Tech Compliance Standards for Businesses

The Irish Government has just announced plans to introduce the Regulation of Artificial Intelligence Bill in its Spring 2025 legislative programme, a pivotal piece of legislation aimed at giving full effect to the European Union’s Artificial Intelligence Act (EU Regulation 2024/1689). Even though the AI Act as a regulation has direct effect, this move is set to shape the national regulatory framework for AI governance in Ireland and establish national enforcement mechanisms in line with the EU’s approach. At the heart of the bill is the designation of Ireland’s National Competent Authorities: the entities that will be responsible for enforcing compliance with the AI Act. These authorities will oversee risk classification, conduct market surveillance, and impose penalties for violations. Given Ireland’s role as the EU base for major technology firms including Google, Anthropic, Meta, and TikTok, the effectiveness of its enforcement regime will be closely scrutinised across the EU and beyond. The Irish Government’s approach will be particularly significant due to the country’s track record in regulating the digital sector. Ireland’s Data Protection Commission (DPC) has wielded considerable influence over EU-wide enforcement of the GDPR, given the presence of multinational tech firms within the state. The DPC was designated as one of ireland’s nine fundamental rights authorities under the AI Act in November 2024. The bill will include provisions for penalties, though details remain unspecified. Under the EU AI Act, non-compliance can result in fines of up to €35 million or 7% of a company’s global annual turnover, whichever is higher. For Ireland, the challenge will be ensuring its enforcement framework has sufficient resources and expertise to oversee AI systems deployed within its jurisdiction. Tech industry leaders and legal experts will be closely monitoring how Ireland structures its national framework. The AI Act imposes strict obligations on high-risk AI applications, including those used in healthcare, banking, and recruitment. Companies will be required to maintain transparency, conduct impact assessments, and ensure that their AI systems do not lead to unlawful discrimination or harm. Ireland’s legislative initiative comes at a time of growing regulatory scrutiny over AI’s impact on society, innovation, and human rights. The AI Act represents the world’s most comprehensive attempt to regulate artificial intelligence, at a time other jurisdictions such as the USA are moving in the opposite regulatory direction. The Regulation of Artificial Intelligence Bill is still in its early stages, at the “Heads in Preparation” point. In the Irish legislative process, the Heads of a Bill serve as a blueprint for the eventual legislation. As Ireland moves toward full implementation of the AI Act, the government’s decisions on AI oversight will have significant implications for businesses, consumers, and the broader EU regulatory landscape.

One of the first mistakes I made when launching my first regulated business was delegating compliance. I started with TransferTo, a mobile micro value transfer service, which wasn’t regulated. Eventually, TransferTo split into two branches (now DT One and Thunes), with the new branch handling actual money transfers that required regulatory compliance. At that time, I thought, "I'll hire a Chief Compliance Officer and let them set up the function," just as I did with marketing or tech. That was a mistake. I faced significant challenges in opening a bank account because I hadn't fully mastered my processes. I also had a hard time communicating with my compliance officer. I didn't have the words or the right codes. Regulatory compliance is ultimately the responsibility of the company and its leadership—it cannot be outsourced. As a CEO, I believe it's essential to make the effort to understand it because the risks for the company are too significant. The least severe risk is a fine. The moderate risk is a suspension of the license. The most severe risk is revocation, or even imprisonment. To effectively manage these risks, I believe it's the CEO's duty to establish the compliance framework. Get your hands dirty. Understand the mechanics. Then, the Chief Compliance Officer can execute your plan. And this is exactly what regulators expect. The CEO's ability to manage compliance is one of the key aspects they evaluate when you apply for a licence. They don't require you to know how to code, but they do expect you to fully understand your company's compliance. If I have one piece of advice for a fintech entrepreneur: invest in compliance. The stakes are too high. As a startup, it could destroy your business. As a scale-up, it could strongly hinder your growth.

Alberta Just Told Data Centres: You’re Not Loads, You’re Grid Actors Alberta is drawing the line: data centres must act like generation if they want to connect. AESO’s draft Connection Requirements for Transmission-Connected Data Centres (TCDCs) rewrite what it means to be a ‘load. This isn’t just guidance. It’s the blueprint for binding rules. Core Rules for Data Centres: ➤ Ramping capped at 10 MW/min. AI clusters can ramp 100+ MW in seconds, but Alberta says: slow down. Compute must move at grid speed, not machine speed. ➤ Ride-through enforced. Ride through voltage sags below 45% of normal for 0.15 seconds, frequency swings as low as 57 Hz for nearly 5 minutes, and RoCoF up to 5 Hz/s. No disappearing acts. In practice: data centres must survive faults that would trip an industrial site because dropping hundreds of MW instantly is worse than riding through. ➤ Reactive power is mandatory. ±0.95 Power Factor with sub-second response. Loads must hold up voltages. ➤ Oscillations restricted. Net variability must stay below 16 kW per 100 ms and forced oscillations in the sub-synchronous band must stay under ±160 kW. Harmonics must be measured, reported, mitigated. Stability is not optional. ➤ Load shedding built in. Centres must trip portions of demand on command. And then come the quiet revolutions: • Backup power is emergency-only, no gensets tariff games. • ≥300 MW loads require dual SCADA paths; ≥500 MW must build physically diverse telecoms. Grid visibility is non-negotiable. • Every site must hand over EMT and phasor models, validated against real disturbance tests. Paper is dead; proof is alive. • Planning anchors are explicit: MSDC = 200 MW, Ramp30 = 300 MW/30 min. Why this matters: Alberta’s record peak demand is just 12.4 GW (Jan 2024), on a system with limited interties: one main 500 kV AC intertie to BC plus smaller AC links, including to Montana. Compare that to: • ERCOT, where summer peaks now push 90–100 GW • PJM, where summer peaks exceed 160 GW, with ~185 GW installed capacity Scale Matters: ▪ In ERCOT, the sudden trip of a 500 MW load is background noise. ▪ In Alberta, it’s a province-wide event, the equivalent of losing ~4% of system demand in an instant. That’s why AESO isn’t waiting for NERC’s 2026 guideline. It’s moving first. Each rule targets risks NERC already flagged: ramping, ride-through, SCADA, oscillations. This isn’t guesswork. It’s local action built on continental risk frameworks. This is Alberta drawing a line before hyperscale AI, crypto, and cloud reshape its grid. The real question is whether larger grids worldwide will act or wait until instability makes the choice for them. My view: This is the start of a new era. Programmable demand is no longer a silent passenger. It’s a grid actor, with obligations. 👉 The question is: will larger grids act before instability makes the choice for them? #DataCenters #AI #PowerSystems #GridStability #Policy #EnergyTransition #SystemStrength

NIS2 vs. DORA vs. CRA – Three major EU cybersecurity laws, but which one actually applies to you, and what’s the difference? Let’s break it down: 1. Scope and Applicability ↳ Network and Information Security Directive 2 (NIS2): Strengthens cybersecurity in critical sectors like energy, healthcare, and transport. ↳ Digital Operational Resilience Act (DORA): Ensures digital operational resilience for financial institutions, including banks, insurers, and ICT providers. ↳ Cyber Resilience Act (CRA): Regulates cybersecurity for hardware and software products with digital elements sold in the EU. 2. Key Requirements ↳ NIS2: Requires risk management frameworks, mandatory incident reporting, and cross-border cooperation. ↳ DORA: Mandates ICT risk management, resilience testing, and oversight of third-party providers. ↳ CRA: Imposes security-by-design principles, vulnerability management, and update obligations for manufacturers. 3. Enforcement and Penalties ↳ NIS2: National authorities oversee compliance; penalties can reach €10M or 2% of global turnover. ↳ DORA: Financial regulators enforce rules, with fines for non-compliant ICT providers up to 1% of daily worldwide turnover. ↳ CRA: Market surveillance authorities ensure compliance; violations can result in fines up to €15M or 2.5% of global turnover. 4. Main Challenge ↳ NIS2: Implementation may vary across EU countries, potentially leading to inconsistencies. ↳ DORA: Strict compliance timelines and third-party oversight may create operational burdens. ↳ CRA: Ensuring uniform cybersecurity standards across diverse digital products may be complex. 👇Do you work with these regulations? What’s your biggest challenge? Let's discuss ♻️ Repost to help someone. 🔔 Follow Amine El Gzouli for more.

On August 1, 2024, the European Union's AI Act came into force, bringing in new regulations that will impact how AI technologies are developed and used within the E.U., with far-reaching implications for U.S. businesses. The AI Act represents a significant shift in how artificial intelligence is regulated within the European Union, setting standards to ensure that AI systems are ethical, transparent, and aligned with fundamental rights. This new regulatory landscape demands careful attention for U.S. companies that operate in the E.U. or work with E.U. partners. Compliance is not just about avoiding penalties; it's an opportunity to strengthen your business by building trust and demonstrating a commitment to ethical AI practices. This guide provides a detailed look at the key steps to navigate the AI Act and how your business can turn compliance into a competitive advantage. 🔍 Comprehensive AI Audit: Begin with thoroughly auditing your AI systems to identify those under the AI Act’s jurisdiction. This involves documenting how each AI application functions and its data flow and ensuring you understand the regulatory requirements that apply. 🛡️ Understanding Risk Levels: The AI Act categorizes AI systems into four risk levels: minimal, limited, high, and unacceptable. Your business needs to accurately classify each AI application to determine the necessary compliance measures, particularly those deemed high-risk, requiring more stringent controls. 📋 Implementing Robust Compliance Measures: For high-risk AI applications, detailed compliance protocols are crucial. These include regular testing for fairness and accuracy, ensuring transparency in AI-driven decisions, and providing clear information to users about how their data is used. 👥 Establishing a Dedicated Compliance Team: Create a specialized team to manage AI compliance efforts. This team should regularly review AI systems, update protocols in line with evolving regulations, and ensure that all staff are trained on the AI Act's requirements. 🌍 Leveraging Compliance as a Competitive Advantage: Compliance with the AI Act can enhance your business's reputation by building trust with customers and partners. By prioritizing transparency, security, and ethical AI practices, your company can stand out as a leader in responsible AI use, fostering stronger relationships and driving long-term success. #AI #AIACT #Compliance #EthicalAI #EURegulations #AIRegulation #TechCompliance #ArtificialIntelligence #BusinessStrategy #Innovation 

🗞️ A must-read for anyone interested in European AI governance right now: this study, drafted for the Committee on Industry, Research and Energy (ITRE) of the European Parliament by the Policy Department for Transformation, Innovation & Health 👉🏼Analyses how the AI Act adopted mid-2024 is articulated with other key EU digital regulations 🔎 Examines interactions with: • GDPR • Data Act (DA) • Data Governance Act (DGA) • Digital Services Act (DSA) • Digital Markets Act (DMA) • Cyber Resilience Act (CRA) • NIS2 Directive, the New Legislative Framework (NLF) and product-safety / digital-elements rules 📖 A timely document as the #EU faces the demanding task of building digital rules that the world still lacks, balancing innovation, transparency and fundamental rights. ➡️ creating a broad legal ecosystem connecting data, algorithms and human values. 🎯 3 goals • Ensure trustworthy #AI in Europe — safe, transparent, respectful of rights and EU values. • Foster innovation and competitiveness • Provide legal certainty through a proportionate, risk-based approach. 🗺️ The study maps the interplay among current acts: 🔹with GDPR – Encourage joint guidance between data-protection and AI authorities to simplify impact assessments and ensure consistent supervision across Member States. 🔹with Data Act -Streamline obligations on data quality and access so that compliance supports, rather than slows, AI innovation. -Coordinate governance to prevent duplication and promote data flows for trustworthy AI. 🔹with Data Governance Act -Build bridges between data-sharing frameworks & AI requirements through interoperable standards and clear responsibilities for data use. 🔹with DSA / DMA -Use platform transparency & risk-assessment mechanisms to reinforce, not duplicate, AI Act duties -promote a coherent, innovation-friendly environment for general-purpose models 🔹with CRA / NIS2 / NLF -Align product-safety, cybersecurity & AI conformity processes to create 1 coherent certification pathway for digital products. 👉🏼an #AI Act as integrated regulatory ecosystem covering data, algorithms, products, platforms and rights = smart coordination turning compliance into trust and competitiveness. Future model proposed : • Principle-based horizontal rules with sectoral modules • Clear layering — data → algorithms → systems → services • Aligned definitions & conformity regimes • Simplified compliance for SMEs, rigorous oversight for high-risk systems 🧭 Practical steps forward ▶️Short term: joint guidelines (AI Act / GDPR), shared sandboxes, harmonised templates. ⏩️Medium term: clarify mandates, connect conformity procedures. ⏭️Long term: build a unified digital framework linking data, AI and platform rules, strengthen international standardisation& partnerships. ➡️ AI for good, trustworthy by design, aligned with rights and values. 🙏🏻 Authors Hans Graux Krzysztof G. Nayana Murali Jonathan Cave Maarten Botterman

Monetary Authority of Singapore (MAS) has just issued a consultation paper on proposed Guidelines on AI Risk Management for the financial sector. The Guidelines will apply to all financial institutions and set out supervisory expectations on: 1. Oversight of AI risk management – roles of the Board and senior management, governance, and risk culture for AI use. 2. AI risk management systems, policies and procedures – firm-wide identification of AI use cases, AI inventories, and risk materiality assessments (impact, complexity, reliance). 3. AI life cycle controls, capabilities and capacities – controls for data management, fairness, transparency/explainability, human oversight, third-party risk, testing, monitoring and change management, applied proportionately to AI risk. The Guidelines are technology- and use-case agnostic, covering a broad range of AI applications, including generative AI and AI agents, and are intended to be proportionate to the size, nature and risk profile of each FI.

This Stanford study examined how six major AI companies (Anthropic, OpenAI, Google, Meta, Microsoft, and Amazon) handle user data from chatbot conversations.  Here are the main privacy concerns. 👀 All six companies use chat data for training by default, though some allow opt-out 👀 Data retention is often indefinite, with personal information stored long-term 👀 Cross-platform data merging occurs at multi-product companies (Google, Meta, Microsoft, Amazon) 👀 Children's data is handled inconsistently, with most companies not adequately protecting minors 👀 Limited transparency in privacy policies, which are complex and hard to understand and often lack crucial details about actual practices Practical Takeaways for Acceptable Use Policy and Training for nonprofits in using generative AI: ✅ Assume anything you share will be used for training - sensitive information, uploaded files, health details, biometric data, etc. ✅ Opt out when possible - proactively disable data collection for training (Meta is the one where you cannot) ✅ Information cascades through ecosystems - your inputs can lead to inferences that affect ads, recommendations, and potentially insurance or other third parties ✅ Special concern for children's data - age verification and consent protections are inconsistent Some questions to consider in acceptable use policies and to incorporate in any training. ❓ What types of sensitive information might your nonprofit staff  share with generative AI?  ❓ Does your nonprofit currently specifically identify what is considered “sensitive information” (beyond PID) and should not be shared with GenerativeAI ? Is this incorporated into training? ❓ Are you working with children, people with health conditions, or others whose data could be particularly harmful if leaked or misused? ❓ What would be the consequences if sensitive information or strategic organizational data ended up being used to train AI models? How might this affect trust, compliance, or your mission? How is this communicated in training and policy? Across the board, the Stanford research points that developers’ privacy policies lack essential information about their practices. They recommend policymakers and developers address data privacy challenges posed by LLM-powered chatbots through comprehensive federal privacy regulation, affirmative opt-in for model training, and filtering personal information from chat inputs by default. “We need to promote innovation in privacy-preserving AI, so that user privacy isn’t an afterthought." How are you advocating for privacy-preserving AI? How are you educating your staff to navigate this challenge? https://lnkd.in/g3RmbEwD

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

✴ AI Governance Blueprint via ISO Standards – The 4-Legged Stool✴ ➡ ISO42001: The Foundation for Responsible AI #ISO42001 is dedicated to AI governance, guiding organizations in managing AI-specific risks like bias, transparency, and accountability. Focus areas include: ✅Risk Management: Defines processes for identifying and mitigating AI risks, ensuring systems are fair, robust, and ethically aligned. ✅Ethics and Transparency: Promotes policies that encourage transparency in AI operations, data usage, and decision-making. ✅Continuous Monitoring: Emphasizes ongoing improvement, adapting AI practices to address new risks and regulatory updates. ➡#ISO27001: Securing the Data Backbone AI relies heavily on data, making ISO27001’s information security framework essential. It protects data integrity through: ✅Data Confidentiality and Integrity: Ensures data protection, crucial for trustworthy AI operations. ✅Security Risk Management: Provides a systematic approach to managing security risks and preparing for potential breaches. ✅Business Continuity: Offers guidelines for incident response, ensuring AI systems remain reliable. ➡ISO27701: Privacy Assurance in AI #ISO27701 builds on ISO27001, adding a layer of privacy controls to protect personally identifiable information (PII) that AI systems may process. Key areas include: ✅Privacy Governance: Ensures AI systems handle PII responsibly, in compliance with privacy laws like GDPR. ✅Data Minimization and Protection: Establishes guidelines for minimizing PII exposure and enhancing privacy through data protection measures. ✅Transparency in Data Processing: Promotes clear communication about data collection, use, and consent, building trust in AI-driven services. ➡ISO37301: Building a Culture of Compliance #ISO37301 cultivates a compliance-focused culture, supporting AI’s ethical and legal responsibilities. Contributions include: ✅Compliance Obligations: Helps organizations meet current and future regulatory standards for AI. ✅Transparency and Accountability: Reinforces transparent reporting and adherence to ethical standards, building stakeholder trust. ✅Compliance Risk Assessment: Identifies legal or reputational risks AI systems might pose, enabling proactive mitigation. ➡Why This Quartet? Combining these standards establishes a comprehensive compliance framework: 🥇1. Unified Risk and Privacy Management: Integrates AI-specific risk (ISO42001), data security (ISO27001), and privacy (ISO27701) with compliance (ISO37301), creating a holistic approach to risk mitigation. 🥈 2. Cross-Functional Alignment: Encourages collaboration across AI, IT, and compliance teams, fostering a unified response to AI risks and privacy concerns. 🥉 3. Continuous Improvement: ISO42001’s ongoing improvement cycle, supported by ISO27001’s security measures, ISO27701’s privacy protocols, and ISO37301’s compliance adaptability, ensures the framework remains resilient and adaptable to emerging challenges.