The Governance Gap Is Already Open: What the New COSO GenAI Framework Tells Us (Part 1 of 3)

This is the first in a three-part series breaking down the Committee of Sponsoring Organizations of the Treadway Commission's (COSO) newly released report, Achieving Effective Internal Control Over Generative AI. Each post covers five key takeaways from the document. Part 1 lays the foundation: the risks, the capability types, and the control principles organizations need to understand before anything else. The full report is available free of charge at coso.org and is worth reading in full. What follows is a guided tour of the highlights.

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Generative AI is not waiting for your governance team. It is already inside your organization, running inside productivity tools, shaping analyses, and generating content, regardless of whether your policies have caught up. The question is no longer whether your employees are using it. The question is whether you know how, where, and with what data.

The COSO report opens with that precise tension. It acknowledges the productivity gains and the analytical possibilities that GenAI introduces across finance, compliance, and operations. It also makes clear that those same qualities, speed, accessibility, and adaptability, are exactly what make GenAI a governance problem if left unmanaged. Hallucinations, prompt injection, model drift, opaque reasoning, and rapid configuration changes can all threaten the reliability of operations and reporting if no one is watching.

That framing sets the stakes. And if your organization has not begun building the internal controls to match, the gap between where you are and where you need to be is already widening.

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Takeaway 1: Shadow AI Is the New BYOD

History does not repeat itself, but it certainly rhymes. In the early 2010s, the rise of the iPhone and Android forced IT departments to grapple with the Bring Your Own Device (BYOD) movement. Workers wanted their personal devices connected to corporate systems, and IT had to build frameworks to accommodate that demand without compromising security. BYOD ultimately displaced BlackBerry's enterprise dominance because the pressure from the workforce was impossible to contain.

The same dynamic is playing out now with AI, and the COSO report names it directly. On page five, the document defines Shadow AI as unauthorized or ungoverned AI implementations operating outside formal IT oversight.

The parallel to BYOD is instructive, but Shadow AI carries a higher risk profile. Getting corporate data onto a personal device in the BYOD era required some degree of technical sophistication. With Shadow AI, the barrier is copy and paste. An employee can move sensitive client data, unreleased financial projections, or regulated personal information into a consumer AI tool in seconds, without any technical skill and without any visible footprint in your systems.

What makes this particularly hard to contain is that the motivation is legitimate. GenAI tools offer genuine productivity advantages, competitive edge in knowledge work, and time savings that employees feel immediately. That is not bad behavior. It is rational behavior in the absence of a governed alternative. The COSO report is right to surface this in the introduction, because until organizations provide a sanctioned path, employees will build their own.

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Takeaway 2: Seven GenAI-Specific Risks

Before the document maps controls to any framework, it lists the risks that make GenAI governance categorically different from traditional IT risk management. These are not generic technology risks. They are specific to how GenAI systems work and how they fail.

The report identifies seven:

1. Data quality, source, and completeness
2. Reliability and consistency
3. Explainability and transparency
4. Security and privacy
5. Bias and fairness
6. Third-party and vendor risk
7. Governance and accountability

Each of these deserves its own treatment, and later posts in this series will go deeper. For now, the important point is the list itself. These risks are not hypothetical. They are active in any organization where GenAI is being used, whether governed or not. Shadow AI, by definition, means these risks exist without the controls designed to manage them.

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Takeaway 3: Eight Capability Types That Map How GenAI Works

One of the most practically useful contributions in the COSO report is its capability-first taxonomy. Rather than organizing GenAI by vendor or product name, which would be outdated before the ink dried, the report organizes it by what the system actually does. This is the right approach. It gives practitioners a durable lens for risk assessment and control design that does not depend on which tools are in the market this quarter.

The report identifies eight capability types following a data-to-decision sequence (Emett et al., 2026, p. 7):

1. Data extraction and ingestion
2. Data transformation and integration
3. Automated transaction processing and reconciliation
4. Workflow orchestration and autonomous task execution
5. Judgment, forecasting, and insight generation
6. AI-powered monitoring and continuous review
7. Knowledge retrieval and summarization
8. Human-AI collaboration

A few of these are worth highlighting from a practical standpoint. Data transformation and integration is one of the most powerful and underappreciated capabilities. The ability to take unstructured information and convert it into structured outputs, or take raw data and convert it into a readable memo, is something GenAI does unusually well. This is not simple summarization. It is a genuine transformation of information across formats and registers that previously required significant human effort. I refer to this as "Data to Documentation" within my GenAI workshops. 

Knowledge retrieval and summarization is another that has real-world traction right now. Tools like NotebookLM are already being used to synthesize large document sets into accessible summaries, a task that once took days. The capability is real, and the productivity gain is real, which is exactly why the governance question cannot wait.

Judgment, forecasting, and insight generation is the most nuanced of the eight. It sits at the intersection of classic machine learning and generative AI, and the report acknowledges that complexity. This capability will receive more attention in Parts 2 and 3 of this series, particularly around how the COSO framework addresses the risk of over-reliance and how human review requirements scale with the materiality of the decision.

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Takeaway 4: Five Foundational Characteristics That Impact Control Design

Before mapping any of the 17 COSO principles to GenAI, the report establishes five foundational characteristics of the technology itself. These are not risk categories. They are architectural realities that should inform how controls are built. The report's treatment of each is worth reading in full; the short version is below (Emett et al., 2026, p. 8):

• Probabilistic, not deterministic: GenAI can be confidently wrong; outputs require validation
• Dynamic: models, prompts, and data change frequently, sometimes without notice
• Easily scalable: automation scales errors just as readily as it scales quality
• Low barrier to entry: accessibility is what enables Shadow AI to flourish
• GenAI can help govern GenAI: its pattern-recognition capabilities can strengthen monitoring and validation

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Takeaway 5: The 17 COSO Principles as They Apply to GenAI

The COSO Internal Control Integrated Framework organizes its guidance around five components and 17 principles. The report applies all 17 to the GenAI context. Here is how they break out across the five components (Emett et al., 2026, pp. 5, 9–17):

Control Environment

• Principle 1: Demonstrate commitment to integrity and ethical values
• Principle 2: Exercise oversight responsibility
• Principle 3: Establish structure, authority, and responsibility
• Principle 4: Demonstrate commitment to competence
• Principle 5: Enforce accountability

Risk Assessment

• Principle 6: Specify suitable objectives
• Principle 7: Identify and analyze risk
• Principle 8: Assess fraud risk
• Principle 9: Identify and analyze significant change

Control Activities

• Principle 10: Select and develop control activities
• Principle 11: Select and develop general controls over technology
• Principle 12: Deploy through policies and procedures

Information and Communication

• Principle 13: Use relevant information
• Principle 14: Communicate internally
• Principle 15: Communicate externally

Monitoring Activities

• Principle 16: Conduct ongoing and/or separate evaluations
• Principle 17: Evaluate and communicate deficiencies

What the report does that previous frameworks have not is apply each of these principles specifically to the GenAI context, with examples, minimum control expectations, and metrics. A principle like "identify and analyze significant change" reads differently when the change in question is a vendor releasing a model update that silently alters how your automated reconciliation system classifies transactions. The familiar framework is still sound. The terrain it has to cover has changed.

The next two posts in this series continue the conversation, surfacing the report's most relevant guidance for practitioners navigating the governance challenges that GenAI presents.

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Reference

Emett, S., Eulerich, M., Guthrie, J., Pikoos, J., & Wood, D. A. (2026). Achieving effective internal control over generative AI (GenAI). Committee of Sponsoring Organizations of the Treadway Commission. https://www.coso.org/generative-ai

UWCISA's 5 Tech Takeaways: From Job Cuts to “Workslop,” How AI Is Reshaping Work - and Raising New Risks

In this week's 5 Tech Takeaways, the stories that bubbled to the top were cybersecurity challenges around Anthropic's Mythos and how AI can be used against you in court. However, the other three articles examine how AI will impact jobs.

When I discuss job losses as they relate to AI and how they will unfold in the economy, I typically walk through three scenarios, as any good consultant would: AI will replace jobs, AI will augment jobs, and AI will create jobs. We do this because we never truly know which direction things will go. Consider the automobile as an analogy. When the world was dominated by horse and buggy, no one could have foreseen the jobs that would emerge from the manufacture of the car, not only in vehicle manufacturing itself but also in all the ancillary roles that followed, like the drive-through window at a fast food restaurant.

AI is eliminating jobs at Snap. But is this really about GenAI, or is it about the fundamental algorithm that actually rules the economy? That algorithm is, of course: Profit = Revenue - Expenses. It has been difficult to determine whether this reflects a deliberate business strategy of minimizing the "E" side of the equation simply to boost stock price.

The more interesting development is the rise of "AI Workslop." 

Though the headline is focusing on the negative, reading the original WSJ article reveals that 60% of workers are seeing some productivity benefits, with 34% reporting a gain of at least two hours per week. 

What does this mean in practice? 

Understanding prompting technique, and knowing when to use AI and, more importantly, when not to, is key to realizing its benefits. The clearest illustration comes from UX designer Steve McGarvey, who found that targeted tools like Perplexity saved him significant time for research, but cautioned that without judgment or discernment in your field, you could do real harm by assuming AI outputs are factual. This reinforces a core principle: AI's benefits are not automatic. They hinge on informed, professional judgment.

Claude Mythos Sparks Fear—and Skepticism—in Cybersecurity World

Anthropic’s unveiling of its new AI model, Claude Mythos Preview, has triggered both alarm and skepticism across the tech and cybersecurity communities. The company claimed the model can autonomously discover and exploit zero-day vulnerabilities across major systems, prompting the creation of Project Glasswing—an invite-only initiative to secure critical infrastructure. While some experts see this as a major leap toward powerful, possibly AGI-level systems, others question the lack of transparency and suggest the rollout may be more of a marketing strategy than a true breakthrough. Independent testing has validated that Mythos performs exceptionally well on cybersecurity benchmarks, though limitations remain. Experts ultimately agree that while the most extreme fears are unlikely, the model represents a meaningful shift in how vulnerabilities can be discovered and exploited—posing both risks and opportunities.

(Source: Mashable)

• A powerful but debated breakthrough: Claude Mythos shows significant advances in identifying security flaws, but experts disagree on its true impact.
• A mix of caution and marketing: The restricted rollout raises questions about whether safety or publicity is the main driver.
• Real risks, but not doomsday: Automation of vulnerability discovery could reshape cybersecurity threats.

AI Chats Could Land in Court, Lawyers Warn

A recent U.S. court ruling is raising serious concerns about the legal privacy of conversations with AI chatbots like ChatGPT and Claude. In a fraud case, a federal judge ruled that a defendant could not shield AI-generated documents from prosecutors, emphasizing that chatbot interactions are not protected by attorney-client privilege. This has prompted widespread warnings from lawyers, who now advise clients to treat AI tools as non-confidential and potentially discoverable in court. While some judges have taken a more lenient view, the legal landscape remains uncertain as courts grapple with AI’s growing role.

(Source: Reuters)

• No legal privilege with AI: Chatbot conversations may be used as evidence in court.
• Lawyers urge caution: Sensitive legal details should not be shared with AI tools.
• Unclear legal future: Courts are still defining how AI fits into legal protections.

The Rise of “Workslop”: When AI Creates More Work Than It Saves

Despite executive claims that AI boosts productivity, many workers report the opposite—coining the term “workslop” to describe flawed AI-generated output that requires heavy correction. Employees say AI speeds up drafts but increases overall workload due to errors and inconsistencies. Surveys reveal a stark disconnect between leadership optimism and worker experience, with many seeing no time savings. Poor implementation and lack of training have compounded the issue, leading to frustration and lost productivity across organizations.

(Source: The Guardian)

• “Workslop” is widespread: AI-generated work often needs significant revision.
• Leadership vs. reality gap: Executives see gains, workers often don’t.
• AI growing pains: Poor rollout strategies are limiting effectiveness.

Business Leaders Bet on AI Augmentation Over Job Losses

As fears of mass unemployment grow, many CEOs argue that AI will augment rather than replace human workers. Industry leaders emphasize that while AI will transform workflows, it will also create demand for new skills like critical thinking and cross-disciplinary analysis. Despite rising adoption, consistent productivity gains remain uncertain, and daily usage is still relatively low. Companies are investing in reskilling efforts, signaling a future where human-AI collaboration becomes the norm rather than outright replacement.

(Source: CNBC)

• Augmentation over replacement: CEOs expect AI to enhance jobs.
• Skills shift underway: Workers must adapt to new ways of thinking.
• Adoption vs. impact gap: Productivity benefits are still emerging.

AI Push Drives Major Layoffs at Snap Amid Investor Pressure

Snap is laying off about 1,000 employees as it pivots toward AI-driven efficiency following pressure from activist investors. The company says AI will allow it to streamline operations and reduce costs, with expected annual savings exceeding $500 million. The layoffs reflect a broader trend in the tech industry, where companies are restructuring around AI capabilities while maintaining investments in future growth areas like augmented reality.

(Source: Reuters)

• AI driving layoffs: Automation is reducing workforce needs.
• Investor influence: Activist pressure accelerated restructuring.
• Industry-wide shift: Tech firms are embracing leaner, AI-powered models.

Author: Malik D. CPA, CA, CISA. This post was written with the assistance of an AI language model. 

UWCISA's 5 Tech Takeaways: Next-Gen AI at CES 2026, Changing Job Dynamics, and High-Stakes Tech Rivalries

This edition looks at AI and digital infrastructure from five angles: NVIDIA’s latest hardware platform, Anthropic’s deep dive on how AI is actually used in the economy, frontline “AI fatigue” inside EY Canada, record-breaking frustration with Canadian telecoms, and Nvidia’s chips turning into bargaining tools in U.S.–China geopolitics. Together, they sketch a picture of powerful technology racing ahead while workers, consumers, and governments scramble to keep pace.

NVIDIA’s Rubin Platform Sets the Stage for Gigascale AI at CES 2026

NVIDIA CEO Jensen Huang opened CES 2026 by unveiling Rubin, the company’s first extreme-codesigned, six-chip AI platform, designed to dramatically cut the cost of AI training and inference while accelerating model development. As the successor to Blackwell, Rubin tightly integrates GPUs, CPUs, networking, storage and software to reduce bottlenecks and deliver AI tokens at roughly one-tenth the previous cost. Alongside Rubin, NVIDIA expanded its open-model ecosystem across healthcare, climate science, robotics, embodied intelligence, and autonomous driving, including Alpamayo, a reasoning VLA model family aimed at Level-4 autonomy and showcased in the new Mercedes-Benz CLA. Huang also highlighted the rise of “physical AI” through robotics, simulation, and industrial partnerships with companies like Siemens, while rolling out consumer-facing news such as DLSS 4.5, RTX gaming updates, and new GeForce NOW options—all reinforcing NVIDIA’s ambition to provide a full-stack AI infrastructure from data center to desktop to car.

• Rubin slashes AI costs: Rubin promises roughly 10x cheaper token generation by co-designing GPUs, CPUs, networking, storage, and software into a single extreme-scale AI platform.
• Open models across six domains: NVIDIA’s open models now span healthcare, climate, reasoning, robotics, embodied intelligence, and autonomous driving, giving developers a broad foundation for new AI applications.
• Physical and personal AI converge: From Level-4-capable vehicles to desktop “personal agents” and RTX gaming tech, NVIDIA is pushing AI into cars, robots, and consumer devices—not just supercomputers.

(Source: NVIDIA Blog)

Inside Claude’s Global Impact: New Data Shows Productivity Gains and Shifting Job Skills

The January 2026 Anthropic Economic Index introduces “economic primitives,” a set of new metrics that describe how people and firms actually use Claude: task complexity, human and AI skill levels, autonomy, use cases, and task success. Drawing on one million anonymized conversations and API calls from late 2025, the report finds that Claude is disproportionately used for high-skill, high-education tasks and tends to deliver larger time savings on more complex work—though reliability drops as tasks become longer and harder. Adoption patterns differ sharply by geography: higher-income, higher-education regions use Claude more collaboratively and for personal or diversified work, while lower-income countries lean more on coursework and targeted technical tasks. When success rates are factored in, the report suggests AI could still add about one percentage point to annual labour-productivity growth over the next decade, but also warns that automation tends to remove the most education-intensive tasks within many jobs, potentially “deskilling” roles even as it boosts efficiency.

• New “economic primitives” map real AI use: Anthropic introduces foundational metrics to quantify how Claude is used—covering complexity, skills, autonomy, use case, and task success across millions of interactions.
• Biggest gains on complex tasks, but with reliability tradeoffs: Claude speeds up higher-skill work the most, yet success rates fall as tasks get longer or more complex, meaning realistic productivity estimates must discount for failures.
• AI reshapes job content and inequality: Usage concentrates on higher-education tasks, often automating the most skill-intensive parts of jobs and potentially deskilling roles, while regions with more education and income are better positioned to benefit.

(Source: Anthropic)

EY Canada Confronts Rising ‘AI Fatigue’ as Workers Feel Overwhelmed by Rapid Change

EY Canada has invested heavily in AI training—400,000 hours of learning time and a $12 million internal program since 2022—but is now grappling with “AI fatigue” among parts of its workforce. After segmenting employees by both skill and willingness to use AI, the firm found that some professionals felt so overwhelmed by the pace of change they didn’t know where to start. In response, EY is tailoring its approach with bespoke learning paths, more guidance on ethical and responsible AI use, and sandbox environments where skeptical staff can experiment without risk. This reflects a wider pattern: across consulting, law, and other white-collar sectors, workers report burnout as AI tools, training requirements, and vendor pitches stack on top of already long workweeks. While some firms are tying promotions and hiring to AI proficiency, EY emphasizes human-in-the-loop oversight—especially for more fragile agentic AI systems—and insists it still plans to hire junior talent rather than replacing entry-level roles outright.

• AI fatigue is a real adoption barrier: Even after large-scale training, some EY staff feel overloaded and disengaged, forcing the firm to rethink how it introduces AI into everyday workflows.
• Personalized, empathetic training is emerging as critical: EY is segmenting employees by “skill” and “will,” using bespoke learning, ethical guidance, and safe sandboxes to engage skeptics instead of simply pushing more generic courses.
• Human oversight remains central, despite automation pressure: The firm stresses that fragile tools like agentic AI still require trained humans in the loop, and continues to recruit entry-level consultants rather than fully automating junior work.

(Source: The Logic)

Telus Sees 78% Complaint Surge as Billing and Contract Issues Rise Nationwide

Canada’s telecom watchdog, the Commission for Complaints for Telecom-television Services (CCTS), reports that consumer complaints have hit a record high, rising 17% to 23,647 accepted cases over the past year. Wireless services remain the biggest source of frustration, but billing problems—incorrect charges and missing credits—make up nearly 46% of all issues. Among the “Big 3” carriers, Rogers leads with 27% of total complaints, while Telus accounts for 21% but suffers the sharpest increase: a 78% year-over-year jump in complaint volume. Bell sits at 17% of the total. The report also flags a 121% spike in breach-of-contract complaints, including fee hikes and broken promises on features, alongside persistent service issues such as outages and installation delays. Although many Canadians still don’t know the CCTS exists, it remains a free avenue for unresolved disputes—and says it successfully resolves most cases. Still, with TV-related complaints up 44% and billing errors at a five-year high, the data paints a grim picture for customer experience in Canada’s concentrated telecom market.

• Record complaint levels across Canadian telecoms: The CCTS logged 23,647 accepted complaints—a 17% jump—driven heavily by wireless issues and billing disputes.
• Telus stands out for rapid deterioration: While Rogers still generates the most complaints overall, Telus suffered a 78% increase in cases, far outpacing Bell and indicating a sharp drop in customer satisfaction.
• Broken contracts and billing errors dominate frustration: Breach-of-contract complaints surged 121%, while billing problems hit a five-year high, underscoring systemic issues in pricing transparency and service reliability.

(Source: iPhone in Canada)

Nvidia’s H200 Becomes Geopolitical Leverage as China Restricts Purchases

China has instructed customs agents that Nvidia’s H200 AI chips are “not permitted” to enter the country and advised domestic tech firms to avoid buying them unless absolutely necessary, creating what sources describe as a de facto—if not yet formal—ban. The directive comes just as the U.S. government approved exports of the H200 to China under certain conditions, turning the chip into a focal point of U.S.–China tech tensions ahead of President Donald Trump’s planned April visit to Beijing. Analysts suggest Beijing may be using the restrictions as bargaining leverage or to push demand toward domestic AI processors like Huawei’s Ascend 910C, which still lag Nvidia’s performance for large-scale model training. The stakes are enormous: Chinese companies have reportedly ordered more than two million H200 units at around US$27,000 each, far exceeding Nvidia’s inventory, while the U.S. stands to collect a 25% fee on chip sales. Whether these moves ultimately favor China’s chip ambitions or Nvidia’s bottom line remains unclear, but the H200 has clearly become a strategic asset in a broader struggle over AI hardware dominance.

• China imposes a de facto block on H200 chips: Customs guidance and warnings to tech firms effectively halt Nvidia H200 imports for now, even though it’s unclear if this is a formal or temporary measure.
• Chips become negotiation tools in U.S.–China relations: The timing—just after U.S. export approval and ahead of high-level talks—suggests Beijing may be using access to H200s as leverage in broader tech and trade negotiations.
• Huge commercial and strategic stakes on both sides: Chinese firms have ordered millions of H200s, while the U.S. benefits from export fees and strategic influence, making the chip central to the evolving AI power balance.

(Source: Reuters)

Author: Malik D. CPA, CA, CISA. The opinions expressed here do not necessarily represent UWCISA, UW,  or anyone else. This post was written with the assistance of an AI language model. 

UWCISA's 5 Tech Takeaways: How AI is Shaping Investments, Policies, Legal Battles, and the Future of Work

User 726679350 · UWCISA's 5 Tech Takeaways: How AI is Shaping Investments+Policies+Legal Battles & Future of Work

Amazon Eyes Additional Multi-Billion Dollar Investment in Anthropic

Amazon.com is considering a second multi-billion dollar investment in AI startup Anthropic, maker of Claude.ai, according to a report by Reuters. This follows Amazon’s previous $4 billion investment in Anthropic, seen as a competitor to OpenAI, in 2023. Anthropic relies on Amazon’s cloud infrastructure but has shown a preference for Nvidia-designed chips over Amazon’s own server technology. The growth of Anthropic, co-founded by former OpenAI executives, has attracted significant interest, with Google-parent Alphabet also committing up to $2 billion in funding. This investment is part of Amazon’s broader AI strategy to offer early access to advanced AI capabilities to its cloud customers.

Key Takeaways:

• Amazon is contemplating a new multi-billion dollar investment in AI company Anthropic, following an initial $4 billion investment.
• Anthropic uses Amazon’s cloud but prefers Nvidia chips, indicating differing technology preferences.
• Previous funding includes a major investment from Alphabet, totaling up to $2 billion.

BYOAI: How Workers Are Adopting AI to Tackle Rising Workloads Amidst Policy Gaps

A new report from Wrike reveals that American workers are managing an increased workload, up 31% from last year, by adopting generative AI tools like ChatGPT, Co-Pilot, and Claude to boost productivity. However, only 31% of companies currently have AI guidelines in place, creating a disconnect between workers’ adoption of AI and company policies. Experts recommend that organizations establish comprehensive AI policies, including ethical training, to bridge this gap and support responsible AI usage. As companies catch up, generative AI is increasingly seen as essential for streamlining tasks and filling gaps amid rising workloads.

Key Takeaways:

• U.S. workers report a 31% increase in workload, leading many to adopt generative AI tools for efficiency.
• Companies lag behind, with only 31% having AI policies in place.
• Experts call for structured AI policies and ethical training to responsibly integrate AI into workplaces.

(Source: VentureBeat)

How OpenAI’s Voice and Search Upgrades Pave the Way for Advanced AI Agents

OpenAI has recently launched updates to its platform, including a Realtime API that supports voice features and a ChatGPT-powered search function, laying the groundwork for AI agents—advanced digital assistants capable of handling complex tasks. Two significant challenges remain: enhancing AI’s reasoning abilities and enabling seamless tool use. OpenAI’s latest model includes a “chain of thought” feature to improve reasoning, though experts note it doesn’t yet achieve true human reasoning. OpenAI aims to expand these AI capabilities across fields like law, economics, and accounting, foreseeing transformative applications.

Key Takeaways:

• OpenAI’s recent updates aim to support AI agents that could manage complex tasks using voice features and search capabilities.
• Challenges remain in developing reasoning and external tool-use abilities for AI agents.
• OpenAI plans to adapt these capabilities for fields like law, economics, and accounting to broaden its applications.

(Source: MIT Technology Review)

CanLII Sues AI Chatbot Caseway Over Copyright Concerns in Legal Data Use

The Canadian Legal Information Institute (CanLII) has filed a lawsuit against Caseway AI, a legal research chatbot, claiming copyright infringement and terms-of-service violations due to unauthorized scraping of 3.5 million legal records. CanLII argues that its legal database enhancements, such as hyperlinks and error corrections, are copyrighted. In response, Caseway claims that it did not harm CanLII’s services and that both organizations share a mission to make legal information accessible. This case is part of a larger debate about AI’s use of public data and copyright in the age of generative AI.

Key Takeaways:

• CanLII has sued Caseway AI for scraping its legal database, alleging copyright infringement.
• Caseway argues that it is working toward a shared goal of making legal data accessible.
• This case highlights ongoing copyright and data-use debates related to generative AI.

(Source: CBC News)

Parti Québécois' Push for AI Over Immigration Sparks Debate on Workforce Solutions

In response to Quebec’s labor shortages, the Parti Québécois (PQ) has proposed automation and AI as alternatives to immigrant labor. The plan targets investments in manufacturing, retail, and agriculture, taking inspiration from automation trends in countries like South Korea and Japan. Experts, however, caution that automating critical roles in fields like healthcare and education presents unique challenges and cannot fully replace the need for immigrants. Business representatives also highlight high costs, taxes, and regulatory hurdles as obstacles for small businesses aiming to adopt AI solutions. While automation could play a role in Quebec’s economy, experts conclude that it cannot be a standalone solution to the province’s labor challenges.

Key Takeaways:

• The Parti Québécois proposes automation to address labor shortages, reducing reliance on immigration.
• Experts say essential jobs in healthcare and education are difficult to automate effectively.
• High costs and regulatory hurdles make automation challenging for smaller businesses, indicating a need for balanced workforce solutions.

(Source: CTV News)

Author: Malik Datardina, CPA, CA, CISA. Malik works at Auvenir as a GRC Strategist who is working to transform the engagement experience for accounting firms and their clients. The opinions expressed here do not necessarily represent UWCISA, UW, Auvenir (or its affiliates), CPA Canada or anyone else. This post was written with the assistance of an AI language model. The model provided suggestions and completions to help me write, but the final content and opinions are my own.