Welcome to our Women’s History Month edition of Techronicler. Today, our guest is Chanelle Coleman, the Director of Service Delivery Operations at Caylent. Chanelle brings a profoundly intentional approach to tech leadership, drawing on a diverse background that spans non-profit work, music entertainment, and IT, underpinned by degrees in Biblical Studies and Organizational Leadership.

Rather than viewing operations as purely mechanical, Chanelle designs systems that enable organizations to scale efficiently while empowering teams to thrive. In this interview, she discusses why true leadership requires a combination of authority and humility, how AI is accelerating operational skill development, and why she relies on transparency and mentorship to help junior women overcome the “broken rung.”

Welcome to our Women’s History Month edition of Techronicler. Today, our guest is Emma Marcotte, SVP of Clients at Moburst.

Emma has built her career by acting as the ultimate translator—bridging the gap between what a client envisions, what technical delivery requires, and what a business can sustainably support. Rather than subscribing to the outdated tech mantra of “move fast and break things,” Emma champions a philosophy of moving fast on learning and deliberately on commitments.

In this interview, she discusses how to operationalize AI without it becoming just another shiny object, why she had to unlearn the habit of “reflexive accommodation,” and how she is actively dismantling the “broken rung” by equipping junior women with both undeniable track records and the language of confidence.

Welcome to a very special Women’s History Month edition of Techronicler. Today, we are speaking with Katherine King, an executive whose career path has been less of a straight corporate ladder and more of a fascinating “seismograph.” From her early days as an audio engineer handling live bands to her current work untangling convoluted legal and claims workflows, Katherine has built a career on asking the awkwardly practical questions that actually drive innovation.

She specializes in translating tech jargon into plain English and politely pointing out when a new system fails to make work genuinely easier. In this interview, she shares her refreshing perspective on why “sharp-elbowed” leadership should be penalized, how she manages the delicate balance of AI security, and why her ultimate dream tech product has everything to do with reducing the invisible mental load.

Since ChatGPT and other large language models (LLMs) gained traction, concerns about copyright and privacy risk have become increasingly urgent. This risk is compounded by the fact that many employees admit to using generative AI tools at work without formal approval or governance, creating hidden exposure for organizations.

One of the most significant areas of risk lies in documents.

Across industries, documents remain the backbone of business operations. They contain contracts, compliance records, medical information, financial histories, personal identifiers, trade secrets, and copyrighted works. As organizations modernize document workflows, they are increasingly looking to augment traditional automation with the reasoning capabilities of LLMs, the contextual intelligence of retrieval-augmented generation (RAG), and even agentic AI. The challenge is to integrate these capabilities without introducing unacceptable copyright or privacy liabilities.

How LLMs Introduce Risk

LLMs are trained by ingesting vast amounts of text – from books, articles, websites—and converting that information into numerical representations known as embeddings. These embeddings can introduce legal and privacy risk in two ways:

• Internally, through representations created during model training and inference, such as token or word embedding; and
  
  
• Externally, when embeddings are stored in vector databases to support semantic search or RAG workflows, often derived from customer content, proprietary documents, or user prompts.
  
  

Recent legal and policy work emphasizes that embeddings are not just harmless math. In some cases, they may be reverse-engineered or exploited to infer underlying content, exposing organizations to copyright infringement or privacy violations.

Copyright Law and Fair Use

U.S. copyright law allows certain uses of copyrighted material under the doctrine of ‘fair use’. In Authors Guild v. Google, Inc., the Second Circuit held that Google’s digitization of books to make them searchable constituted fair use because it was transformative and did not substitute for the originals.

However, the Supreme Court narrowed this reasoning in Goldsmith v. Andy Warhol Foundation, emphasizing that courts must evaluate each specific use and consider whether it competes with or harms the market for the original work. Transformation alone is not sufficient. These distinctions are increasingly relevant for AI.

The New Wave of AI Copyright Litigation

Courts are now testing how fair use applies to AI training and outputs. In Bartz et al. v. Anthropic PBC, a federal judge suggested that training models on copyrighted books could qualify as fair use in principle but allowed the case to proceed based on concerns that output-level memorization or regurgitation could constitute derivative works.

The subsequent $1.5 billion settlement—the largest copyright settlement in U.S. history—highlighted how quickly the legal landscape is shifting.

In December 2025, a new, non–class-action lawsuit led by Bad Blood author John Carreyrou targeted six major AI companies, challenged the foundational premise that fair use should extend to training, especially where companies knowingly ingested infringing datasets and have generated enormous commercial value from models without adequate compensation

At the center of these cases is the fourth fair-use factor: market harm. Courts are increasingly acknowledging that AI systems are not merely indexes or search tools, but generative systems whose outputs may contain copyrighted text, replicate proprietary styles, or substitute for original works.

So where does this leave companies? With so many legal implications, business leaders must consider, is training data more akin to copying to build a derivative artifact, which may be infringing?  Are embeddings “copies”? Do AI outputs cause market harm when AI substitutes for original journalism or books?

A Safe Architectural Approach

General-purpose AI tools such as LLMs were not designed with these risks in mind.  But, purpose-built Document AI differs both philosophically and technically from generative models.

First, they avoid unlicensed, scraped training data.  While Foundational AI models rely on massive datasets gathered from the open internet, some of it licensed, much of it not, purpose-built AI takes the opposite approach: they build models using controlled datasets, synthetic data, or licensed corpora. There’s no mystery about where the training material came from, and no risk that the system learned from pirated books or proprietary documents.

Second, they don’t retain customer documents for model improvement. In general-purpose AI, anything a customer uploads may become part of the next model. With purpose-built Document AI, the workflow is explicitly segmented: data goes in, fields come out, and the underlying documents are not absorbed into a global model.

Third, they support on-premise and private-cloud deployment. Many enterprises handle data that cannot legally leave their infrastructure. Purpose-built Document AI solutions let them keep full control, avoiding the security gaps and compliance risks that come with sending documents to third-party servers.

Fourth, they minimize or eliminate persistent embeddings. Embeddings are one of the biggest drivers of legal uncertainty because they can encode personal or expressive information. Document AI often bypasses or tightly controls embedding creation. When embeddings are used for classification or semantic matching, they’re isolated to the customer’s own environment and not co-mingled across tenants.

Fifth, they don’t generate new copyrighted output. Generative AI models can accidentally reproduce training content word-for-word. Document AI doesn’t generate text—it extracts. That eliminates one of the biggest sources of infringement risk.

Protecting Privacy by Design

While copyright gets more attention in headlines, the privacy issues are just as urgent. In many industries, document processing directly exposes AI systems to some of the most sensitive information a company handles.

Purpose-built Document AI reduces this risk through design choices that make privacy protection easier and more reliable.

Data minimization is built into the workflow. Instead of keeping full documents, these systems can retain only the fields that matter—amounts, dates, IDs, addresses—discarding the wider document context. That drastically reduces the exposure if a breach occurs.

Field-level redaction and pseudonymization, such as names, account numbers, birthdates, and other personal identifiers, can be redacted or hashed automatically before the data moves into downstream systems.

If embeddings are generated at all, they remain locked inside customer-specific environments. Attackers cannot probe the model to discover whether an individual’s data was used.

Document AI systems tend to include full tracking of who accessed a document, when, and for what purpose. This satisfies regulators’ expectations for accountability and helps organizations demonstrate compliance.

Best Practices for Safe AI Deployment

Even with a safer toolset, responsible implementation matters. Organizations adopting Document AI should embrace several practical safeguards:

•   Keep training separate from customer data. Never let customer documents flow into general models unless you have explicit, documented permission.
  
  
•   Encrypt everything, including intermediate representations. Attackers increasingly target vector databases.
  
  
•   Limit retention windows. If documents or embeddings aren’t needed after extraction, delete them.
  
  
•   Use redaction by default. Especially for identity data, health information, or payment details.
  
  
•   Maintain a clear data lineage. Track where documents came from, how they were processed, and where output is stored.
  
  
•   Prepare for erasure and unlearning requests. Privacy laws increasingly require the ability to remove personal data from derived artifacts, not just raw documents.
  
  
  

A Different Kind of Intelligence

As generative AI reshapes work, it’s tempting to try to solve every problem with the same model. But the legal landscape is making one thing clear: when dealing with sensitive or copyrighted documents, a different kind of intelligence is needed.

Purpose-built Document AI avoids the pitfalls of general-purpose models by design. It processes documents without absorbing them. It extracts information without learning more than it needs. It keeps data isolated rather than blending it into global models. And it equips organizations with the guardrails required to meet evolving copyright and privacy standards.

In a world where the rules of AI are still taking shape, organizations cannot afford guesswork. They need tools designed for compliance, not just performance. They need systems that treat documents with the care the law requires and the caution reality demands.

By combining rights-safe training, privacy-by-design features, controlled embeddings, and robust security frameworks, such systems significantly reduce the exposure to both copyright infringement and data-privacy violations while retaining operational efficiency.

Frank Sommers, Vice President, Technology Leasing, at First Financial Equipment Leasing, brings 30 years of experience in the IT leasing industry, advising global enterprise organizations on how to modernize their infrastructure while preserving capital and accelerating technology adoption. A former collegiate soccer player at Cal Poly San Luis Obispo, Frank brings a strong sense of competitiveness and teamwork to every client relationship.

As AI adoption moves quickly across industries, large enterprises are facing significant financial hurdles, with full-scale AI data center investments ranging from $150 to $500 million. Frank joins us today to explain how organizations can overcome these barriers. He discusses how IT leasing acts as a “budget multiplier,” allowing companies to bypass the massive upfront costs of high-performance compute infrastructure and avoid the trap of rapidly depreciating technology.

In today’s edition of the Women’s History Month interview series on Techronicler, we are speaking with a true pioneer whose career stretches across four decades of technological evolution.

Our guest is Brenda Christensen, the CEO of Stellar PR and a distinguished Inc. 500 and Fast 500 founder. Brenda began her career in tech 40 years ago as a daily newspaper reporter in Detroit before launching the first AI robotic programming language for General Motors at just 23 years old. She then transitioned into public relations with a mission to bridge the gap between complex technology companies and the journalists covering them.

Throughout her career, Brenda has consistently championed user-centered innovation—most notably pushing engineering teams in the late 80s and early 90s to implement the “vibrate” mode on cell phones, a feature that fundamentally transformed mobile etiquette. Today, she continues to advise F50 companies and unicorn startups, focusing on technologies that drive social good.