estate.

Yet this convergence introduces complex legal and regulatory challenges. Companies deploying AI-enabled robotics must navigate issues related to data privacy, intellectual property, workplace safety, liability, and compliance with emerging AI governance frameworks.

The Shift: Robotics as an AI Subset

Traditionally, robotics was viewed as a standalone discipline focused on mechanical automation. Today, robotics is increasingly powered by machine learning algorithms, natural language processing, and predictive analytics—hallmarks of AI technology.

This evolution raises critical questions for legal teams:

  • Who owns the data generated by AI-enabled robots?
  • How do we allocate liability when autonomous systems make decisions without human intervention?
  • What contractual safeguards should be in place when outsourcing robotics solutions to third-party vendors?

As robotics increasingly incorporates AI functionality, traditional contract structures for hardware procurement and service agreements require significant updates. This evolution introduces new risk categories that must be addressed through precise drafting and negotiation.

Continue Reading The AI-Driven Evolution of Robotics