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IBM introduces a structured approach to securing generative AI by focusing on protection at each phase of the AI lifecycle. The framework emphasizes securing three critical elements: the data consumed by AI systems, the model itself (during development/training), and the usage environment (live inference). These are supported by robust infrastructure controls and governance mechanisms to oversee fairness, bias, and drift over time.
In the data collection and handling stage, risks include centralized repositories that grant broad access to intellectual property and personally identifiable information (PII). To mitigate threats like data exfiltration or misuse, IBM recommends rigorous access controls, encryption, and continuous risk assessments tailored to specific data types.
Next, during model development and training, the framework warns about threats such as data poisoning and the insertion of malicious code. It advises implementing secure development practices—scanning for vulnerabilities, enforcing access policies, and treating the model build process with the same rigor as secure software development.
When it comes to model inference and live deployment, organizations face risks like prompt‑injection, adversarial attacks, and unauthorized usage. IBM recommends real-time monitoring, anomaly detection, usage policies, and safeguards to validate inputs and outputs in live AI environments.
Beyond securing each phase of the pipeline, the framework emphasizes the importance of securing the underlying infrastructure—infrastructure-as-a-service, compute nodes, storage systems—so that large language models and associated applications operate in hardened, compliant environments.
Crucially, IBM insists on embedding strong AI governance: policies, oversight structures, and continuous monitoring to detect bias, drift, and compliance issues. Governance should integrate with existing regulatory frameworks like the NIST AI Risk Management Framework and adapt alongside evolving regulations such as the EU AI Act.
Additionally, IBM’s broader work—including partnerships with AWS and internal tools like X‑Force Red—surfaced common gaps in security posture: many organizations prioritize innovation over security. Findings indicate that most active generative AI initiatives lack foundational controls across these five pillars: data, model, usage, infrastructure, and governance.
Opinion
IBM’s framework delivers a well-structured, holistic approach to the complex challenge of securing generative AI. By breaking security into discrete but interlinked phases — data, model, usage, infrastructure, governance — it helps organizations methodically build defenses where vulnerabilities are most likely. It’s also valuable that IBM aligns its framework with broader models such as NIST and incorporates continuous governance, which is essential in fast-moving AI environments.
That said, the real test lies in execution. Many enterprises still grapple with “shadow AI” — unsanctioned AI tools used by employees — and IBM’s own recent breach report suggests that only around 3% of organizations studied have adequate AI access controls in place, despite steep average breach costs ($670K extra from shadow AI alone). This gap between framework and reality underscores the need for cultural buy-in, investment in tooling, and staff training alongside technical controls.
All told, IBM’s Framework for Securing Generative AI is a strong starting point—especially when paired with governance, red teaming, infrastructure hardening, and awareness programs. But its impact will vary widely depending on how well organizations integrate its principles into everyday operations and security culture.
Lifecycle Risk Management Under the EU AI Act, providers of high-risk AI systems are obligated to establish a formal risk management system that spans the entire lifecycle of the AI system—from design and development to deployment and ongoing use.
Continuous Implementation This system must be established, implemented, documented, and maintained over time, ensuring that risks are continuously monitored and managed as the AI system evolves.
Risk Identification The first core step is to identify and analyze all reasonably foreseeable risks the AI system may pose. This includes threats to health, safety, and fundamental rights when used as intended.
Misuse Considerations Next, providers must assess the risks associated with misuse of the AI system—those that are not intended but are reasonably predictable in real-world contexts.
Post-Market Data Analysis The system must include regular evaluation of new risks identified through the post-market monitoring process, ensuring real-time adaptability to emerging concerns.
Targeted Risk Measures Following risk identification, providers must adopt targeted mitigation measures tailored to reduce or eliminate the risks revealed through prior assessments.
Residual Risk Management If certain risks cannot be fully eliminated, the system must ensure these residual risks are acceptable, using mitigation strategies that bring them to a tolerable level.
System Testing Requirements High-risk AI systems must undergo extensive testing to verify that the risk management measures are effective and that the system performs reliably and safely in all foreseeable scenarios.
Special Consideration for Vulnerable Groups The risk management system must account for potential impacts on vulnerable populations, particularly minors (under 18), ensuring their rights and safety are adequately protected.
Ongoing Review and Adjustment The entire risk management process should be dynamic, regularly reviewed and updated based on feedback from real-world use, incident reports, and changing societal or regulatory expectations.
🔐 Main Requirement Summary:
Providers of high-risk AI systems must implement a comprehensive, documented, and dynamic risk management system that addresses foreseeable and emerging risks throughout the AI lifecycle—ensuring safety, fundamental rights protection, and consideration for vulnerable groups.
1. The New Era of AI Governance AI is now part of everyday life—from facial recognition and recommendation engines to complex decision-making systems. As AI capabilities multiply, businesses urgently need standardized frameworks to manage associated risks responsibly. ISO 42001:2023, released at the end of 2023, offers the first global management system standard dedicated entirely to AI systems.
2. What ISO 42001 Offers The standard establishes requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS). It covers everything from ethical use and bias mitigation to transparency, accountability, and data governance across the AI lifecycle.
3. Structure and Risk-Based Approach Built around the Plan-Do-Check-Act (PDCA) methodology, ISO 42001 guides organizations through formal policies, impact assessments, and continuous improvement cycles—mirroring the structure used by established ISO standards like ISO 27001. However, it is tailored specifically for AI management needs.
4. Core Benefits of Adoption Implementing ISO 42001 helps organizations manage AI risks effectively while demonstrating responsible and transparent AI governance. Benefits include decreased bias, improved user trust, operational efficiency, and regulatory readiness—particularly relevant as AI legislation spreads globally.
5. Complementing Existing Standards ISO 42001 can integrate with other management systems such as ISO 27001 (information security) or ISO 27701 (privacy). Organizations already certified to other standards can adapt existing controls and processes to meet new AI-specific requirements, reducing implementation effort.
6. Governance Across AI Lifecycle The standard covers every stage of AI—from development and deployment to decommissioning. Key controls include leadership and policy setting, risk and impact assessments, transparency, human oversight, and ongoing monitoring of performance and fairness.
7. Certification Process Overview Certification follows the familiar ISO 17021 process: a readiness assessment, then stage 1 and stage 2 audits. Once certified, organizations remain valid for three years, with annual surveillance audits to ensure ongoing adherence to ISO 42001 clauses and controls.
8. Market Trends and Regulatory Context Interest in ISO 42001 is rising quickly in 2025, driven by global AI regulation like the EU AI Act. While certification remains voluntary, organizations adopting it gain competitive advantage and pre-empt regulatory obligations.
9. Controls Aligned to Ethical AI ISO 42001 includes 38 distinct controls grouped into control objectives addressing bias mitigation, data quality, explainability, security, and accountability. These facilitate ethical AI while aligning with both organizational and global regulatory expectations.
10. Forward-Looking Compliance Strategy Though certification may become more common in 2026 and beyond, organizations should begin early. Even without formal certification, adopting ISO 42001 practices enables stronger AI oversight, builds stakeholder trust, and sets alignment with emerging laws like the EU AI Act and evolving global norms.
Opinion: ISO 42001 establishes a much-needed framework for responsible AI management. It balances innovation with ethics, governance, and regulatory alignment—something no other AI-focused standard has fully delivered. Organizations that get ahead by building their AI governance around ISO 42001 will not only manage risk better but also earn stakeholder trust and future-proof against incoming regulations. With AI accelerating, ISO 42001 is becoming a strategic imperative—not just a nice-to-have.
Rising AI Risks Demand Structured Assessment As generative AI use spreads rapidly within organizations, informal tool adoption is creating governance blind spots. Although many have moved past initial panic, daily emergence of new AI tools continues to raise security and compliance concerns.
Discovery Is the Foundation A critical first step is discovering the AI tools being used across the organization—including those introduced outside IT’s visibility. Without automated inventory, you can’t secure or govern what you don’t know exists.
Integration Mapping Is Essential Next, map which AI tools are integrated into core business systems. Review OAuth grants, APIs and app connections to identify potential data leakage pathways. Ask: what data is shared, who approved it, and how are identities protected?
Supply‑Chain & Vendor Exposure Don’t overlook the AI used by SaaS vendors in your ecosystem. Many rely on third-party AI providers—necessitating detailed scrutiny of vendor AI supply chains, sub-processors, and third- or fourth-party data flow.
Governance Framework Alignment To structure assessments, organizations should anchor AI risk work within recognized frameworks like NIST AI RMF, ISO 42001, EU AI Act, and ISO 27001/SOC 2. This helps ensure consistency and traceability.
Security Controls & Monitoring Risk evaluation should include access controls (e.g. RBAC), data encryption, audit logs, and consistent vendor security reviews. Continuous monitoring helps detect anomalies in AI usage.
Human‑Centric Governance AI risk management isn’t just technical—it’s behavioral. Real-time nudges, policy just-in-time guidance, and education help users avoid risky behavior before it occurs. Nudge Security emphasizes user-friendly interventions.
Continuous Feedback & Iteration Governance must be dynamic. Policies, tool inventories, and risk assessments need regular updates as tools evolve, use cases change, and new regulations emerge.
Make the Case with Visibility Platforms like Nudge Security offer SaaS and AI discovery, tracking supply‑chain exposure, and enabling just‑in‑time governance nudges that guide secure user behavior without slowing innovation.
Mitigating Technical Threats Governance also requires awareness of specific AI threats—like prompt injection, adversarial manipulation, supply‑chain exploitation, or agentic‑AI misuse—all of which require both automated guardrails and red‑teaming strategies.
10 Best Questions to Ask When Evaluating an AI Vendor
What automated discovery mechanisms do you support to detect both known and unknown AI tools in use across the organization?
Can you map integrations between your AI platform and core systems or SaaS tools, including OAuth grants and third-party processors?
Do you publish an AI Bill of Materials (AIBOM) that details underlying AI models and third‑party suppliers or sub‑processors?
How do you support alignment with frameworks like NIST AI RMF, ISO 42001, or the EU AI Act during risk assessments?
What data protection measures do you implement—such as encryption, RBAC, retention controls, and audit logging?
How do you help organizations govern shadow AI usage at scale, including user Nudges or real-time policy enforcement?
Do you provide continuous monitoring and alerting for anomalous or potentially risky AI usage patterns?
What defenses do you offer against specific AI threats, such as prompt injection, model adversarial attacks, or agentic AI exploitation?
Have you been independently assessed or certified against any AI or security standards—SOC 2, ISO 27001, ISO 42001 or AI-specific audits?
How do you support vendor governance—e.g., tracking whether third- and fourth‑party SaaS providers in your ecosystem are using AI in ways that might impact our risk profile?
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.
2
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.
7
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.
8
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.
AI is enhancing both offensive and defensive cyber capabilities. Hackers use AI for automated phishing, malware generation, and evading detection. On the other side, defenders use AI for threat detection, behavioral analysis, and faster response. Standards like ISO/IEC 27001, ISO/IEC 42001, NIST AI RMF, and the EU AI Act promote secure AI development, risk-based controls, AI governance and transparency—helping to reduce the misuse of AI in cyberattacks. Regulations enforce accountability, transparency, trustworthiness especially for high-risk systems, and create a framework for safe AI innovation.
Regulations enforce accountability and support safe AI innovation in several key ways:
Defined Risk Categories: Laws like the EU AI Act classify AI systems by risk level (e.g., unacceptable, high, limited, minimal), requiring stricter controls for high-risk applications. This ensures appropriate safeguards are in place based on potential harm.
Mandatory Compliance Requirements: Standards such as ISO/IEC 42001 or NIST AI RMF help organizations implement risk management frameworks, conduct impact assessments, and maintain documentation. Regulators can audit these artifacts to ensure responsible use.
Transparency and Explainability: Many regulations require that AI systems—especially those used in sensitive areas like finance, health, or law—be explainable and auditable, which builds trust and deters misuse.
Human Oversight: Regulations often mandate human-in-the-loop or human-on-the-loop controls to prevent fully autonomous decision-making in critical scenarios, minimizing the risk of AI causing unintended harm.
Accountability for Outcomes: By assigning responsibility to providers, deployers, or users of AI systems, regulations like EU AI Act make it clear who is liable for breaches, misuse, or failures, discouraging reckless or opaque deployments.
Security and Robustness Requirements: Regulations often require AI to be tested against adversarial attacks and ensure resilience against manipulation, helping mitigate risks from malicious actors.
Innovation Sandboxes: Some regulatory frameworks allow for “sandboxes” where AI systems can be tested under regulatory supervision. This encourages innovation while managing risk.
In short, regulations don’t just restrict—they guide safe development, reduce uncertainty, and encourage trust in AI systems, which is essential for long-term innovation.
Yes, for a solid starting point in safe AI development and building trust, I recommend:
Focuses on establishing a management system specifically for AI, covering risk management, governance, and ethical considerations.
Helps organizations integrate AI safety into existing processes.
NIST AI Risk Management Framework (AI RMF)
Provides a practical, flexible approach to identifying and managing AI risks throughout the system lifecycle.
Emphasizes trustworthiness, transparency, and accountability.
EU Artificial Intelligence Act (Draft Regulation)
Sets clear legal requirements for AI systems based on risk levels.
Encourages transparency, robustness, and human oversight, especially for high-risk AI applications.
Starting with ISO/IEC 42001 or the NIST AI RMF is great for internal governance and risk management, while the EU AI Act is important if you operate in or with the European market due to its legal enforceability.
Together, these standards and regulations provide a comprehensive foundation to develop AI responsibly, foster trust with users, and enable innovation within safe boundaries.
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
EU AI Act: A Risk-Based Approach to Managing AI Compliance
1. Objective and Scope The EU AI Act aims to ensure that AI systems placed on the EU market are safe, respect fundamental rights, and encourage trustworthy innovation. It applies to both public and private actors who provide or use AI in the EU, regardless of whether they are based in the EU or not. The Act follows a risk-based approach, categorizing AI systems into four levels of risk: unacceptable, high, limited, and minimal.
2. Prohibited AI Practices Certain AI applications are completely banned because they violate fundamental rights. These include systems that manipulate human behavior, exploit vulnerabilities of specific groups, enable social scoring by governments, or use real-time remote biometric identification in public spaces (with narrow exceptions such as law enforcement).
3. High-Risk AI Systems AI systems used in critical sectors—like biometric identification, infrastructure, education, employment, access to public services, and law enforcement—are considered high-risk. These systems must undergo strict compliance procedures, including risk assessments, data governance checks, documentation, human oversight, and post-market monitoring.
4. Obligations for High-Risk AI Providers Providers of high-risk AI must implement and document a quality management system, ensure datasets are relevant and free from bias, establish transparency and traceability mechanisms, and maintain detailed technical documentation. They must also register their AI system in a publicly accessible EU database before placing it on the market.
5. Roles and Responsibilities The Act defines clear responsibilities for all actors in the AI supply chain—providers, importers, distributors, and deployers. Each has specific obligations based on their role. For instance, deployers of high-risk AI systems must ensure proper human oversight and inform individuals impacted by the system.
6. Limited and Minimal Risk AI For AI systems with limited risk (like chatbots), providers must meet transparency requirements, such as informing users that they are interacting with AI. Minimal-risk systems (e.g., spam filters or AI in video games) are largely unregulated, though developers are encouraged to voluntarily follow codes of conduct and ethical guidelines.
7. General Purpose AI Models General-purpose AI (GPAI) models, including foundation models like GPT, are subject to specific transparency obligations. Developers must provide technical documentation, summaries of training data, and usage instructions. Advanced GPAIs with systemic risks face additional requirements, including risk management and cybersecurity obligations.
8. Enforcement, Governance, and Sanctions Each Member State will designate a national supervisory authority, while the EU will establish a European AI Office to oversee coordination and enforcement. Non-compliance can result in fines of up to €35 million or 7% of annual global turnover, depending on the severity of the violation.
9. Timeline and Compliance Strategy The AI Act will come into effect in stages after formal adoption. Prohibited practices will be banned within six months; GPAI rules will apply after 12 months; and the core high-risk system obligations will become enforceable in 24 months. Businesses should begin gap assessments, build internal governance structures, and prepare for conformity assessments to ensure timely compliance.
For U.S. organizations operating in or targeting the EU market, preparation involves mapping AI use cases against the Act’s risk tiers, enhancing risk management practices, and implementing robust documentation and accountability frameworks. By aligning with the EU AI Act’s principles, U.S. firms can not only ensure compliance but also demonstrate leadership in trustworthy AI on a global scale.
A compliance readiness checklist for U.S. organizations preparing for the EU AI Act:
The Artificial Intelligence for Cybersecurity Professional (AICP) certification by EXIN focuses on equipping professionals with the skills to assess and implement AI technologies securely within cybersecurity frameworks. Here are the key benefits of obtaining this certification:
🔒 1. Specialized Knowledge in AI and Cybersecurity
Combines foundational AI concepts with cybersecurity principles.
Prepares professionals to handle AI-related risks, secure machine learning systems, and defend against AI-powered threats.
📈 2. Enhances Career Opportunities
Signals to employers that you’re prepared for emerging AI-security roles (e.g., AI Risk Officer, AI Security Consultant).
Helps you stand out in a growing field where AI intersects with InfoSec.
🧠 3. Alignment with Emerging Standards
Reflects principles from frameworks like ISO 42001, NIST AI RMF, and AICM (AI Controls Matrix).
Prepares you to support compliance and governance in AI adoption.
💼 4. Ideal for GRC and Security Professionals
Designed for cybersecurity consultants, compliance officers, risk managers, and vCISOs who are increasingly expected to assess AI use and risk.
📚 5. Vendor-Neutral and Globally Recognized
EXIN is a respected certifying body known for practical, independent training programs.
AICP is not tied to any specific vendor tools or platforms, allowing broader applicability.
🚀 6. Future-Proof Your Skills
AI is rapidly transforming cybersecurity — from threat detection to automation.
AICP helps professionals stay ahead of the curve and remain relevant as AI becomes integrated into every security program.
Here’s a comparison of AICP by EXIN vs. other key AI security certifications — focused on practical use, target audience, and framework alignment:
✅ 1. AICP (Artificial Intelligence for Cybersecurity Professional) – EXIN
Feature
Details
Focus
Practical integration of AI in cybersecurity, including threat detection, governance, and AI-driven risk.
Based On
General AI principles, cybersecurity practices, and touches on ISO, NIST, and AICM concepts.
Best For
Cybersecurity professionals, GRC consultants, vCISOs looking to expand into AI risk/security.
Strengths
Balanced overview of AI in cyber, vendor-neutral, exam-based credential, accessible without deep AI technical background.
Weaknesses
Less technical depth in machine learning-specific attacks or AI development security.
🧠 2. NIST AI RMF (Risk Management Framework) Training & Certifications
Feature
Details
Focus
Managing and mitigating risks associated with AI systems. Framework-based approach.
Based On
NIST AI Risk Management Framework (released Jan 2023).
Best For
U.S. government contractors, risk managers, policy/governance leads.
Strengths
Authoritative for U.S.-based public sector and compliance programs.
Weaknesses
Not a formal certification (yet) — most offerings are private training or awareness courses.
🔐 3. CSA AICM (AI Controls Matrix) Training
Feature
Details
Focus
Applying 243 AI-specific security and compliance controls across 18 domains.
AI is rapidly embedding itself into daily life—from smartphones and web browsers to drive‑through kiosks—with baked‑in assistants changing how we seek information. However, this shift also means AI tools are increasingly requesting extensive access to personal data under the pretext of functionality.
This mirrors a familiar pattern: just as simple flashlight or calculator apps once over‑requested permissions (like contacts or location), modern AI apps are doing the same—collecting far more than needed, often for profit.
For example, Perplexity’s AI browser “Comet” seeks sweeping Google account permissions: calendar manipulation, drafting and sending emails, downloading contacts, editing events across all calendars, and even accessing corporate directories.
Although Perplexity asserts that most of this data remains locally stored, the user is still granting the company extensive rights—rights that may be used to improve its AI models, shared among others, or retained beyond immediate usage.
This trend isn’t isolated. AI transcription tools ask for access to conversations, calendars, contacts. Meta’s AI experiments even probe private photos not yet uploaded—all under the “assistive” justification.
Signal’s president Meredith Whittaker likens this to “putting your brain in a jar”—granting agents clipboard‑level access to passwords, browsing history, credit cards, calendars, and contacts just to book a restaurant or plan an event.
The consequence: you surrender an irreversible snapshot of your private life—emails, contacts, calendars, archives—to a profit‑motivated company that may also employ people who review your private prompts. Given frequent AI errors, the benefits gained rarely justify the privacy and security costs.
Perspective: This article issues a timely and necessary warning: convenience should not override privacy. AI tools promising to “just do it for you” often come with deep data access bundled in unnoticed. Until robust regulations and privacy‑first architectures (like end‑to‑end encryption or on‑device processing) become standard, users must scrutinize permission requests carefully. AI is a powerful helper—but giving it full reign over intimate data without real safeguards is a risk many will come to regret. Choose tools that require minimal, transparent data access—and never let automation replace ownership of your personal information.
A recent Accenture survey of over 2,200 security and technology leaders reveals a worrying gap: while AI adoption accelerates, cybersecurity measures are lagging. Roughly 36% say AI is advancing faster than their defenses, and about 90% admit they lack adequate security protocols for AI-driven threats—including securing AI models, data pipelines, and cloud infrastructure. Yet many organizations continue prioritizing rapid AI deployment over updating existing security frameworks. The solution lies not in starting from scratch, but in reinforcing and adapting current cybersecurity strategies to address AI-specific risks —- This disconnect between innovation and security is a classic but dangerous oversight. Organizations must embed cybersecurity into AI initiatives from the start—by integrating controls, enhancing talent, and updating frameworks—rather than treating it as an afterthought. Embedding security as a foundational pillar, not a bolt-on, is essential to ensure we reap AI benefits without compromising digital safety.
“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.”
1. AI Adoption Rates Are Sky‑High According to F5’s mid‑2025 report based on input from 650 IT leaders and 150 AI strategists across large enterprises, a staggering 96 % of organizations are deploying AI models in some form. Yet, only 2 % qualify as ‘highly ready’ to scale AI securely throughout their operations.
2. Readiness Is Mostly Moderate or Low While the majority—77 %—fall into a “moderately ready” category, they often lack robust governance and security practices. Meanwhile, 21 % are low–readiness, executing AI in siloed or experimental contexts rather than at scale .
3. AI Usage vs. Saturation Even in moderately ready firms, AI is actively used—around 70 % already employ generative AI, and 25 % of applications on average incorporate AI. In low‑readiness firms, AI remains under‑utilized—typically in less than one‑quarter of apps.
4. Model Diversity and Risks Most organizations use a diverse mix of tools—65 % run two or more paid AI models alongside at least one open‑source variant (e.g. GPT‑4, Llama, Mistral, Gemma). However, this diversity heightens risk unless proper governance is in place.
5. Security Gaps Leave Firms Vulnerable Only 18 % of moderately ready firms have deployed an AI firewall, though 47 % plan to in a year. Continuous data labeling—a key measure for transparency and adversarial resilience—is practiced by just 24 %. Hybrid and multi-cloud environments exacerbate governance gaps and expand the attack surface.
6. Recommendations for Improvement F5’s report urges companies to: diversify models under tight governance; embed AI across workflows, analytics, and security; deploy AI‑specific protections like firewalls; and institutionalize formal data governance—including continuous labeling—to safely scale AI.
7. Strategic Alignment Is Essential Leaders are clear: AI demands more than experimentation. To truly harness AI’s potential, organizations must align strategy, operations, and risk controls. Without mature governance and cross‑cloud security alignment, AI risks becoming a liability rather than a transformative asset.
AI adoption is widespread, but deep readiness is rare
This report paints a familiar picture: AI adoption is widespread, but deep readiness is rare. While nearly all organizations are deploying AI, very few—just 2 %—are prepared to scale it securely and strategically. The gap between “AI explored” and “AI operationalized responsibly” is wide and risky.
The reliance on multiple models—particularly open‑source variants—without strong governance frameworks is especially concerning. AI firewalls and continuous data labeling, currently underutilized, should be treated as foundational controls—not optional add‑ons.
Ultimately, organizations that treat AI scaling as a strategic transformation—rather than just a technical experiment—will lead. This requires aligning technology investment, data culture, governance, and workforce skills. Firms that ignore these pillars may see short‑term gains in AI experimentation, but they’ll miss long‑term value—and may expose themselves to unnecessary risk.
Databricks AI Security Framework (DASF) and the AI Controls Matrix (AICM) from CSA can both be used effectively for AI security readiness assessments, though they serve slightly different purposes and scopes.
✅ How to Use DASF for AI Security Readiness Assessment
DASF focuses specifically on securing AI and ML systems throughout the model lifecycle. It’s particularly suited for technical assessments in data and model-centric environments like Databricks, but can be adapted elsewhere.
Key steps:
Map Your AI Lifecycle: Identify where your models are in the lifecycle—data ingestion, training, evaluation, deployment, monitoring.
Assess Security Controls by Domain: DASF has categories like:
Data protection
Model integrity
Access controls
Incident response
Score Maturity: Rate each domain (e.g., 0–5 scale) based on current security implementations.
Gap Analysis: Highlight where controls are absent or underdeveloped.
Prioritize Remediation: Use risk impact (data sensitivity, exposure risk) to prioritize control improvements.
✅ Best for:
ML-heavy organizations
Data science and engineering teams
Deep-dive technical control validation
✅ How to Use AICM (AI Controls Matrix by CSA)
AICM is a comprehensive, governance-first matrix with 243 control objectives across 18 domains, aligned with industry standards like ISO 42001, NIST AI RMF, and EU AI Act.
Key steps:
Map Business and Risk Context: Understand how AI is used in business processes, risk categories, and critical assets.
Select Relevant Controls: Use AICM to filter based on AI system types (foundational, open source, fine-tuned, etc.).
Perform Readiness Assessment:
Mark controls as implemented, partially implemented, or not implemented.
Evaluate across governance, privacy, data security, lifecycle management, transparency, etc.
Generate a Risk Scorecard: Assign weighted risk scores to each domain or control set.
Benchmark Against Frameworks: AICM allows alignment with ISO 42001, NIST AI RMF, etc., to help demonstrate compliance.
Use AICM for the top-down governance, risk, and control mapping, especially to align with regulatory requirements.
Use DASF for bottom-up, technical control assessments focused on securing actual AI/ML pipelines and systems.
For example:
AICM will ask “Do you have data lineage and model accountability policies?”
DASF will validate “Are you logging model inputs/outputs and tracking versions with access controls in place?”
🧠 Final Thought
Using DASF + AICM together gives you a holistic AI security readiness assessment—governance at the top, technical controls at the ground level. This combination is particularly powerful for AI risk audits, compliance readiness, or building an AI security roadmap.
⚙️ Service Name
AI Security Readiness Assessment (ASRA) (Powered by CSA AICM + Databricks DASF)
📋 Scope of Work
Phase 1 – Discovery & Scoping
Business use cases of AI
Model types and deployment workflows
Regulatory obligations (e.g., ISO 42001, NIST AI RMF, EU AI Act)
Phase 2 – AICM-Based Governance Readiness
18 domains / 243 controls (filtered by your AI system type)
Governance, accountability, transparency, bias, privacy, etc.
Scorecard: Implemented / Partial / Not Implemented
Regulatory alignment
Phase 3 – DASF-Based Technical Security Review
AI/ML pipeline review (data ingestion → model monitoring)
“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.”
The AICM (AI Controls Matrix) is a cybersecurity and risk management framework developed by the Cloud Security Alliance (CSA) to help organizations manage AI-specific risks across the AI lifecycle.
AICM stands for AI Controls Matrix, and it is:
A risk and control framework tailored for Artificial Intelligence (AI) systems.
Built to address trustworthiness, safety, and compliance in the design, development, and deployment of AI.
Structured across 18 security domains with 243 control objectives.
Aligned with existing standards like:
ISO/IEC 42001 (AI Management Systems)
ISO/IEC 27001
NIST AI Risk Management Framework
BSI AIC4
EU AI Act
+———————————————————————————+ | ARTIFICIAL INTELLIGENCE CONTROL MATRIX (AICM) | | 243 Control Objectives | 18 Security Domains | +———————————————————————————+
Domain No.
Domain Name
Example Controls Count
1
Governance & Leadership
15
2
Risk Management
14
3
Compliance & Legal
13
4
AI Ethics & Responsible AI
18
5
Data Governance
16
6
Model Lifecycle Management
17
7
Privacy & Data Protection
15
8
Security Architecture
13
9
Secure Development Practices
15
10
Threat Detection & Response
12
11
Monitoring & Logging
12
12
Access Control
14
13
Supply Chain Security
13
14
Business Continuity & Resilience
12
15
Human Factors & Awareness
14
16
Incident Management
14
17
Performance & Explainability
13
18
Third-Party Risk Management
13
+———————————————————————————+
TOTAL CONTROL OBJECTIVES: 243
+———————————————————————————+
Legend: 📘 = Policy Control 🔧 = Technical Control 🧠 = Human/Process Control 🛡️ = Risk/Compliance Control
🧩 Key Features
Covers traditional cybersecurity and AI-specific threats (e.g., model poisoning, data leakage, prompt injection).
Applies across the entire AI lifecycle—from data ingestion and training to deployment and monitoring.
Includes a companion tool: the AI-CAIQ (Consensus Assessment Initiative Questionnaire for AI), enabling organizations to self-assess or vendor-assess against AICM controls.
🎯 Why It Matters
As AI becomes pervasive in business, compliance, and critical infrastructure, traditional frameworks (like ISO 27001 alone) are no longer enough. AICM helps organizations:
Implement responsible AI governance
Identify and mitigate AI-specific security risks
Align with upcoming global regulations (like the EU AI Act)
Demonstrate AI trustworthiness to customers, auditors, and regulators
Here are the 18 security domains covered by the AICM framework:
Audit and Assurance
Application and Interface Security
Business Continuity Management and Operational Resilience
Supply Chain Management, Transparency and Accountability
Threat & Vulnerability Management
Universal Endpoint Management
Gap Analysis Template based on AICM (Artificial Intelligence Control Matrix)
#
Domain
Control Objective
Current State (1-5)
Target State (1-5)
Gap
Responsible
Evidence/Notes
Remediation Action
Due Date
1
Governance & Leadership
AI governance structure is formally defined.
2
5
3
John D.
No documented AI policy
Draft governance charter
2025-08-01
2
Risk Management
AI risk taxonomy is established and used.
3
4
1
Priya M.
Partial mapping
Align with ISO 23894
2025-07-25
3
Privacy & Data Protection
AI models trained on PII have privacy controls.
1
5
4
Sarah W.
Privacy review not performed
Conduct DPIA
2025-08-10
4
AI Ethics & Responsible AI
AI systems are evaluated for bias and fairness.
2
5
3
Ethics Board
Informal process only
Implement AI fairness tools
2025-08-15
…
…
…
…
…
…
…
…
…
…
🔢 Scoring Scale (Current & Target State)
1 – Not Implemented
2 – Partially Implemented
3 – Implemented but Not Reviewed
4 – Implemented and Reviewed
5 – Optimized and Continuously Improved
The AICM contains 243 control objectives distributed across 18 security domains, analyzed by five critical pillars, including Control Type, Control Applicability and Ownership, Architectural Relevance, LLM Lifecycle Relevance, and Threat Category.
It maps to leading standards, including NIST AI RMF 1.0 (via AI NIST 600-1), and BSI AIC4 (included today), as well as ISO 42001 & ISO 27001 (next month).
This will be the framework for STAR for AI organizational certification program. Any AI model provider, cloud service provider or SaaS provider will want to go through this program. CSA is leaving it open as to enterprises, they believe it is going to make sense for them to consider the certification as well. The release includes the Consensus Assessment Initiative Questionnaire for AI (AI-CAIQ), so CSA encourage you to start thinking about showing your alignment with AICM soon.
CSA will also adapt our Valid-AI-ted AI-based automated scoring tool to analyze AI-CAIQ submissions
Prompt injection attacks are a rising threat in the AI landscape. They occur when malicious instructions are embedded within seemingly innocent user input. Once processed by an AI model, these instructions can trigger unintended and dangerous behavior—such as leaking sensitive information or generating harmful content. Traditional cybersecurity defenses like firewalls and antivirus tools are powerless against these attacks because they operate at the application level, not the content level where AI vulnerabilities lie.
A practical example is asking a chatbot to summarize an article, but the article secretly contains instructions that override the intended behavior of the AI—like requesting sensitive internal data or malicious actions. Without specific safeguards in place, many AI systems follow these hidden prompts blindly. This makes prompt injection not only technically alarming but a serious business liability.
To counter this, AI security proxies are emerging as a preferred solution. These proxies sit between the user and the AI model, inspecting both inputs and outputs for harmful instructions or data leakage. If a prompt is malicious, the proxy intercepts it before it reaches the model. If the AI response includes sensitive or inappropriate content, the proxy can block or sanitize it before delivery.
AI security proxies like Llama Guard use dedicated models trained to detect and neutralize prompt injection attempts. They offer several benefits: centralized protection for multiple AI systems, consistent policy enforcement across different models, and a unified dashboard to monitor attack attempts. This approach simplifies and strengthens AI security without retraining every model individually.
Relying solely on model fine-tuning to resist prompt injections is insufficient. Attackers constantly evolve their tactics, and retraining models after every update is both time-consuming and unreliable. Proxies provide a more agile and scalable layer of defense that aligns with the principle of defense in depth—an approach that layers multiple controls for stronger protection.
More than a technical issue, prompt injection represents a strategic business risk. AI systems that leak data or generate toxic content can trigger compliance violations, reputational harm, and financial loss. This is why prompt injection mitigation should be built into every organization’s AI risk management strategy from day one.
Opinion & Recommendation: To effectively counter prompt injection, organizations should adopt a layered defense model. Start with strong input/output filtering using AI-aware security proxies. Combine this with secure prompt design, robust access controls, and model-level fine-tuning for context awareness. Regular red-teaming exercises and continuous threat modeling should also be incorporated. Like any emerging threat, proactive governance and cross-functional collaboration will be key to building AI systems that are secure by design.
“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.”
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.
1. The Rise of AI and the Data Dilemma Artificial intelligence (AI) is revolutionizing industries, enabling faster decisions and improved productivity. However, its exponential growth is outpacing efforts to ensure data protection and security. The integration of AI into critical infrastructure and business systems introduces new vulnerabilities, particularly as vast amounts of sensitive data are used for training models.
2. AI as Both Solution and Threat AI offers great potential for threat detection and prevention, yet it also presents new risks. Threat actors are exploiting AI tools to create sophisticated cyberattacks, such as deepfakes, phishing campaigns, and automated intrusion tactics. This dual-use nature of AI complicates its adoption and regulation.
3. Data Privacy in the Age of AI AI systems often rely on massive datasets, which can include personally identifiable information (PII). Improper handling or insufficient anonymization of data poses privacy risks. Regulators and organizations are increasingly concerned with how data is collected, stored, and used within AI systems, as breaches or misuse can lead to severe legal and reputational consequences.
4. Regulatory Pressure and Gaps Governments and regulatory bodies are rushing to catch up with AI advancements. While frameworks like GDPR and the AI Act (in the EU) aim to govern AI use, there remains a lack of global standardization. The absence of unified policies leaves organizations vulnerable to compliance gaps and fragmented security postures.
5. Shadow AI and Organizational Blind Spots One emerging challenge is the rise of “shadow AI”—tools and models used without official oversight or governance. Employees may experiment with AI tools without understanding the associated risks, leading to data leaks, IP exposure, and compliance violations. This shadow usage exacerbates existing security blind spots.
6. Vulnerable Supply Chains AI systems often depend on third-party tools, open-source models, and external data sources. This complex supply chain introduces additional risks, as vulnerabilities in any component can compromise the entire system. Supply chain attacks targeting AI infrastructure are becoming more common and harder to detect.
7. Security Strategies Lag Behind AI Adoption Despite the growing risks, many organizations still treat AI security reactively rather than proactively. Traditional cybersecurity frameworks may not be sufficient to protect dynamic AI systems. There’s a pressing need to embed security into AI development and deployment processes, including model integrity checks and data governance protocols.
8. Building Trust in AI Requires Transparency and Collaboration To address these challenges, organizations must foster transparency, cross-functional collaboration, and continuous monitoring of AI systems. It’s essential to align AI innovation with ethical practices, robust governance, and security-by-design principles. Trustworthy AI must be both functional and safe.
Opinion: The article accurately highlights a growing paradox in the AI space—innovation is moving at breakneck speed, while security and governance lag dangerously behind. In my view, this imbalance could undermine public trust in AI if not corrected swiftly. Organizations must treat AI as a high-stakes asset, not just a tool. Proactively securing data pipelines, monitoring AI behaviors, and setting strict access controls are no longer optional—they are essential pillars of responsible innovation. Investing in data governance and AI security now is the only way to ensure its benefits outweigh the risks.
“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.”
Introduction to Model Abstraction Leading AI teams are moving beyond fine-tuning and instead are abstracting their models behind well-designed APIs. This architectural approach shifts the focus from model mechanics to delivering reliable, user-oriented outcomes at scale.
Why Users Don’t Need Models End users and internal stakeholders aren’t interested in the complexities of LLMs; they want consistent, dependable results. Model abstraction isolates internal variability and ensures APIs deliver predictable functionality.
Simplifying Integration via APIs By converting complex LLMs into standardized API endpoints, engineers free teams from model management. Developers can build AI-driven tools without worrying about infrastructure or continual model updates.
Intelligent Task Routing Enterprises are deploying intelligent routing systems that send tasks to optimal models—open-source, proprietary, or custom—based on need. This orchestration maximizes both performance and cost-effectiveness.
Governance, Monitoring, and Cost Control API-based architectures enable central oversight of AI usage. Teams can enforce policies, track usage, and apply cost controls across every request—something much harder with ad hoc LLM deployments.
Scalable, Multi‑Model Resilience With abstraction layers, systems can gracefully degrade or shift models without breaking integrators. This flexible pattern supports redundancy, rollout strategies, and continuous improvement across multiple AI engines.
Foundations for Internal AI Tools These API layers make it easy to build internal developer portals and GPT-style copilots. They also underpin real‑time decisioning systems—providing business value via low-latency, scalable automation.
The Future: AI as Infrastructure This architectural shift represents a new frontier in enterprise AI infrastructure—AI delivered as dependable, governed service layers. Instead of customizing models per task, teams build modular intelligence platforms that power diverse use cases.
Conclusion Pulling models behind APIs lets organizations treat AI as composable infrastructure—abstracting away technical complexity while maintaining flexibility, control, and scale. This approach is reshaping how enterprises deploy and govern AI at scale.
“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.”
The global data governance market is on a strong upward trajectory and is expected to reach $9.62 billion by 2030. This growth is fueled by an evolving business landscape where data is at the heart of decision-making and operations. As organizations recognize the strategic value of data, governance has shifted from a technical afterthought to a business-critical priority.
The demand surge is largely attributed to increased regulatory pressure, including global mandates like ISO 27001, ISO 42001, ISO 27701, GDPR and CCPA, which require organizations to manage personal data responsibly. Simultaneously, companies face mounting obligations to demonstrate compliance and accountability in their data handling practices.
The exponential growth in data volumes, driven by digital transformation, IoT, and cloud adoption, has added complexity to data environments. Enterprises now require sophisticated frameworks to ensure data accuracy, accessibility, and security throughout its lifecycle.
Highly regulated sectors such as finance, insurance, and healthcare are leading the charge in governance investments. For these industries, maintaining data integrity is not just about compliance—it’s also about building trust with customers and avoiding operational and reputational risks.
Looking back, the data governance market was valued at just $1.3 billion in 2015. Over the past decade, cyber threats, cloud adoption, and the evolving regulatory climate have dramatically reshaped how organizations view data control, privacy, and stewardship.
Governance is no longer a luxury—it’s an operational necessity. Businesses striving to scale and innovate recognize that a lack of governance leads to data silos, inconsistent reporting, and increased exposure to risk. As a result, many are embedding governance policies into their digital strategy and enterprise architecture.
The focus on data governance is expected to intensify over the next five years. Emerging trends such as AI governance, real-time data lineage, and automation in compliance management will shape the next generation of tools and frameworks. As organizations increasingly adopt data mesh and decentralized architectures, governance solutions will need to be more agile, scalable, and intelligent to meet modern demands.
Data Governance Market Progression (Next 5 Years):
The next five years will see data governance evolve into a more intelligent, automated, and embedded function within digital enterprises. Expect the market to expand across small and mid-sized businesses, not just large enterprises, driven by affordable SaaS solutions and frameworks tailored to industry-specific needs. Additionally, AI and machine learning will become central to governance platforms, enabling predictive policy enforcement, automated classification, and real-time anomaly detection. With the increasing use of generative AI, data lineage and auditability will gain prominence. Overall, governance will move from being reactive to proactive, adaptive, and risk-focused, aligning closely with broader ESG (Environmental, Social, and Governance factors) and data ethics initiatives.
📘 Data Governance Guidelines Outline
1. Define Objectives and Scope
Align governance with business goals (e.g., compliance, quality, security).
Identify which data domains and systems are in scope.
In the race to leverage artificial intelligence (AI), organizations are rushing to train, deploy, and scale AI systems—but often without fully addressing a critical piece of the puzzle: AI data security. The recent guidance from the Cybersecurity and Infrastructure Security Agency (CISA) and Cybersecurity Strategic Initiative (CSI) offers a timely blueprint for protecting AI-related data across its lifecycle.
Why AI Security Starts with Data
AI models are only as trustworthy as the data they are trained on. From sensitive customer information to proprietary business insights, the datasets feeding AI systems are now prime targets for attackers. That’s why the CSI emphasizes securing this data not just at rest or in transit, but throughout its entire lifecycle—from ingestion and training to inference and long-term storage.
A Lifecycle Approach to Risk
Traditional cybersecurity approaches aren’t enough. The AI lifecycle introduces new risks at every stage—like data poisoning during training or model inversion attacks during inference. To counter this, security leaders must adopt a holistic, lifecycle-based strategy that extends existing security controls into AI environments.
Know Your Data: Visibility and Classification
Effective AI security begins with understanding what data you have and where it lives. CSI guidance urges organizations to implement robust data discovery, labeling, and classification practices. Without this foundation, it’s nearly impossible to apply appropriate controls, meet regulatory requirements, or detect misuse.
Evolving Controls: IAM, Encryption, and Monitoring
It’s not just about locking data down. Security controls must evolve to fit AI workflows. This includes applying least privilege access, enforcing strong encryption, and continuously monitoring model behavior. CSI makes it clear: your developers and data scientists need tailored IAM policies, not generic access.
Model Integrity and Data Provenance
The source and quality of your data directly impact the trustworthiness of your AI. Tracking data provenance—knowing where it came from, how it was processed, and how it’s used—is essential for both compliance and model integrity. As new AI governance frameworks like ISO/IEC 42001 and NIST AI RMF gain traction, this capability will be indispensable.
Defending Against AI-Specific Threats
AI brings new risks that conventional tools don’t fully address. Model inversion, adversarial attacks, and data leakage are becoming common. CSI recommends implementing defenses like differential privacy, watermarking, and adversarial testing to reduce exposure—especially in sectors dealing with personal or regulated data.
Aligning Security and Strategy
Ultimately, protecting AI data is more than a technical issue—it’s a strategic one. CSI emphasizes the need for cross-functional collaboration between security, compliance, legal, and AI teams. By embedding security from day one, organizations can reduce risk, build trust, and unlock the true value of AI—safely.
Ready to Apply CSI Guidance to Your AI Roadmap?
Don’t leave your AI initiatives exposed to unnecessary risk. Whether you’re training models on sensitive data or deploying AI in regulated environments, now is the time to embed security across the lifecycle.
At Deura InfoSec, we help organizations translate CSI and CISA guidance into practical, actionable steps—from risk assessments and data classification to securing training pipelines and ensuring compliance with ISO 42001 and NIST AI RMF.
👉 Let’s secure what matters most—your data, your trust, and your AI advantage.
Book a free 30-minute consultation to assess where you stand and map out a path forward: 📅 Schedule a Call | 📩 info@deurainfosec.com
“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.”
In today’s fast-evolving AI landscape, rapid innovation is accompanied by serious challenges. Organizations must grapple with ethical dilemmas, data privacy issues, and uncertain regulatory environments—all while striving to stay competitive. These complexities make it critical to approach AI development and deployment with both caution and strategy.
Despite the hurdles, AI continues to unlock major advantages. From streamlining operations to improving decision-making and generating new roles across industries, the potential is undeniable. However, realizing these benefits demands responsible and transparent management of AI technologies.
That’s where ISO/IEC 42001:2023 comes into play. This global standard introduces a structured framework for implementing Artificial Intelligence Management Systems (AIMS). It empowers organizations to approach AI development with accountability, safety, and compliance at the core.
Deura InfoSec LLC (deurainfosec.com) specializes in helping businesses align with the ISO 42001 standard. Our consulting services are designed to help organizations assess AI risks, implement strong governance structures, and comply with evolving legal and ethical requirements.
We support clients in building AI systems that are not only technically sound but also trustworthy and socially responsible. Through our tailored approach, we help you realize AI’s full potential—while minimizing its risks.
If your organization is looking to adopt AI in a secure, ethical, and future-ready way, ISO Consulting LLC is your partner. Visit Deura InfoSec to discover how our ISO 42001 consulting services can guide your AI journey.
We guide company through ISO/IEC 42001 implementation, helping them design a tailored AI Management System (AIMS) aligned with both regulatory expectations and ethical standards. Our team conduct a comprehensive risk assessment, implemented governance controls, and built processes for ongoing monitoring and accountability.
👉 Visit Deura Infosec to start your AI compliance journey.
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.”
“90% aren’t ready for AI attacks, are you?”, with remediation guidance at the end:
1. Organizations are lagging in AI‑era security A recent Accenture report warns that while AI is rapidly reshaping business operations, around 90% of organizations remain unprepared for AI‑driven cyberattacks. Alarmingly, 63% fall into what Accenture labels the “Exposed Zone”—lacking both a defined cybersecurity strategy and critical technical safeguards.
2. Threat landscape outpacing defenses AI has increased the speed, scope, and sophistication of cyber threats far beyond what current defenses can manage. Approximately 77% of companies do not practice essential data and AI security hygiene, leaving their business models, data architectures, and cloud environments dangerously exposed.
3. Cybersecurity must be integrated into AI initiatives Paolo Dal Cin of Accenture underscores that cybersecurity can no longer be an afterthought. Growing geopolitical instability and AI‑augmented attacks demand that security be designed into AI projects from the very beginning to maintain competitiveness and customer trust.
4. AI systems need governance and protection Daniel Kendzior, Accenture’s global Data & AI Security lead, stresses the importance of formalizing security policies and maintaining real‑time oversight of AI systems. This includes ensuring secure AI development, deployment, and operational readiness to stay ahead of evolving threats.
5. Cyber readiness varies sharply across regions The report reveals stark geographic differences in cybersecurity maturity. Only 14% of North American and 11% of European organizations are deemed “Reinvention Ready,” while in Latin America and the Asia‑Pacific region, over 70% remain in the “Exposed Zone,” highlighting major readiness disparities.
6. Reinvention‑Ready firms lead in resilience and trust The top 10% of organizations—the “Reinvention Ready” group—are demonstrably more effective at defending against advanced attacks. They block threats nearly 70% more successfully, cut technical debt, improve visibility, and enhance customer trust, illustrating that maturity aligns with tangible business benefits.
Implement accountability structures and frameworks tuned to AI risks, ensuring compliance and alignment with business goals.
Incorporate security into AI design
Embed protections into every stage of AI system development, from data handling to model deployment and infrastructure configuration.
Secure and monitor AI systems continuously
Regularly test AI pipelines, enforce encryption and access controls, and proactively update threat detection capabilities.
Leverage AI defensively
Use AI to streamline security workflows—automating threat hunting, anomaly detection, and rapid response.
Conduct maturity assessments by region and function
Benchmark cybersecurity posture across different regions and business units to identify and address vulnerabilities.
Commit to education and culture change
Train staff on AI‑related risks and security best practices, and shift the organizational mindset to view cybersecurity as foundational rather than optional.
By adopting these measures, companies can climb into the “Reinvention Ready Zone,” significantly reducing their risk exposure and reinforcing trust in their AI‑enabled operations.
“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.”
