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AI risk management and governance, so aligning your risk management policy means integrating AI-specific considerations alongside your existing risk framework. Here’s a structured approach:
1. Understand ISO 42001 Scope and Requirements
ISO 42001 sets standards for AI governance, risk management, and compliance across the AI lifecycle.
Key areas include:
Risk identification and assessment for AI systems.
Mitigation strategies for bias, errors, security, and ethical concerns.
Transparency, explainability, and accountability of AI models.
Compliance with legal and regulatory requirements (GDPR, EU AI Act, etc.).
2. Map Your Current Risk Policy
Identify where your existing policy addresses:
Risk assessment methodology
Roles and responsibilities
Monitoring and reporting
Incident response and corrective actions
Note gaps related to AI-specific risks, such as algorithmic bias, model explainability, or data provenance.
3. Integrate AI-Specific Risk Controls
AI Risk Identification: Add controls for data quality, model performance, and potential bias.
Risk Assessment: Include likelihood, impact, and regulatory consequences of AI failures.
Mitigation Strategies: Document methods like model testing, monitoring, human-in-the-loop review, or bias audits.
Governance & Accountability: Assign clear ownership for AI system oversight and compliance reporting.
4. Ensure Regulatory and Ethical Alignment
Map your AI systems against applicable standards:
EU AI Act (high-risk AI systems)
GDPR or HIPAA for data privacy
ISO 31000 for general risk management principles
Document how your policy addresses ethical AI principles, including fairness, transparency, and accountability.
5. Update Policy Language and Procedures
Add a dedicated “AI Risk Management” section to your policy.
Include:
Scope of AI systems covered
Risk assessment processes
Monitoring and reporting requirements
Training and awareness for stakeholders
Ensure alignment with ISO 42001 clauses (risk identification, evaluation, mitigation, monitoring).
6. Implement Monitoring and Continuous Improvement
Establish KPIs and metrics for AI risk monitoring.
Include regular audits and reviews to ensure AI systems remain compliant.
Integrate lessons learned into updates of the policy and risk register.
7. Documentation and Evidence
Keep records of:
AI risk assessments
Mitigation plans
Compliance checks
Incident responses
This will support ISO 42001 certification or internal audits.
Karen Hao’s Empire of AI provides a critical lens on the current AI landscape, questioning what intelligence truly means in these systems. Hao explores how AI is often framed as an extraordinary form of intelligence, yet in reality, it remains highly dependent on the data it is trained on and the design choices of its creators.
She highlights the ways companies encourage users to adopt AI tools, not purely for utility, but to collect massive amounts of data that can later be monetized. This approach, she argues, blurs the line between technological progress and corporate profit motives.
According to Hao, the AI industry often distorts reality. She describes AI as overhyped, framing the movement almost as a quasi-religious phenomenon. This hype, she suggests, fuels unrealistic expectations both among developers and the public.
Within the AI discourse, two camps emerge: the “boomers” and the “doomers.” Boomers herald AI as a new form of superior intelligence that can solve all problems, while doomers warn that this same intelligence could ultimately be catastrophic. Both, Hao argues, exaggerate what AI can actually do.
Prominent figures sometimes claim that AI possesses “PhD-level” intelligence, capable of performing complex, expert-level tasks. In practice, AI systems often succeed or fail depending on the quality of the data they consume—a vulnerability when that data includes errors or misinformation.
Hao emphasizes that the hype around AI is driven by money and venture capital, not by a transformation of the economy. According to her, Silicon Valley’s culture thrives on exaggeration: bigger models, more data, and larger data centers are marketed as revolutionary, but these features alone do not guarantee real-world impact.
She also notes that technology is not omnipotent. AI is not independently replacing jobs; company executives make staffing decisions. As people recognize the limits of AI, they can make more informed, “intelligent” choices themselves, countering some of the fears and promises surrounding automation.
OpenAI exemplifies these tensions. Founded as a nonprofit intended to counter Silicon Valley’s profit-driven AI development, it quickly pivoted toward a capitalistic model. Today, OpenAI is valued around $300–400 billion, and its focus is on data and computing power rather than purely public benefit, reflecting the broader financial incentives in the AI ecosystem.
Hao likens the AI industry to 18th-century colonialism: labor exploitation, monopolization of energy resources, and accumulation of knowledge and talent in wealthier nations echo historical imperial practices. This highlights that AI’s growth has social, economic, and ethical consequences far beyond mere technological achievement.
Hao’s analysis shows that AI, while powerful, is far from omnipotent. The overhype and marketing-driven narrative can weaken society by creating unrealistic expectations, concentrating wealth and power in the hands of a few corporations, and masking the social and ethical costs of these technologies. Instead of empowering people, it can distort labor markets, erode worker rights, and foster dependence on systems whose decision-making processes are opaque. A society that uncritically embraces AI risks being shaped more by financial incentives than by human-centered needs.
Today’s AI can perform impressive feats—from coding and creating images to diagnosing diseases and simulating human conversation. While these capabilities offer huge benefits, AI could be misused, from autonomous weapons to tools that spread misinformation and destabilize societies. Experts like Elon Musk and Geoffrey Hinton echo these concerns, advocating for regulations to keep AI safely under human control.
Hackers breached a third-party contact center platform, stealing data from 6M customers. No credit cards or passwords were exposed, but the board still cut senior leader bonuses by 15%. The CEO alone lost A$250,000.
This isn’t just an airline problem. It’s a wake-up call: boards are now holding executives financially accountable for cyber failures.
Key lessons for leaders: 🔹 Harden your help desk – add multi-step verification, ban one-step resets. 🔹 Do a vendor “containment sweep” – limit what customer data sits in third-party tools. 🔹 Prep customer comms kits – be ready to notify with clarity and speed. 🔹 Minimize sensitive data – don’t let vendors store more than they need. 🔹 Enforce strong controls – MFA, device trust checks, and callback verification. 🔹 Report to the board – show vendor exposure, tabletop results, and timelines.
My take: Boards are done treating cybersecurity as “someone else’s problem.” Linking executive pay to cyber resilience is the fastest way to drive accountability. If you’re an executive, assume vendor platforms are your systems—because when they fail, you’re the one explaining it to customers and shareholders.
Here’s a layered diagram showing how an AI system provider can build a deeply decarbonized data center — starting from clean energy supply at the outer layer down to handling residual emissions at the core.
AI data centers are the backbone of modern artificial intelligence—but they come with a growing list of side effects that are raising eyebrows across environmental, health, and policy circles. Here’s a breakdown of the most pressing concerns:
⚡ Environmental & Energy Impacts
Massive energy consumption: AI workloads require high-performance computing, which dramatically increases electricity demand. This strains local grids and often leads to reliance on fossil fuels.
Water usage for cooling: Many data centers use evaporative cooling systems, consuming millions of gallons of water annually—especially problematic in drought-prone regions.
Carbon emissions: Unless powered by renewables, data centers contribute significantly to greenhouse gas emissions, undermining climate goals
An AI system provider can build a deeply decarbonized data center by designing it to minimize greenhouse gas emissions across its full lifecycle—construction, energy use, and operations. Here’s how:
Power Supply (Clean Energy First)
Run entirely on renewable electricity (solar, wind, hydro, geothermal).
Use power purchase agreements (PPAs) or direct renewable energy sourcing.
Design for 24/7 carbon-free energy rather than annual offsets.
Efficient Infrastructure
Deploy high-efficiency cooling systems (liquid cooling, free-air cooling, immersion).
Optimize server utilization (AI workload scheduling, virtualization, consolidation).
Use energy-efficient chips/accelerators designed for AI workloads.
Sustainable Building Design
Construct facilities with low-carbon materials (green concrete, recycled steel).
Maximize modular and prefabricated components to cut waste.
Use circular economy practices for equipment reuse and recycling.
Carbon Capture & Offsets (Residual Emissions)
Where emissions remain (backup generators, construction), apply carbon capture or credible carbon removal offsets.
Water & Heat Management
Implement closed-loop water cooling to minimize freshwater use.
Recycle waste heat to warm nearby buildings or supply district heating.
Smart Operations
Apply AI-driven energy optimization to reduce idle consumption.
Dynamically shift workloads to regions/times where renewable energy is abundant.
Supply Chain Decarbonization
Work with hardware vendors committed to net-zero manufacturing.
Require carbon transparency in procurement.
👉 In short: A deeply decarbonized AI data center runs on clean energy, uses ultra-efficient infrastructure, minimizes embodied carbon, and intelligently manages workloads and resources.
Disproportionate impact on marginalized communities: Many data centers are built in areas with existing environmental burdens, compounding risks for vulnerable populations.
Land use and displacement: Large-scale facilities can disrupt ecosystems and push out local residents or businesses.
Transparency issues: Communities often lack access to information about the risks and benefits of hosting data centers, leading to mistrust and resistance.
🔋 Strategic & Policy Challenges
Energy grid strain: The rapid expansion of AI infrastructure is pushing governments to consider controversial solutions like small modular nuclear reactors.
Regulatory gaps: Current zoning and environmental regulations may not be equipped to handle the scale and speed of AI data center growth.
The SANS Institute has unveiled its “Own AI Securely” blueprint, a strategic framework designed to help organizations integrate artificial intelligence (AI) securely and responsibly. This initiative addresses the growing concerns among Chief Information Security Officers (CISOs) about the rapid adoption of AI technologies without corresponding security measures, which has created vulnerabilities that cyber adversaries are quick to exploit.
A significant challenge highlighted by SANS is the speed at which AI-driven attacks can occur. Research indicates that such attacks can unfold more than 40 times faster than traditional methods, making it difficult for defenders to respond promptly. Moreover, many Security Operations Centers (SOCs) are incorporating AI tools without customizing them to their specific needs, leading to gaps in threat detection and response capabilities.
To mitigate these risks, the blueprint proposes a three-part framework: Protect AI, Utilize AI, and Govern AI. The “Protect AI” component emphasizes securing models, data, and infrastructure through measures such as access controls, encryption, and continuous monitoring. It also addresses emerging threats like model poisoning and prompt injection attacks.
The “Utilize AI” aspect focuses on empowering defenders to leverage AI in enhancing their operations. This includes integrating AI into detection and response systems to keep pace with AI-driven threats. Automation is encouraged to reduce analyst workload and expedite decision-making, provided it is implemented carefully and monitored closely.
The “Govern AI” segment underscores the importance of establishing clear policies and guidelines for AI usage within organizations. This includes defining acceptable use, ensuring compliance with regulations, and maintaining transparency in AI operations.
Rob T. Lee, Chief of Research and Chief AI Officer at SANS Institute, advises that CISOs should prioritize investments that offer both security and operational efficiency. He recommends implementing an adoption-led control plane that enables employees to access approved AI tools within a protected environment, ensuring security teams maintain visibility into AI operations across all data domains.
In conclusion, the SANS AI security blueprint provides a comprehensive approach to integrating AI technologies securely within organizations. By focusing on protection, utilization, and governance, it offers a structured path to mitigate risks associated with AI adoption. However, the success of this framework hinges on proactive implementation and continuous monitoring to adapt to the evolving threat landscape.
AIMS and Data Governance – Managing data responsibly isn’t just good practice—it’s a legal and ethical imperative.
ISO 42001—the first international standard for managing artificial intelligence. Developed for organizations that design, deploy, or oversee AI, ISO 42001 is set to become the ISO 9001 of AI: a universal framework for trustworthy, transparent, and responsible AI.
“Whether you’re a technology professional, policymaker, academic, or simply a curious reader, this book will arm you with the knowledge to navigate the complex intersection of AI, security, and society.”
Overview: This academic paper examines the growing ethical and regulatory challenges brought on by AI’s integration with cybersecurity. It traces the evolution of AI regulation, highlights pressing concerns—like bias, transparency, accountability, and data privacy—and emphasizes the tension between innovation and risk mitigation.
Key Insights:
AI systems raise unique privacy/security issues due to their opacity and lack of human oversight.
Current regulations are fragmented—varying by sector—with no unified global approach.
Bridging the regulatory gap requires improved AI literacy, public engagement, and cooperative policymaking to shape responsible frameworks.
Source: Authored by Vikram Kulothungan, published in January 2025, this paper cogently calls for a globally harmonized regulatory strategy and multi-stakeholder collaboration to ensure AI’s secure deployment.
Why This Post Stands Out
Comprehensive: Tackles both cybersecurity and privacy within the AI context—not just one or the other.
Forward-Looking: Addresses systemic concerns, laying the groundwork for future regulation rather than retrofitting rules around current technology.
Action-Oriented: Frames AI regulation as a collaborative challenge involving policymakers, technologists, and civil society.
Additional Noteworthy Commentary on AI Regulation
1. Anthropic CEO’s NYT Op-ed: A Call for Sensible Transparency
Anthropic CEO Dario Amodei criticized a proposed 10-year ban on state-level AI regulation as “too blunt.” He advocates a federal transparency standard requiring AI developers to disclose testing methods, risk mitigation, and pre-deployment safety measures.
2. California’s AI Policy Report: Guarding Against Irreversible Harms
A report commissioned by Governor Newsom warns of AI’s potential to facilitate biological and nuclear threats. It advocates “trust but verify” frameworks, increased transparency, whistleblower protections, and independent safety validation.
3. Mutually Assured Deregulation: The Risks of a Race Without Guardrails
Gilad Abiri argues that dismantling AI safety oversight in the name of competition is dangerous. Deregulation doesn’t give lasting advantages—it undermines long-term security, enabling proliferation of harmful AI capabilities like bioweapon creation or unstable AGI.
Broader Context & Insights
Fragmented Landscape: U.S. lacks unified privacy or AI laws; even executive orders remain limited in scope.
Data Risk: Many organizations suffer from unintended AI data exposure and poor governance despite having some policies in place.
Regulatory Innovation: Texas passed a law focusing only on government AI use, signaling a partial step toward regulation—but private sector oversight remains limited.
International Efforts: The Council of Europe’s AI Convention (2024) is a rare international treaty aligning AI development with human rights and democratic values.
Research Proposals: Techniques like blockchain-enabled AI governance are being explored as transparency-heavy, cross-border compliance tools.
Opinion
AI’s pace of innovation is extraordinary—and so are its risks. We’re at a crossroads where lack of regulation isn’t a neutral stance—it accelerates inequity, privacy violations, and even public safety threats.
What’s needed:
Layered Regulation: From sector-specific rules to overarching international frameworks; we need both precision and stability.
Transparency Mandates: Companies must be held to explicit standards—model testing practices, bias mitigation, data usage, and safety protocols.
Public Engagement & Literacy: AI literacy shouldn’t be limited to technologists. Citizens, policymakers, and enforcement institutions must be equipped to participate meaningfully.
Safety as Innovation Avenue: Strong regulation doesn’t kill innovation—it guides it. Clear rules create reliable markets, investor confidence, and socially acceptable products.
The paper “Securing the AI Frontier” sets the right tone—urging collaboration, ethics, and systemic governance. Pair that with state-level transparency measures (like Newsom’s report) and critiques of over-deregulation (like Abiri’s essay), and we get a multi-faceted strategy toward responsible AI.
Connected vehicles have rapidly proliferated across Europe, brimming with sophisticated software, myriad sensors, and continuous connectivity. While these advancements deliver conveniences like remote control features and intelligent navigation, they simultaneously expand the vehicle’s digital attack surface, what enhances “smartness” inherently introduces fresh cybersecurity vulnerabilities.
A recent study — both technical and survey-based — questioned roughly 300 mostly European participants about their awareness and attitudes regarding smart-car security and privacy. The findings indicate that most people understand their vehicles share data with both manufacturers and third parties, particularly those driving newer models. Western Europeans showed greater awareness of these data flows than respondents from Eastern Europe.
Despite rising awareness, many drivers lack clarity about what precisely “smart car” entails. Consumers tend to emphasize visible functionalities — such as self-driving aids or entertainment systems — while overlooking the less visible but critical issue of how data is managed, stored, or potentially exploited.
The existing regulatory environment is striving to catch up. Frameworks like UN R155 and R156, already in effect, mandate systematic cybersecurity management and secure software update mechanisms for connected cars. Similarly, from July 2024, EU rules require that new vehicles cannot be registered unless they guarantee robust cybersecurity—pushing automakers toward ‘security by design.
Moreover, Europe is developing additional protective technologies. For example, the EU-funded SELFY project is building a toolkit to safeguard connected traffic systems, aiming to issue cybersecurity certificates and bolster defenses against cyber threats. The European Commission is also establishing protocols around testing, data recording, safety monitoring, and incident reporting for advanced automated and driverless vehicle systems.
Nevertheless, gaps remain—particularly between policy progress and public trust. Even as regulations evolve and technical tools mature, many vehicle users remain uncertain about the extent of data collection, storage, and sharing. Without stronger transparency, consumer trust is likely to lag behind technological and regulatory advancements.
Car Security and Privacy
Connected cars represent a defining shift in the mobility landscape—offering unprecedented convenience but accompanied by elevated risks. The central paradox is clear: as vehicles become more connected and intelligent, they become more exposed. This isn’t just a matter of potential remote hacking; it’s about data flow—where, how, and by whom vehicle data is used.
Europe is taking commendable steps by enforcing cybersecurity mandates (like R155/R156) and promoting proactive, security-by-design approaches. Projects like SELFY and structured regulatory initiatives around automated vehicles signal forward motion.
However, the real challenge lies in closing the trust gap. Many drivers still don’t have a clear understanding of data practices. Communicating complex cybersecurity architecture in accessible terms is essential. Automakers and regulators must both educate and reassure—perhaps through public dashboards, standardized labels on data practices, or periodic transparency reports that explain what data is collected, why, who has access, and how it’s protected.
For drivers, vigilance remains crucial. Prioritize vehicles that support secure over-the-air updates, enforce two-factor authentication for vehicle apps, and carefully review privacy settings. As consumers, push for clarity and accountability—our vehicles shouldn’t just be smart; they should also be secure and respectful of our privacy.
ISO 42001 is the upcoming standard for AI Management Systems (AIMS), similar in structure to ISO 27001 for information security. While the full standard is not yet widely published, the main requirements for an internal audit of an ISO 42001 AIMS can be outlined based on common audit principles and the expected clauses in the standard. Here’s a structured view:
1. Audit Scope and Objectives
Define what parts of the AI management system will be audited (processes, teams, AI models, AI governance, data handling, etc.).
Ensure the audit covers all ISO 42001 clauses relevant to your organization.
Determine audit objectives, e.g.,:
Compliance with ISO 42001.
Effectiveness of risk management for AI.
Alignment with organizational AI strategy and policies.
2. Compliance with AIMS Requirements
Check whether the organization’s AI management system meets ISO 42001 requirements, which likely include:
AI governance framework.
Risk management for AI (AI lifecycle, bias, safety, privacy).
Policies and procedures for AI development, deployment, and monitoring.
Data management and ethical AI principles.
Roles, responsibilities, and competency requirements for AI personnel.
3. Documentation and Records
Verify that documentation exists and is maintained, e.g.:
AI policies, procedures, and guidelines.
Risk assessments, impact assessments, and mitigation plans.
Training records and personnel competency evaluations.
Records of AI incidents, anomalies, or failures.
Audit logs of AI models and data handling activities.
4. Risk Management and Controls
Review whether risks related to AI (bias, safety, security, privacy) are identified, assessed, and mitigated.
Check implementation of controls:
Data quality and integrity controls.
Model validation and testing.
Human oversight and accountability mechanisms.
Compliance with relevant regulations and ethical standards.
5. Performance Monitoring and Improvement
Evaluate monitoring and measurement processes:
Metrics for AI model performance and compliance.
Monitoring of ethical and legal adherence.
Feedback loops for continuous improvement.
Assess whether corrective actions and improvements are identified and implemented.
6. Internal Audit Process Requirements
Audits should be planned, objective, and systematic.
Auditors must be independent of the area being audited.
Audit reports must include:
Findings (compliance, nonconformities, opportunities for improvement).
Recommendations.
Follow-up to verify closure of nonconformities.
7. Management Review Alignment
Internal audit results should feed into management reviews for:
AI risk mitigation effectiveness.
Resource allocation.
Policy updates and strategic AI decisions.
Key takeaway: An ISO 42001 internal audit is not just about checking boxes—it’s about verifying that AI systems are governed, ethical, and risk-managed throughout their lifecycle, with evidence, controls, and continuous improvement in place.
An Internal Audit agreement aligned with ISO 42001 should include the following key components, each described below to ensure clarity and operational relevance:
🧭 Scope of Services
The agreement should clearly define the consultant’s role in leading and advising the internal audit team. This includes directing the audit process, training team members on ISO 42001 methodologies, and overseeing all phases—from planning to reporting. It should also specify advisory responsibilities such as interpreting ISO 42001 requirements, identifying compliance gaps, and validating governance frameworks. The scope must emphasize the consultant’s authority to review and approve all audit work to ensure alignment with professional standards.
📄 Deliverables
A detailed list of expected outputs should be included, such as a comprehensive audit report with an executive summary, gap analysis, and risk assessment. The agreement should also cover a remediation plan with prioritized actions, implementation guidance, and success metrics. Supporting materials like policy templates, training recommendations, and compliance monitoring frameworks should be outlined. Finally, it should ensure the development of a capable internal audit team and documentation of audit procedures for future use.
⏳ Timeline
The agreement must specify key milestones, including project start and completion dates, training deadlines, audit phase completion, and approval checkpoints for draft and final reports. This timeline ensures accountability and helps coordinate internal resources effectively.
💰 Compensation
This section should detail the total project fee, payment terms, and a milestone-based payment schedule. It should also clarify reimbursable expenses (e.g., travel) and note that internal team costs and facilities are the client’s responsibility. Transparency in financial terms helps prevent disputes and ensures mutual understanding.
👥 Client Responsibilities
The client’s obligations should be clearly stated, including assigning qualified internal audit team members, ensuring their availability, designating a project coordinator, and providing access to necessary personnel, systems, and facilities. The agreement should also require timely feedback on deliverables and commitment from the internal team to complete audit tasks under the consultant’s guidance.
🎓 Consultant Responsibilities
The consultant’s duties should include providing expert leadership, training the internal team, reviewing and approving all work products, maintaining quality standards, and being available for ongoing consultation. This ensures the consultant remains accountable for the integrity and effectiveness of the audit process.
🔐 Confidentiality
A robust confidentiality clause should protect proprietary information shared during the engagement. It should specify the duration of confidentiality obligations post-engagement and ensure that internal audit team members are bound by equivalent terms. This builds trust and safeguards sensitive data.
💡 Intellectual Property
The agreement should clarify ownership of work products, stating that outputs created by the internal team under the consultant’s guidance belong to the client. It should also allow the consultant to retain general methodologies and templates for future use, while jointly owning training materials and audit frameworks.
⚖️ Limitation of Liability
This clause should cap the consultant’s liability to the total fee paid and exclude consequential or punitive damages. It should reinforce that ISO 42001 compliance is ultimately the client’s responsibility, with the consultant providing guidance and oversight—not execution.
🛑 Termination
The agreement should include provisions for termination with advance notice, payment for completed work, delivery of all completed outputs, and survival of confidentiality obligations. It should also ensure that any training and knowledge transfer remains with the client post-termination.
📜 General Terms
Standard legal provisions should be included, such as independent contractor status, governing law, severability, and a clause stating that the agreement represents the entire understanding between parties. These terms provide legal clarity and protect both sides.
BruteForceAI is a free, open-source penetration testing tool that enhances traditional brute-force attacks by integrating large language models (LLMs). It automates identification of login form elements—such as username and password fields—by analyzing HTML content and deducing the correct selectors.
After mapping out the login structure, the tool conducts multi-threaded brute-force or password-spraying attacks. It simulates human-like behavior by randomizing timing, introducing slight delays, and varying the user-agent—concealing its activity from conventional detection systems.
Intended for legitimate security use, BruteForceAI is geared toward authorized penetration testing, academic research, self-assessment of one’s applications, and participation in bug bounty programs—always within proper legal and ethical bounds. It is freely available on GitHub for practitioners to explore and deploy.
By combining intelligence-powered analysis and automated attack execution, BruteForceAI streamlines what used to be a tedious and manual process. It automates both discovery (login field detection) and exploitation (attack execution). This dual capability can significantly speed up testing workflows for security professionals.
BruteForceAI
BruteForceAI represents a meaningful leap in how penetration testers can validate and improve authentication safeguards. On the positive side, its automation and intelligent behavior modeling could expedite thorough and realistic attack simulations—especially useful for uncovering overlooked vulnerabilities hidden in login logic or form implementations.
That said, such power is a double-edged sword. There’s an inherent risk that malicious actors could repurpose the tool for unauthorized attacks, given its stealthy methods and automation. Its detection evasion tactics—mimicking human activity to avoid being flagged—could be exploited by bad actors to evade traditional defenses. For defenders, this heightens the importance of deploying robust controls like rate limiting, behavioral monitoring, anomaly detection, and multi-factor authentication.
In short, as a security tool it’s impressive and helpful—if used responsibly. Ensuring it remains in the hands of ethical professionals and not abused requires awareness, cautious deployment, and informed defense strategies.
Download
This tool is designed for responsible and ethical use, including authorized penetration testing, security research and education, testing your own applications, and participating in bug bounty programs within the proper scope.
Regulatory Alignment: ISO 42001 supports GDPR, HIPAA, and EU AI Act compliance.
Client Trust: Demonstrates responsible AI governance to enterprise clients.
Competitive Edge: Positions ShareVault as a forward-thinking, standards-compliant VDR provider.
Audit Readiness: Facilitates internal and external audits of AI systems and data handling.
If ShareVault were to pursue ISO 42001 certification, it would not only strengthen its AI governance but also reinforce its reputation in regulated industries like life sciences, finance, and legal services.
Here’s a tailored ISO/IEC 42001 implementation roadmap for a Virtual Data Room (VDR) provider like ShareVault, focusing on responsible AI governance, risk mitigation, and regulatory alignment.
🗺️ ISO/IEC 42001 Implementation Roadmap for ShareVault
Phase 1: Initiation & Scoping
🔹 Objective: Define the scope of AI use and align with business goals.
Identify AI-powered features (e.g., smart search, document tagging, access analytics).
The age of AI-assisted hacking is no longer looming—it’s here. Hackers of all stripes—from state actors to cybercriminals—are now integrating AI tools into their operations, while defenders are racing to catch up.
Key Developments
In mid‑2025, Russian intelligence reportedly sent phishing emails to Ukrainians containing AI-powered attachments that automatically scanned victims’ computers for sensitive files and transmitted them back to Russia. NBC Bay Area
AI models like ChatGPT have become highly adept at translating natural language into code, helping hackers automate their work and scale operations. NBC Bay Area
AI hasn’t ushered in a hacking revolution that enables novices to bring down power grids—but it is significantly enhancing the efficiency and reach of skilled hackers. NBC Bay Area
On the Defensive Side
Cybersecurity defenders are also turning to AI—Google’s “Gemini” model helped identify over 20 software vulnerabilities, speeding up bug detection and patching.
Alexei Bulazel of the White House’s National Security Council believes defenders currently hold a slight edge over attackers, thanks to America’s tech infrastructure, but that balance may shift as agentic (autonomous) AI tools proliferate.
A notable milestone: an AI called “Xbow” topped the HackerOne leaderboard, prompting the platform to create a separate category for AI-generated hacking tools.
My Take
This article paints a vivid picture of an escalating AI arms race in cybersecurity. My view? It’s a dramatic turning point:
AI is already tipping the scale—but not overwhelmingly. Hackers are more efficient, but full-scale automated digital threats haven’t arrived. Still, what used to require deep expertise is becoming accessible to more people.
Defenders aren’t standing idle. AI-assisted scanning and rapid vulnerability detection are powerful tools in the white-hat arsenal—and may remain decisive, especially when backed by robust tech ecosystems.
The real battleground is trust. As AI makes exploits more sophisticated and deception more believable (e.g., deepfakes or phishing), trust becomes the most vulnerable asset. This echoes broader reports showing attacks are increasingly AI‑powered, whether via deceptive audio/video or tailored phishing campaigns.
Vigilance must evolve. Automated defenses and rapid detection will be key. Organizations should also invest in digital literacy—training humans to recognize deception even as AI tools become ever more convincing.
Related Reading Highlights
Here are some recent news pieces that complement the NBC article, reinforcing the duality of AI’s role in cyber threats:
This book positions itself not just as a technical guide but as a strategic roadmap for the future of cybersecurity leadership. It emphasizes that in today’s complex threat environment, CISOs must evolve beyond technical mastery and step into the role of business leaders who weave cybersecurity into the very fabric of organizational strategy.
The core message challenges the outdated view of CISOs as purely technical experts. Instead, it calls for a strategic shift toward business alignment, measurable risk management, and adoption of emerging technologies like AI and machine learning. This evolution reflects growing expectations from boards, executives, and regulators—expectations that CISOs must now meet with business fluency, not just technical insight.
The book goes further by offering actionable guidance, case studies, and real-world examples drawn from extensive experience across hundreds of security programs. It explores practical topics such as risk quantification, cyber insurance, and defining materiality, filling the gap left by more theory-heavy resources.
For aspiring CISOs, the book provides a clear path to transition from technical expertise to strategic leadership. For current CISOs, it delivers fresh insight into strengthening business acumen and boardroom credibility, enabling them to better drive value while protecting organizational assets.
My thought: This book’s strength lies in recognizing that the modern CISO role is no longer just about defending networks but about enabling business resilience and trust. By blending strategy with technical depth, it seems to prepare security leaders for the boardroom-level influence they now require. In an era where cybersecurity is a business risk, not just an IT issue, this perspective feels both timely and necessary.
Harmonized rules for AI systems in the EU, prohibitions on certain AI practices, requirements for high risk AI, transparency rules, market surveillance, and innovation support.
1. Overview: How the AI Act Treats Open-Source vs. Closed-Source Models
The EU AI Act (formalized in 2024) regulates AI systems using a risk-based framework that ranges from unacceptable to minimal risk. It also includes a specific layer for general-purpose AI (GPAI)—“foundation models” like large language models.
Open-source models enjoy limited exemptions, especially if:
They’re not high-risk,
Not unsafe or interacting directly with individuals,
Not monetized,
Or not deemed to present systemic risk.
Closed-source (proprietary) models don’t benefit from such leniency and must comply with all applicable obligations across risk categories.
2. Benefits of Open-Source Models under the AI Act
a) Greater Transparency & Documentation
Open-source code, weights, and architecture are accessible by default—aligning with transparency expectations (e.g., model cards, training data logs)—and often already publicly documented.
Independent auditing becomes more feasible through community visibility.
A Stanford study found open-source models tend to comply more readily with data and compute transparency requirements than closed-source alternatives.
b) Lower Compliance Burden (in Certain Cases)
Exemptions: Non-monetized open-source models that don’t pose systemic risk may dodge burdensome obligations like documentation or designated representatives.
For academic or purely scientific purposes, there’s additional leniency—even if models are open-source.
c) Encourages Innovation, Collaboration & Inclusion
Open-source democratizes AI access, reducing barriers for academia, startups, nonprofits, and regional players.
Wider collaboration speeds up innovation and enables localization (e.g., fine-tuning for local languages or use cases).
Diverse contributors help surface bias and ethical concerns, making models more inclusive.
3. Drawbacks of Open-Source under the AI Act
a) Disproportionate Regulatory Burden
The Act’s “one-size-fits-all” approach imposes heavy requirements (like ten-year documentation, third-party audits) even on decentralized, collectively developed models—raising feasibility concerns.
Who carries responsibility in distributed, open environments remains unclear.
b) Loopholes and Misuse Risks
The Act’s light treatment of non-monetized open-source models could be exploited by malicious actors to skirt regulations.
Open-source models can be modified or misused to generate disinformation, deepfakes, or hate content—without safeguards that closed systems enforce.
c) Still Subject to Core Obligations
Even under exemptions, open-source GPAI must still:
Disclose training content,
Respect EU copyright laws,
Possibly appoint authorized representatives if systemic risk is suspected.
d) Additional Practical & Legal Complications
Licensing: Some so-called “open-source” models carry restrictive terms (e.g., commercial restrictions, copyleft provisions) that may hinder compliance or downstream use.
Support disclaimers: Open-source licenses typically disclaim warranties—risking liability gaps.
Security vulnerabilities: Public availability of code may expose models to tampering or release of harmful versions.
4. Closed-Source Models: Benefits & Drawbacks
Benefits
Able to enforce usage restrictions, internal safety mechanisms, and fine-grained control over deployment—reducing misuse risk.
Clear compliance path: centralized providers can manage documentation, audits, and risk mitigation systematically.
Stable liability chain, with better alignment to legal frameworks.
Drawbacks
Less transparency: core workings are hidden, making audits and oversight harder.
Higher compliance burden: must meet all applicable obligations across risk categories without the possibility of exemptions.
Innovation lock-in: smaller players and researchers may face high entry barriers.
5. Synthesis: Choosing Between Open-Source and Closed-Source under the AI Act
Dimension
Open-Source
Closed-Source
Transparency & Auditing
High—code, data, model accessible
Low—black box systems
Regulatory Burden
Lower for non-monetized, low-risk models; heavy for complex, high-risk cases
Uniformly high, though manageable by central entities
Under the EU AI Act, open-source AI is recognized and, in some respects, encouraged—but only under narrow, carefully circumscribed conditions. When models are non-monetized, low-risk, or aimed at scientific research, open-source opens up paths for innovation. The transparency and collaborative dynamics are strong virtues.
However, when open-source intersects with high risk, monetization, or systemic potential, the Act tightens its grip—subjecting models to many of the same obligations as proprietary ones. Worse, ambiguity in responsibility and enforcement may undermine both innovation and safety.
Conversely, closed-source models offer regulatory clarity, security, and control; but at the cost of transparency, higher compliance burden, and restricted access for smaller players.
TL;DR
Choose open-source if your goal is transparency, inclusivity, and innovation—so long as you keep your model non-monetized, transparently documented, and low-risk.
Choose closed-source when safety, regulatory oversight, and controlled deployment are paramount, especially in sensitive or high-risk applications.
As AI adoption accelerates, especially in regulated or high-impact sectors, the European Union is setting the bar for responsible development. Article 15 of the EU AI Act lays out clear obligations for providers of high-risk AI systems—focusing on accuracy, robustness, and cybersecurity throughout the AI system’s lifecycle. Here’s what that means in practice—and why it matters now more than ever.
1. Security and Reliability From Day One
The AI Act demands that high-risk AI systems be designed with integrity and resilience from the ground up. That means integrating controls for accuracy, robustness, and cybersecurity not only at deployment but throughout the entire lifecycle. It’s a shift from reactive patching to proactive engineering.
2. Accuracy Is a Design Requirement
Gone are the days of vague performance promises. Under Article 15, providers must define and document expected accuracy levels and metrics in the user instructions. This transparency helps users and regulators understand how the system should perform—and flags any deviation from those expectations.
3. Guarding Against Exploitation
AI systems must also be robust against manipulation, whether it’s malicious input, adversarial attacks, or system misuse. This includes protecting against changes to the AI’s behavior, outputs, or performance caused by vulnerabilities or unauthorized interference.
4. Taming Feedback Loops in Learning Systems
Some AI systems continue learning even after deployment. That’s powerful—but dangerous if not governed. Article 15 requires providers to minimize or eliminate harmful feedback loops, which could reinforce bias or lead to performance degradation over time.
5. Compliance Isn’t Optional—It’s Auditable
The Act calls for documented procedures that demonstrate compliance with accuracy, robustness, and security standards. This includes verifying third-party contributions to system development. Providers must be ready to show their work to market surveillance authorities (MSAs) on request.
6. Leverage the Cyber Resilience Act
If your high-risk AI system also falls under the scope of the EU Cyber Resilience Act (CRA), good news: meeting the CRA’s essential cybersecurity requirements can also satisfy the AI Act’s demands. Providers should assess the overlap and streamline their compliance strategies.
7. Don’t Forget the GDPR
When personal data is involved, Article 15 interacts directly with the GDPR—especially Articles 5(1)(d), 5(1)(f), and 32, which address accuracy and security. If your organization is already GDPR-compliant, you’re on the right track, but Article 15 still demands additional technical and operational precision.
Final Thought:
Article 15 raises the bar for how we build, deploy, and monitor high-risk AI systems. It doesn’t just aim to prevent failures—it pushes providers to deliver trustworthy, resilient, and secure AI from the start. For organizations that embrace this proactively, it’s not just about avoiding fines—it’s about building AI systems that earn trust and deliver long-term value.
Transforming Cybersecurity & Compliance into Strategic Strength
In an era of ever-tightening regulations and ever-evolving threats, Deura InfoSec Consulting (DISC LLC) stands out by turning compliance from a checkbox into a proactive asset.
🛡️ What We Offer: Core Services at a Glance
1. vCISO Services
Access seasoned CISO-level expertise—without the cost of a full-time executive. Our vCISO services provide strategic leadership, ongoing security guidance, executive reporting, and risk management aligned with your business needs.
2. Compliance & Certification Support
Whether you’re targeting ISO 27001, ISO 27701, ISO 42001, NIST, GDPR, SOC 2, HIPAA, or PCI DSS, DISC supports your entire journey—from assessments and gap analysis to policy creation, control implementation, and audit preparation.
3. Security Risk Assessments
Identify risks across infrastructure, cloud, vendors, and business-critical systems using frameworks such as MITRE ATT&CK (via CALDERA), with actionable risk scorecards and remediation roadmaps.
4. Risk‑based Strategic Planning
We bridge the gap from your current (“as‑is”) security state to your desired (“to‑be”) maturity level. Our process includes strategic roadmapping, metrics to measure progress, and embedding business-aligned security into operations.
5. Security Awareness & Training
Equip your workforce and leadership with tailored training programs—ranging from executive briefings to role-based education—in vital areas like governance, compliance, and emerging threats.
6. Penetration Testing & Tool Oversight
Using top-tier tools like Burp Suite Pro and OWASP ZAP, DISC uncovers vulnerabilities in web applications and APIs. These assessments are accompanied by remediation guidance and optional managed detection support.
7. At DISC LLC, we help organizations harness the power of data and artificial intelligence—responsibly. OurAIMS (Artificial Intelligence Management System) & Data Governance solutions are designed to reduce risk, ensure compliance, and build trust. We implement governance frameworks that align with ISO 27001, ISO 27701, ISO 42001, GDPR, EU AI ACT, HIPAA, and CCPA, supporting both data accuracy and AI accountability. From data classification policies to ethical AI guidelines, bias monitoring, and performance audits, our approach ensures your AI and data strategies are transparent, secure, and future-ready. By integrating AI and data governance, DISC empowers you to lead with confidence in a rapidly evolving digital world.
🔍 Why DISC Works
Fixed-fee, hands‑on approach: No bloated documents, just precise and efficient delivery aligned with your needs.
Expert-led services: With 20+ years in security and compliance, DISC’s consultants guide you at every stage.
Audit-ready processes: Leverage frameworks and tools like GRC platform to streamline compliance, reduce overhead, and stay audit-ready.
Tailored to SMBs & enterprises: From startups to established firms, DISC crafts solutions scalable to your size and skillset.
🚀 Ready to Elevate Your Security?
DISC LLC is more than a service provider—it’s your long-term advisor. Whether you’re combating cyber risk or scaling your compliance posture, our services deliver predictable value and empower you to make security a strategic advantage.
Get started today with a free consultation, including a one-hour session with a vCISO, to see where your organization stands—and where it needs to go.
IBM’s latest Cost of a Data Breach Report (2025) highlights a growing and costly issue: “shadow AI”—where employees use generative AI tools without IT oversight—is significantly raising breach expenses. Around 20% of organizations reported breaches tied to shadow AI, and those incidents carried an average $670,000 premium per breach, compared to firms with minimal or no shadow AI exposure IBM+Cybersecurity Dive.
The latest IBM/Ponemon Institute report reveals that the global average cost of a data breach fell by 9% in 2025, down to $4.44 million—the first decline in five years—mainly driven by faster breach identification and containment thanks to AI and automation. However, in the United States, breach costs surged 9%, reaching a record high of $10.22 million, attributed to higher regulatory fines, rising detection and escalation expenses, and slower AI governance adoption. Despite rapid AI deployment, many organizations lag in establishing oversight: about 63% have no AI governance policies, and some 87% lack AI risk mitigation processes, increasing exposure to vulnerabilities like shadow AI. Shadow AI–related breaches tend to cost more—adding roughly $200,000 per incident—and disproportionately involve compromised personally identifiable information and intellectual property. While AI is accelerating incident resolution—which for the first time dropped to an average of 241 days—the speed of adoption is creating a security oversight gap that could amplify long-term risks unless governance and audit practices catch up IBM.
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Although only 13% of organizations surveyed reported breaches involving AI models or tools, a staggering 97% of those lacked proper AI access controls—showing that even a small number of incidents can have profound consequences when governance is poor IBM Newsroom.
3
When shadow AI–related breaches occurred, they disproportionately compromised critical data: personally identifiable information in 65% of cases and intellectual property in 40%, both higher than global averages for all breaches.
4
The absence of formal AI governance policies is striking. Nearly two‑thirds (63%) of breached organizations either don’t have AI governance in place or are still developing one. Even among those with policies, many lack approval workflows or audit processes for unsanctioned AI usage—fewer than half conduct regular audits, and 61% lack governance technologies.
5
Despite advances in AI‑driven security tools that help reduce detection and containment times (now averaging 241 days, a nine‑year low), the rapid, unchecked rollout of AI technologies is creating what IBM refers to as security debt, making organizations increasingly vulnerable over time.
6
Attackers are integrating AI into their playbooks as well: 16% of breaches studied involved use of AI tools—particularly for phishing schemes and deepfake impersonations, complicating detection and remediation efforts.
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The financial toll remains steep. While the global average breach cost has dropped slightly to $4.44 million, US organizations now average a record $10.22 million per breach. In many cases, businesses reacted by raising prices—with nearly one‑third implementing hikes of 15% or more following a breach.
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IBM recommends strengthening AI governance via root practices: access control, data classification, audit and approval workflows, employee training, collaboration between security and compliance teams, and use of AI‑powered security monitoring. Investing in these practices can help organizations adopt AI safely and responsibly IBM.
🧠 My Take
This report underscores how shadow AI isn’t just a budding IT curiosity—it’s a full-blown risk factor. The allure of convenient AI tools leads to shadow adoption, and without oversight, vulnerabilities compound rapidly. The financial and operational fallout can be severe, particularly when sensitive or proprietary data is exposed. While automation and AI-powered security tools are bringing detection times down, they can’t fully compensate for the lack of foundational governance.
Organizations must treat AI not as an optional upgrade, but as a core infrastructure requiring the same rigour: visibility, policy control, audits, and education. Otherwise, they risk building a house of cards: fast growth over fragile ground. The right blend of technology and policy isn’t optional—it’s essential to prevent shadow AI from becoming a shadow crisis.
President Trump’s long‑anticipated executive 20‑page “AI Action Plan” was unveiled during his “Winning the AI Race” speech in Washington, D.C. The document outlines a wide-ranging federal push to accelerate U.S. leadership in artificial intelligence.
The plan is built around three central pillars: Infrastructure, Innovation, and Global Influence. Each pillar includes specific directives aimed at streamlining permitting, deregulating, and boosting American influence in AI globally.
Under the infrastructure pillar, the plan proposes fast‑tracking data center permitting and modernizing the U.S. electrical grid—including expanding new power sources—to meet AI’s intensive energy demands.
On innovation, it calls for removing regulatory red tape, promoting open‑weight (open‑source) AI models for broader adoption, and federal efforts to pre-empt or symbolically block state AI regulations to create uniform national policy.
The global influence component emphasizes exporting American-built AI models and chips to allies to forestall dependence on Chinese AI technologies such as DeepSeek or Qwen, positioning U.S. technology as the global standard.
A series of executive orders complemented the strategy, including one to ban “woke” or ideologically biased AI in federal procurement—requiring that models be “truthful,” neutral, and free from DEI or political content.
The plan also repealed or rescinded previous Biden-era AI regulations and dismantled the AI Safety Institute, replacing it with a pro‑innovation U.S. Center for AI Standards and Innovation focused on economic growth rather than ethical guardrails.
Workforce development received attention through new funding streams, AI literacy programs, and the creation of a Department of Labor AI Workforce Research Hub. These seek to prepare for economic disruption but are limited in scope compared to the scale of potential AI-driven change.
Observers have praised the emphasis on domestic infrastructure, streamlined permitting, and investment in open‑source models. Yet critics warn that corporate interests, especially from major tech and energy industries, may benefit most—sometimes at the expense of public safeguards and long-term viability.
⚠️ Lack of regulatory guardrails
The AI Action Plan notably lacks meaningful guardrails or regulatory frameworks. It strips back environmental permitting requirements, discourages state‑level regulation by threatening funding withdrawals, bans ideological considerations like DEI from federal AI systems, and eliminates previously established safety standards. While advocating a “try‑first” deployment mindset, the strategy overlooks critical issues ranging from bias, misinformation, copyright and data use to climate impact and energy strain. Experts argue this deregulation-heavy stance risks creating brittle, misaligned, and unsafe AI ecosystems—with little accountability or public oversight
A comparison of Trump’s AI Action Plan and the EU AI Act, focusing on guardrails, safety, security, human rights, and accountability:
1. Regulatory Guardrails
EU AI Act: Introduces a risk-based regulatory framework. High-risk AI systems (e.g., in critical infrastructure, law enforcement, and health) must comply with strict obligations before deployment. There are clear enforcement mechanisms with penalties for non-compliance.
Trump AI Plan: Focuses on deregulation and rapid deployment, removing many guardrails such as environmental and ethical oversight. It rescinds Biden-era safety mandates and discourages state-level regulation, offering minimal federal oversight or compliance mandates.
➡ Verdict: The EU prioritizes regulated innovation, while the Trump plan emphasizes unregulated speed and growth.
2. AI Safety
EU AI Act: Requires transparency, testing, documentation, and human oversight for high-risk AI systems. Emphasizes pre-market evaluation and post-market monitoring for safety assurance.
Trump AI Plan: Shutters the U.S. AI Safety Institute and replaces it with a pro-growth Center for AI Standards, focused more on competitiveness than technical safety. No mandatory safety evaluations for commercial AI systems.
➡ Verdict: The EU mandates safety as a prerequisite; the U.S. plan defers safety to industry discretion.
3. Cybersecurity and Technical Robustness
EU AI Act: Requires cybersecurity-by-design for AI systems, including resilience against manipulation or data poisoning. High-risk AI systems must ensure integrity, robustness, and resilience.
Trump AI Plan: Encourages rapid development and deployment but provides no explicit cybersecurity requirements for AI models or infrastructure beyond vague infrastructure support.
➡ Verdict: The EU embeds security controls, while the Trump plan omits structured cyber risk considerations.
4. Human Rights and Discrimination
EU AI Act: Prohibits AI systems that pose unacceptable risks to fundamental rights (e.g., social scoring, manipulative behavior). Strong safeguards for non-discrimination, privacy, and civil liberties.
Trump AI Plan: Bans AI models in federal use that promote “woke” or DEI-related content, aiming for so-called “neutrality.” Critics argue this amounts to ideological filtering, not real neutrality, and may undermine protections for marginalized groups.
➡ Verdict: The EU safeguards rights through legal obligations; the U.S. approach is politicized and lacks rights-based protections.
5. Accountability and Oversight
EU AI Act: Creates a comprehensive governance structure including a European AI Office and national supervisory authorities. Clear roles for compliance, enforcement, and redress.
Trump AI Plan: No formal accountability mechanisms for private AI developers or federal use beyond procurement preferences. Lacks redress channels for affected individuals.
➡ Verdict: EU embeds accountability through regulation; Trump’s plan leaves accountability vague and market-driven.
6. Transparency Requirements
EU AI Act: Requires AI systems (especially those interacting with humans) to disclose their AI nature. High-risk models must document datasets, performance, and design logic.
Trump AI Plan: No transparency mandates for AI models—either in federal procurement or commercial deployment.
➡ Verdict: The EU enforces transparency, while the Trump plan favors developer discretion.
7. Bias and Fairness
EU AI Act: Demands bias detection and mitigation for high-risk AI, with auditing and dataset scrutiny.
Trump AI Plan: Frames anti-bias mandates (like DEI or fairness audits) as ideological interference, and bans such requirements from federal procurement.
➡ Verdict: EU takes bias seriously as a safety issue; Trump’s plan politicizes and rejects fairness frameworks.
8. Stakeholder and Public Participation
EU AI Act: Drafted after years of consultation with stakeholders: civil society, industry, academia, and governments.
Trump AI Plan: Developed behind closed doors with little public engagement and strong industry influence, especially from tech and energy sectors.
➡ Verdict: The EU Act is consensus-based, while Trump’s plan is executive-driven.
9. Strategic Approach
EU AI Act: Balances innovation with protection, ensuring AI benefits society while minimizing harm.
Trump AI Plan: Views AI as an economic and geopolitical race, prioritizing speed, scale, and market dominance over systemic safeguards.
⚠️ Conclusion: Lack of Guardrails in the Trump AI Plan
The Trump AI Action Plan aggressively promotes AI innovation but does so by removing guardrails rather than installing them. It lacks structured safety testing, human rights protections, bias mitigation, and cybersecurity controls. With no regulatory accountability, no national AI oversight body, and an emphasis on ideological neutrality over ethical safeguards, it risks unleashing AI systems that are fast, powerful—but potentially misaligned, unsafe, and unjust.
In contrast, the EU AI Act may slow innovation at times but ensures it unfolds within a trusted, accountable, and rights-respecting framework. U.S. as prioritizing rapid innovation with minimal oversight, while the EU takes a structured, rules-based approach to AI development. Calling it the “Wild Wild West” of AI governance isn’t far off — it captures the perception that in the U.S., AI developers operate with few legal constraints, limited government oversight, and an emphasis on market freedom rather than public safeguards.
A Nation of Laws or a Race Without Rules?
America has long stood as a beacon of democratic governance, built on the foundation of laws, accountability, and institutional checks. But in the race to dominate artificial intelligence, that tradition appears to be slipping. The Trump AI Action Plan prioritizes speed over safety, deregulation over oversight, and ideology over ethical alignment.
In stark contrast, the EU AI Act reflects a commitment to structured, rights-based governance — even if it means moving slower. This emerging divide raises a critical question: Is the U.S. still a nation of laws when it comes to emerging technologies, or is it becoming the Wild West of AI?
If America aims to lead the world in AI—not just through dominance but by earning global trust—it may need to return to the foundational principles that once positioned it as a leader in setting international standards, rather than treating non-compliance as a mere business expense. Notably, Meta has chosen not to sign the EU’s voluntary Code of Practice for general-purpose AI (GPAI) models.
The penalties outlined in the EU AI Act do enforce compliance. The Act is equipped with substantial enforcement provisions to ensure that operators—such as AI providers, deployers, importers, and distributors—adhere to its rules. example question below, guess what is an appropriate penality for explicitly prohibited use of AI system under EU AI Act.
A technology company was found to be using an AI system for real-time remote biometric identification, which is explicitly prohibited by the AI Act. What is the appropriate penalty for this violation?
A) A formal warning without financial penalties B) An administrative fine of up to €7.5 million or 1% of the total global annual turnover in the previous financial year C) An administrative fine of up to €15 million or 3% of the total global annual turnover in the previous financial year D) An administrative fine of up to €35 million or 7% of the total global annual turnover in the previous financial year
Integrating ISO standards across business functions—particularly Governance, Risk, and Compliance (GRC)—has become not just a best practice but a necessity in the age of Artificial Intelligence (AI). As AI systems increasingly permeate operations, decision-making, and customer interactions, the need for standardized controls, accountability, and risk mitigation is more urgent than ever. ISO standards provide a globally recognized framework that ensures consistency, security, quality, and transparency in how organizations adopt and manage AI technologies.
In the GRC domain, ISO standards like ISO/IEC 27001 (information security), ISO/IEC 38500 (IT governance), ISO 31000 (risk management), and ISO/IEC 42001 (AI management systems) offer a structured approach to managing risks associated with AI. These frameworks guide organizations in aligning AI use with regulatory compliance, internal controls, and ethical use of data. For example, ISO 27001 helps in safeguarding data fed into machine learning models, while ISO 31000 aids in assessing emerging AI risks such as bias, algorithmic opacity, or unintended consequences.
The integration of ISO standards helps unify siloed departments—such as IT, legal, HR, and operations—by establishing a common language and baseline for risk and control. This cohesion is particularly crucial when AI is used across multiple departments. AI doesn’t respect organizational boundaries, and its risks ripple across all functions. Without standardized governance structures, businesses risk deploying fragmented, inconsistent, and potentially harmful AI systems.
ISO standards also support transparency and accountability in AI deployment. As regulators worldwide introduce new AI regulations—such as the EU AI Act—standards like ISO/IEC 42001 help organizations demonstrate compliance, build trust with stakeholders, and prepare for audits. This is especially important in industries like healthcare, finance, and defense, where the margin for error is small and ethical accountability is critical.
Moreover, standards-driven integration supports scalability. As AI initiatives grow from isolated pilot projects to enterprise-wide deployments, ISO frameworks help maintain quality and control at scale. ISO 9001, for instance, ensures continuous improvement in AI-supported processes, while ISO/IEC 27017 and 27018 address cloud security and data privacy—key concerns for AI systems operating in the cloud.
AI systems also introduce new third-party and supply chain risks. ISO standards such as ISO/IEC 27036 help in managing vendor security, and when integrated into GRC workflows, they ensure AI solutions procured externally adhere to the same governance rigor as internal developments. This is vital in preventing issues like AI-driven data breaches or compliance gaps due to poorly vetted partners.
Importantly, ISO integration fosters a culture of risk-aware innovation. Instead of slowing down AI adoption, standards provide guardrails that enable responsible experimentation and faster time to trust. They help organizations embed privacy, ethics, and accountability into AI from the design phase, rather than retrofitting compliance after deployment.
In conclusion, ISO standards are no longer optional checkboxes; they are strategic enablers in the age of AI. For GRC leaders, integrating these standards across business functions ensures that AI is not only powerful and efficient but also safe, transparent, and aligned with organizational values. As AI’s influence grows, ISO-based governance will distinguish mature, trusted enterprises from reckless adopters.
What does BS ISO/IEC 42001 – Artificial intelligence management system cover? BS ISO/IEC 42001:2023 specifies requirements and provides guidance for establishing, implementing, maintaining and continually improving an AI management system within the context of an organization.
ISO/IEC 42001:2023 – from establishing to maintain an AI management system.
ISO/IEC 27701 2019 Standard – Published in August of 2019, ISO 27701 is a new standard for information and data privacy. Your organization can benefit from integrating ISO 27701 with your existing security management system as doing so can help you comply with GDPR standards and improve your data security.
In today’s landscape, cyber threats are no longer a question of “if” but “when.” The financial and reputational costs of data breaches can be devastating. Traditionally, encryption has served as the frontline defense—locking data away. But tokenization offers a different—and arguably superior—approach: remove sensitive data entirely, and hackers end up breaking into an empty vault
Tokenization works much like casino chips. Instead of walking around with cash, players use chips that only hold value within the casino. If stolen, these chips are useless outside the establishment. Similarly, sensitive information (like credit card numbers) is stored in a highly secure “token vault.” The system returns a non-sensitive, randomized token to your application—a placeholder with zero intrinsic value
Once your systems are operating solely with tokens, real data never touches them. This minimizes the risk: even if your servers are compromised, attackers only obtain meaningless tokens. The sensitive data remains locked away, accessible only through secure channels to the token vault
Tokenization significantly reduces your “risk profile.” Without sensitive data in your environment, the biggest asset that cybercriminals target disappears. This process, often referred to as “data de-scoping,” eliminates your core liability—if you don’t store sensitive data, you can’t lose it
For businesses handling payment cards, tokenization simplifies compliance with PCI DSS. Most mandates apply only when real cardholder data enters your systems. By outsourcing tokenization to a certified provider, you dramatically shrink your audit scope and compliance burden, translating into cost and time savings
Unlike many masking methods, tokenization preserves the utility of data. Tokens can mirror the format of the original data—such as 16-digit numbers preserving the last four digits. This allows you to perform analytics, generate reports, and support loyalty systems without ever exposing the actual data
More than just an enhanced security layer, tokenization is a strategic data management tool. It fundamentally reduces the value of what resides in your systems, making them less enticing and more resilient. This dual benefit—heightened security and operational efficiency—forms the basis for a more robust and trustworthy enterprise
🔒 Key Benefits of Tokenization
Risk Reduction: Sensitive data is removed from core systems, minimizing exposure to breaches.
Simplified Compliance: Limits PCI DSS scope and lowers audit complexity and costs.
Operational Flexibility: Maintains usability of data for analytics and reporting.
Security by Design: Reduces attack surface—no valuable data means no incentive for theft.
🔄 Step-by-Step Example (Credit Card Payment)
Scenario: A customer enters their credit card number on an e-commerce site.
Original Data Collected: Customer enters: 4111 1111 1111 1111.
Tokenization Process Begins: The payment processor sends the card number to a tokenization service.
Token Issued: The service generates a random token, like A94F-Z83D-J1K9-X72B, and stores the actual card number securely in its token vault.
Token Returned: The merchant’s system only stores and uses the token (A94F-Z83D-J1K9-X72B)—not the real card number.
Transaction Authorization: When needed (e.g. to process a refund), the merchant sends the token to the tokenization provider, which maps it back to the original card and processes the transaction securely.
Most risk assessments fail to support real decisions. Learn how to turn risk management into a strategic advantage, not just a compliance task.
1. In many organizations, risk assessments are treated as checklist exercises—completed to meet compliance requirements, not to drive action. They often lack relevance to current business decisions and serve more as formalities than strategic tools.
2. When no real decision is being considered, a risk assessment becomes little more than paperwork. It consumes time, effort, and even credibility without providing meaningful value to the business. In such cases, risk teams risk becoming disconnected from the core priorities of the organization.
3. This disconnect is reflected in recent research. According to PwC’s 2023 Global Risk Survey, while 73% of executives agree that risk management is critical to strategic decisions, only 22% believe it is effectively influencing those decisions. Gartner’s 2023 survey also found that over half of organizations see risk functions as too siloed to support enterprise-wide decisions.
4. Even more concerning is the finding from NC State’s ERM Initiative: over 60% of risk assessments are performed without a clear decision-making context. This means that most risk work happens in a vacuum, far removed from the actual choices business leaders are making.
5. Risk management should not be a separate track from business—it should be a core driver of decision-making under uncertainty. Its value lies in making trade-offs explicit, identifying blind spots, and empowering leaders to act with clarity and confidence.
6. Before launching into a new risk register update or a 100 plus page report, organizations should ask a sharper business related question: What business decision are we trying to support with this assessment? When risk is framed this way, it becomes a strategic advantage, not an overhead cost.
7. By shifting focus from managing risks to enabling better decisions, risk management becomes a force multiplier for strategy, innovation, and resilience. It helps business leaders act not just with caution—but with confidence.
Conclusion A well-executed risk assessment helps businesses prioritize what matters, allocate resources wisely, and protect value while pursuing growth. To be effective, risk assessments must be decision-driven, timely, and integrated into business conversations. Don’t treat them as routine reports—use them as decision tools that connect uncertainty to action.
