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Risks of Shared AI Workspaces and Confidentiality, Security, and Privacy Concerns

AI & Algorithms , ,
Traditionally, the relationship between a company and its outside advisors, law firms, consultants, and financial advisors has been governed by confidentiality agreements, attorney-client privilege, and codes of professional ethics. These agreements assure that these outside advisors have access only to the information necessary for the scope of the project. However, artificial intelligence is becoming a mainstay in these working relationships, dismantling that clear separation.  AI-powered productivity tools are increasingly deployed not just within a single organization, but across shared digital workspaces, the collaborative platforms where companies and their external advisors jointly draft documents, manage new projects, exchange data, and make decisions. This shift represents a fundamentally new risk landscape, one that most organizations and their advisors have not yet adequately mapped.  This post identifies the three primary risk categories that arise when AI enters these shared spaces and the key considerations to mitigate them.  

Risk 1: Confidentiality

When AI tools operate within a shared workspace, there are two primary threats to client confidentiality:  1) Cross-client training and model contamination, and  2) over-input of information.  

Cross-Client Training Model Contamination

Many AI tools learn continuously from user interactions. For example, if a law firm’s AI assistant is trained, even implicitly, on documents, queries, and outputs across multiple client engagements sharing a platform environment. In this case, client information can become embedded in the model’s behavior. The AI may begin surfacing language, structures, or strategic approaches drawn from one client’s confidential materials when assisting another.  This is an example of cross-client training contamination. 

Over-Input of Information

When processing the information above, AI tools may ask follow-up questions, or the user may want to include additional context and guidance for the tool. These prompts and the need for greater contextual clarity may drive users to input additional information, information that may not normally be shared or be strictly necessary for the task at hand. This could lead to AI tools being trained on, and potentially re-sharing, information that is not strictly necessary. 

Risk 2: Overexposure

AI processes operating across shared workspaces introduce a new failure mode: overexposure through automated workflow. When an AI agent is tasked with summarizing documents, preparing briefings, or surfacing relevant materials, it may draw on content from across the workspace without respecting the role-based and project-based permissions designed to contain that information.

Misconfiguration and Permission Gaps

AI tools in shared workspaces are typically configured by IT or platform administrators, not by the lawyers or compliance officers who understand the sensitivity of the underlying information. Permissioning structures that may be technically correct for human access often fail to account for how AI agents traverse and aggregate information. A consultant with project-scoped access to a workspace may, through the AI layer, receive synthesized summaries that draw on materials outside their authorized scope.

Role and Project Segmentation Failures

Even well-intentioned configurations can break down when AI tools are updated. For example, this could occur when team membership changes or when workspace structures evolve mid-engagement. Unlike a human employee who is subject to ongoing supervision, an AI system with broad access will continue operating at that level until it is explicitly restricted. The moment of overexposure may be difficult to trace, making the discovery of these failures especially challenging. 

Risk 3: Accountability

Who is Responsible when AI makes the decision? Professional service relationships often assign responsibilities clearly; for example, the lawyer is responsible for legal advice, the auditor for the audit opinion, and the consultant for the recommendation. These lines of responsibility are the foundation of malpractice liability, professional licensing, and regulatory compliance. However, AI tools make this division more complicated. 

The Absence of Auditable Decision Trails

Many AI tools used in professional services do not generate decision logs or explainable outputs in a meaningful sense. When a deal recommendation, a compliance conclusion, or a litigation strategy is influenced by an AI-generated analysis, there may be no record of what data the AI considered, what it weighted, or what it excluded. If the decision is later challenged in litigation, a regulatory proceeding, or a malpractice claim, the AI’s contribution cannot be reconstructed or audited.

Diffused Liability Across a Shared Platform

In a shared workspace involving the company, its law firm, its auditors, and potentially a technology platform provider, an AI-assisted error may have no clear owner. Did the AI fail because of a platform defect? Because the law firm configured it incorrectly? Because the company provided bad inputs? Because no human professional adequately reviewed the output? Engagement letters, platform terms of service, and professional liability policies may not be drafted to answer these questions.

Key Considerations in Light of these Risks

The risks described may be present in any organization that has extended its advisory relationships (law firms, consultants, and financial advisors, to name a few) into AI-enabled collaborative platforms. To minimize these risks, organizations may want to consider the following tips:  Consider…
  • Auditing shared platforms and tools currently used with outside advisors to identify any AI features, and map what data those features can access. 
  • Reviewing engagement agreements, NDAs, and platform terms of service for AI-specific confidentiality provisions. 
  • Assessing whether AI access controls in shared workspaces respect role-based and project-based information silos and construct limitations where they do not. 
  • Establishing AI decision-logging protocols with outside advisors, including requirements for human review and sign-off before AI-influenced advice is acted upon. 
  • Negotiating clear contractual allocation of liability for AI-related errors across the full advisory chain, company, advisors, and platform providers. 
  • Briefing executive leadership and the board on AI-specific risks in advisory relationships, particularly in regulated industries where privilege and data protection obligations are most acute. 
Establishing governance frameworks for AI early in advisory relationships may enable companies to reduce their own exposure and hold advisors accountable if one of the risks of use materializes. 
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What to know about CIPA and Shine the Light claims

What to Know About CIPA and Shine the Light Claims

Cybersecurity

Doing Business in California? What To Know About CIPA and Shine the Light Claims

  Blog Contributor: Madeline Yuki Gaudlitz, 2L at the University of Michigan Law School In recent months, companies operating in California have reported an increase in demand letters requesting damages for alleged violations under new and existing privacy laws. Under current data privacy legislation, companies can expect these claims to continue. Plaintiffs’ attorneys have relied on two statutes as a basis for their demands, the California Invasion of Privacy Act (“CIPA”) and California Civil Code § 1798.83 (“Shine the Light”).

What is CIPA?

Originally enacted in 1967 to “protect the right of privacy” of California residents, CIPA bans wiretapping, eavesdropping, or recording private communications. In recent years, Plaintiffs’ attorneys compared real-time consumer-tracking software embedded in companies’ websites to the type of behavior CIPA prohibits. In addition to imposing criminal penalties and fines of up to $10,000, the statute allows private individuals whose personal data has been intercepted by businesses to sue for $5,000 per violation.

Who does CIPA apply to?

CIPA may apply to:
  • Companies with consumer facing-websites or applications used by a California resident
  • Both companies that use these technologies in their consumer-facing website or application and third-party developers

What technologies may leave my company exposed under CIPA?

Potential CIPA liability may apply to a range of real-time consumer tracking technologies that are a standard part of website or application design, which may include:
  • Website analytics
  • Software developer kits
  • Third-party tracking pixels and software
  • Fingerprinting software
  • Application programming interfaces
  • Conversation intelligence software-as-a-service (SaaS)
  • Cookies and identity profiles
*Notably, CIPA is sensitive to the processes used to collect customers’ information. Likewise, Shine the Light may not apply to businesses that share information with third parties only for administrative or customer service purposes. To assess liability under these statutes, businesses may want to coordinate with third parties to ensure awareness of their own business practices and awareness and compliance under CIPA.

What is Shine the Light?

Originally enacted in 2003, the Shine the Light law was aimed at increasing customer awareness of how their personal information may be shared with third parties for direct marketing purposes. CIPA requires businesses to disclose their information-sharing practices upon request or allow customers to consent to information sharing. Failure to comply may result in a civil penalty of $500 per violation, and $3,000 if the violation is willful, intentional, or reckless.

Who does Shine the Light apply to?

The Shine the Light law may apply to: 1. For-profit companies with 20 or more full or part-time employees, 2. that collect personal information from California residents, and 3. that have shared customer information with third parties for direct marketing purposes 4. within the immediately preceding calendar year. Direct marketing may include spamming, telemarketing, or mail. Personal information may include name, address, e-mail address, telephone numbers, date of birth, medical or financial information, information about children, race, religion, occupation and education, and information about the transaction.

Best Practices

While these statutes impose distinct obligations, compliance may be able to be addressed by general practices that reflect their obligations to limit data collection and sharing of personal information. To work toward compliance, a company may consider:
  • Reviewing your company’s privacy policy to ensure that it accurately informs consumers in California of their privacy rights.
  • Clearly communicating your company’s privacy policy to consumers.
  • Ensuring that the consumer consents to the collection and sharing of personal information.

For CIPA

Regarding liability under CIPA, businesses may want to consider:
  • Reviewing your website or application design for features that collect personal information of users.
  • Coordinating with third party providers to ensure their awareness and compliance with CIPA risks and requirements.
  • If utilizing real-time tracking technologies, securing a consumer’s affirmative consent to data tracking.

For Shine the Light:

There are a couple of avenues that may limit risk under the Shine the Light Law:
  • Ensure that website or application design, physical store, or employees clearly disclose consumer data privacy rights.
  • Ensure that that website or application design allows consumers to actively and easily consent to personal information sharing.

OR

  • Maintain awareness of sales of customers’ personal information within the preceding year.
  • Establish a designated address–email, mail, or toll-free number–that customers may use to contact a business and request information about how their personal information is used.
  • Be prepared to disclose the types of information shared and the names and contact points for third parties that received or purchased the information within the preceding year within 30 days.

What’s Next?

In the coming years, we may see legislation that responds to the challenges CIPA claims pose to regular business operations in the digital age. SB 690 proposes an exception to CIPA liability for companies that use personal data for commercial purposes. However, the current status of this critical amendment is stalled. What we know now:
  • It will not be reconsidered until the 2026 legislative session, currently set to run from January 5-August 31, 2026.
  • Legislative history indicates that any exception would only apply to future cases, not currently pending claims or claims filed before the amendment is finalized.
  • Unanimous approval in the state senate may reflect policymakers’ concern with applying CIPA to commercial data collecting practices.
Ultimately, the amendment’s status is uncertain, but there is reason for companies to be optimistic about an eventual tapering down of CIPA claims. Despite this, businesses should remain cognizant of other regulations aimed specifically at digital data collection. Credit: Madeline Yuki Gaudlitz
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AI and Legal Privilege

AI and Legal Privilege: Updates from Federal District Courts

AI & Algorithms

AI and Legal Privilege: Updates from Federal District Courts 

US v. Heppner and Warner v. Gilbarco

“Chat, is our conversation protected?”  As usual, the answer may be “it depends.”

Highlights from two recent federal district court cases, US v. Heppner and Warner v. Gilbarco, provide different answers to this question. The learning? If you are using AI tools for legal-related matters, you should think twice before entering personal information or other case-related information.

United States v. Heppner

On February 17, 2026, the federal district court for the Southern District of New York found that neither attorney-client privilege nor the work product doctrine applied in protecting legal strategy materials that were generated using a public version of Claude. In its memorandum of reasoning, the court states its ruling “appears to answer a question of first impression nationwide: whether, when a user communicated with a publicly available AI platform in connection with a pending criminal investigation, are the AI user’s communication protected by attorney-client privilege or the work product doctrine?” The court answers with a resounding “no,” given the circumstances of the case. In Heppner, the court first ruled that the defendant’s conversations with AI were not covered by attorney-client privilege. This is because attorney-client privilege attaches with:
  1. Communications between a client and their attorney,
  2. which are intended to be, and were, kept confidential,
  3. for the purposes of obtaining or providing legal advice.
The court held that the AI-generated communications failed at least two, if not all three of these elements. Not only were the conversations not with counsel, but Heppner’s communications were not confidential because he used a public or consumer version of the Claude platform. The court notes that the platform’s privacy policy specifies that user inputs and outputs are used for training purposes, and that the platform reserves the right to disclose this information to third parties, including governmental regulatory authorities. In Heppner, the court also held that the work product doctrine also did not apply to the materials generated from the public or consumer version of Claude. This is because the work product doctrine requires that materials are prepared by or at the direction of counsel. Because these documents were not prepared by or on behalf of counsel, and did not reflect the defense counsel’s strategy, the court held the work product doctrine did not apply.

Warner v. Gilbarco

On February 10, 2026, the Eastern District of Michigan heard a similar – but not identical case – and found that the work generated by AI was attorney-client work product. In this case, the AI tools were used to prepare legal materials. However, in contrast to Heppner, the court reasoned that “ChatGPT (and other generative AI programs) are tools, not persons” and found that both the attorney-client privilege and work product doctrine apply. Although the court determined that sensitive information pertaining to the case was provided to ChatGPT, they found that this was not equivalent to a “voluntary disclosure to a third person,” which would ordinarily waive attorney-client privilege, did not apply. This is because the AI was not considered a third person. Additionally, the court found that work product waiver requires disclosure to an adversary or in a manner likely to reach an adversary. Because this was not found to be the case with the disclosure to ChatGPT, this doctrine was not waived.

Key Takeaways

Although these two similar cases come to different conclusions, it is important to note that they are not factually identical. It is also important to emphasize that these are early federal district court cases, and these matters of first impression are likely to evolve in the coming year. In the meantime, individuals (and other entities) using generative AI for legal advice should consider these cases and their outcomes. If you are planning on using generative AI for legal advice, you should consider the AI tools you’re using, the configurations of those tools, and the purposes for which you are using the tools. Credit: Emma Wallace
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Data Safety Laws You Can't Ignore

Kids, Clicks, and Compliance: Data Safety Laws You Can’t Ignore

Children

Understanding age assurance vs. age verification vs. age signals and their impact on children and developers

For companies operating online, safeguarding kids in a digital world means navigating complex data protection rules along with many compliance challenges.  In the vacuum of federal legislation, individual states started passing their own regulations, creating a fast-growing patchwork of age-verification laws across the country. In several U.S. states, such as Florida, Texas, Louisiana, and Utah, among others, “age gating” for adult content is now or will become mandatory. In addition, many social media apps and app stores are now voluntarily “age gating” to meet privacy compliance requirements or reduce liability for AI-generated content. These laws and requirements vary substantially in their scope, applicable age thresholds, definitions of covered platforms, and enforcement frameworks.  For companies operating nationwide, that inconsistency is a major compliance obstacle, with implications reaching well beyond the protection of children online. Clear divisions have emerged between those who regard age verification as a necessary safeguard and critics who warn it could normalize a surveilled and censored internet. Profound consequences regarding privacy, speech and digital rights would affect every American, regardless of age. A legal battleground is taking shape around age assurance, age verification and age signals. The motivations behind these laws are generally positive. Lawmakers want to (i) prevent children from accessing pornographic or other harmful content; (ii) provide age-appropriate content and guardrails regarding suicide, self-harm, and addictive content; and (iii) provide more parental controls around children’s data.  The right way to implement these policy goals, however, is a lot messier.  Here’s the key question everyone needs to consider: How much information are we going to ask people to hand over to “know” their age? Before wading into this quagmire, let’s at least agree on some key definitions: Age Assurance – These are techniques to determine a person’s age and can be as low-tech as getting a user to self-report their age, or as high-tech as using AI techniques to “guess” a person’s age based on facial estimation, behavioral analysis or an analysis of data broker information. Age Verification – This is a subset of age assurance, where there is a high level of proof concerning a person’s age. This includes turning over a driver’s license or other types of digital IDs to access a service.  Age Signals – This is a signal from a device, operating system, or browser that can be based on age assurance or age verification techniques.  California’s Digital Age Assurance Act (AB 1043) set to take effect January 1, 2027 requires operating systems and app stores to obtain age verification upon account creation and then send age brackets via an age signal to developers. Developers cannot use this data or share it for purposes other than identifying a user’s age.  Compared to other age verification laws, which may require multiple websites or services to obtain vast amounts of personal data, this seems like a balanced approach. This law seems to limit the number of parties that collect sensitive data but still provides some level of age assurance for developers. In addition, we would strongly encourage app stores and operating systems to use on-device storage and processing, to further protect sensitive data. So what do you think? Do you agree with the California approach, and should this approach be adopted nationally? 
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Automated decision-making technologies (ADMT) in employment decisions

Using AI’s Tools in Hiring, Firing, and Compensation Decisions

AI & Algorithms

What Employers Need to Know About Using ADMT in Employment Decisions

Decisions about hiring, termination, and compensation represent substantial administrative costs for employers. Automated decision-making technologies (“ADMT”) can significantly streamline the process. However, employers using ADMT should be aware of recent and existing regulations governing the use of AI tools in evaluating prospective and current employees.

In addition to recent AI-specific regulation, use of AI tools in making employment decisions may be regulated by existing anti-discrimination statutes. Use of an algorithm that discriminates against a protected class identified in federal statutes – most notably Title VII of the Civil Rights Act and the Americans with Disabilities Act (ADA) – may expose employers to liability. What is ADMT? ADMT, or automated decision-making technology, is any technology that processes personal information and uses computation to replace or substantially replace human decision-making. AI tools used in employment may be one type of ADMT available to employers. In the context of ADMT, significant employment decisions may include:
  • Hiring
  • Allocating work or compensation
  • Promotion and demotion
  • Suspension and termination
State and local compliance requirements may create exceptions for businesses that do not use the AI tool’s recommendations as a substitute for human discretion. However, this may be a high bar to overcome, and not all types of human involvement qualify for an exception. For further explanation, please refer to the “Best Practices for Employers” section below. What are the risks of employment discrimination? AI and other ADMT tools involved in significant employment decisions may pose two key risks regarding employment discrimination. There is a risk they may: 1) Exclude or disadvantage applicants from a protected group identified in Title VII or applicants with disabilities. Groups are protected by the statute on the basis of race, color, sex, religion, or national origin. This may apply even if there is no intent to discriminate: If the technology is shown to have a disproportionate effect on a protected group, the employer may be vulnerable to a lawsuit. For example, if ADMT tends to exclude candidates with names that suggest a particular racial or national identity, this could pose risk to the employer using this ADMT. 2) Screen out candidates based on aspects of their application that characterize a disability recognized by the ADA. This screening process may apply to a seemingly neutral selection criterion. For example, an AI tool that screens employees out for a resume gap lasting longer than four months could raise a risk of liability if the individual has a disability requiring substantial recovery periods after medical intervention. What types of ADMT pose particular risks of discrimination? Certain types of ADMT may pose particular risks of violating state and federal regulations. This may include AI-hiring tools with algorithms that:
  • Fail to take into account reasonable accommodations or available workplace alternatives in their assessment of a candidate’s ability to uphold the employer’s performance standards
  • Fail to include measures to mitigate against sensitivity to names of candidates – which contain information as to the gender and/or ethnic or racial origins of the applicant
  • Are overly reliant on inferences between the applicant and existing successful employees, which may reinforce existing hiring biases
  • Fail to account for possible reasonable accommodations related to their disability that are available to the applicant
  • Rely on an empirical evaluation of an individual’s conformity with a subjective standard such as “culture fit”.Additionally, video-interviewing software that includes emotion-recognition technology without human involvement in their hiring decision, and hiring tools that require the applicant to provide medical information prior to employment may also create additional risk.
Best Practices for Employers When selecting an AI tool for use in your employment decisions, there are measures employers can take to potentially reduce the risk of discrimination. 1. Transparency. Measures may include requesting transparency from the developer about mitigating measures to insulate decisions against particular risk factors.For example, seek tools that do not weigh factors posing particular risks of discrimination in the scoring process so heavily that they disqualify candidates. Transparency is also useful in preparing risk assessments which may be required by state and local regulations when using AMDT. 2. Human Involvement. Employers may also consider assessing the degree of human involvement in the decision-making process to see if the applied use qualifies for an exception from the regulation. If seeking an exception, a certain degree of human involvement may be required. Examples of insufficient degrees of human involvement may include situations where the decision-maker:
  • Is tasked with merely reviewing AI output
  • Lacks authority to change the decision
  • Lacks access necessary to make an independent decision
  • Operates under time constraints insufficient for substantive review
  • Only intervenes for obvious mistakes
In general, businesses should not recommend that the human decision-maker follow the AI’s decision by default in policy or in practice and should encourage independent human review. 3. Preparation. When using AI to assist in employment decisions, businesses may want to consider:
  • Conducting and submitting a risk assessment evaluating the risks of potential discrimination or data privacy balanced against the benefit to the business
  • Disclosing use of an AI tool in the applicant selection process before an applicant submits their application
  • Consulting state and local regulations to confirm compliance with required procedures and components. For example, CA, NY, IL, and CO are among the states that mandate some type of pre-disclosure when using ADMT or similar tools. Depending on the jurisdiction, it may be helpful for employers to consult relevant statutes to determine specific compliance requirements and timelines for disclosure.
  • Maintaining alternative processes to ADMT for selecting qualified candidates and allow potential applicants to opt-out of its use in evaluating their application. For candidates with disabilities, this may also include providing candidates with reasonable accommodations, including specialized equipment or extended timing or other modifications for timed skill assessments.
  • Establishing an appeal process for employment decisions made using AI tools.
  • Anticipating possible requests for deletion of personal data in response to evolving privacy rights across various jurisdictions. For example, in California, applicants may have existing privacy protections that include the rights to:
    • Be notified regarding a business’s use of AI in making employment decisions
    • Know what data is being collected, its purpose, and with whom it will be shared
    • Request deletion of personal information
    • Correct inaccurate personal information
    • Stop or limit the sale of sensitive personal information
    • And non-discrimination for exercising the rights provided.
What’s Next? The recent Executive Order suggests that national policy may soon tend away from allowing applicants and/or employees to bring claims based on an AI tool’s disproportionate effect on a protected group. (Executive Order, Ensuring a National Policy Framework for Artificial Intelligence, Sections 6 & 9, issued December 11, 2025). However, as state and local-level protections take effect and as federal minimum standards continue to be fleshed out, some caution is required as these standards are interpreted by relevant state and federal agencies.
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