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These are AI practices banned outright because they pose a clear threat to safety, rights, or democracy. Examples:
Social scoring by governments (like assigning citizens a “trust score”).
Real-time biometric identification in public spaces for mass surveillance (with narrow exceptions like serious crime).
Manipulative AI that exploits vulnerabilities (e.g., toys with voice assistants that encourage dangerous behavior in kids).
👉 If your system falls here → cannot be marketed or used in the EU.
🔹 2. High Risk
These are AI systems with significant impact on people’s rights, safety, or livelihoods. They are allowed but subject to strict compliance (risk management, testing, transparency, human oversight, etc.). Examples:
AI in recruitment (CV screening, job interview analysis).
Credit scoring or AI used for approving loans.
Medical AI (diagnosis, treatment recommendations).
AI in critical infrastructure (electricity grid management, transport safety systems).
AI in education (grading, admissions decisions).
👉 If your system is high-risk → must undergo conformity assessment and registration before use.
🔹 3. Limited Risk
These require transparency obligations, but not full compliance like high-risk systems. Examples:
Chatbots (users must know they’re talking to AI, not a human).
AI systems generating deepfakes (must disclose synthetic nature unless for law enforcement/artistic/expressive purposes).
Emotion recognition systems in non-high-risk contexts.
👉 If limited risk → inform users clearly, but lighter obligations.
🔹 4. Minimal or No Risk
The majority of AI applications fall here. They’re largely unregulated beyond general EU laws. Examples:
Spam filters.
AI-powered video games.
Recommendation systems for e-commerce or music streaming.
AI-driven email autocomplete.
👉 If minimal/no risk → free use with no extra requirements.
⚖️ Rule of Thumb for Classification:
If it manipulates or surveils → often unacceptable risk.
If it affects health, jobs, education, finance, safety, or fundamental rights → high risk.
If it interacts with humans but without major consequences → limited risk.
If it’s just convenience or productivity-related → minimal/no risk.
A decision tree you can use to classify any AI system under the EU AI Act risk framework:
🧭 EU AI Act AI System Risk Classification Decision Tree
Step 1: Check for Prohibited Practices
👉 Does the AI system do any of the following?
Social scoring of individuals by governments or large-scale ranking of citizens?
Manipulative AI that exploits vulnerable groups (e.g., children, disabled, addicted)?
Real-time biometric identification in public spaces (mass surveillance), except for narrow law enforcement use?
Subliminal manipulation that harms people?
✅ Yes → UNACCEPTABLE RISK (Prohibited, not allowed in EU). ❌ No → go to Step 2.
Step 2: Check for High-Risk Use Cases
👉 Does the AI system significantly affect people’s safety, rights, or livelihoods, such as:
The OWASP AI Maturity Assessment provides organizations with a structured way to evaluate how mature their practices are in managing the security, governance, and ethical use of AI systems. Its scope goes beyond technical safeguards, emphasizing a holistic approach that covers people, processes, and technology.
2. Core Maturity Domains
The framework divides maturity into several domains: governance, risk management, security, compliance, and operations. Each domain contains clear criteria that organizations can use to assess themselves and identify both strengths and weaknesses in their AI security posture.
3. Governance and Oversight
A strong governance foundation is highlighted as essential. This includes defining roles, responsibilities, and accountability structures for AI use, ensuring executive alignment, and embedding oversight into organizational culture. Without governance, technical controls alone are insufficient.
4. Risk Management Integration
Risk management is emphasized as an ongoing process that must be integrated into AI lifecycles. This means continuously identifying, assessing, and mitigating risks associated with data, algorithms, and models, while also accounting for evolving threats and regulatory changes.
5. Security and Technical Controls
Security forms a major part of the maturity model. It stresses the importance of secure coding, model hardening, adversarial resilience, and robust data protection. Secure development pipelines and automated monitoring of AI behavior are seen as crucial for preventing exploitation.
6. Compliance and Ethical Considerations
The assessment underscores regulatory alignment and ethical responsibilities. Organizations are expected to demonstrate compliance with applicable laws and standards while ensuring fairness, transparency, and accountability in AI outcomes. This dual lens of compliance and ethics sets the framework apart.
7. Operational Excellence
Operational maturity is measured by how well organizations integrate AI governance into day-to-day practices. This includes ongoing monitoring of deployed AI systems, clear incident response procedures for AI failures or misuse, and mechanisms for continuous improvement.
8. Maturity Levels
The framework uses levels of maturity (from ad hoc practices to fully optimized processes) to help organizations benchmark themselves. Moving up the levels involves progress from reactive, fragmented practices to proactive, standardized, and continuously improving capabilities.
9. Practical Assessment Method
The assessment is designed to be practical and repeatable. Organizations can self-assess or engage third parties to evaluate maturity against OWASP criteria. The output is a roadmap highlighting gaps, recommended improvements, and prioritized actions based on risk appetite.
10. Value for Organizations
Ultimately, the OWASP AI Maturity Assessment enables organizations to transform AI adoption from a risky endeavor into a controlled, strategic advantage. By balancing governance, security, compliance, and ethics, it gives organizations confidence in deploying AI responsibly at scale.
My Opinion
The OWASP AI Maturity Assessment stands out as a much-needed framework in today’s AI-driven world. Its strength lies in combining technical security with governance and ethics, ensuring organizations don’t just “secure AI” but also use it responsibly. The maturity levels provide clear benchmarks, making it actionable rather than purely theoretical. In my view, this framework can be a powerful tool for CISOs, compliance leaders, and AI product managers who need to align innovation with trust and accountability.
visual roadmap of the OWASP AI Maturity levels (1–5), showing the progression from ad hoc practices to fully optimized, proactive, and automated AI governance and security.
At the AI4 2025 conference in Las Vegas, Geoffrey Hinton—renowned as the “Godfather of AI” and a Nobel Prize winner—issued a powerful warning about the trajectory of artificial intelligence. Speaking to an audience of over 8,000 tech leaders, researchers, and policymakers, Hinton emphasized that while AI’s capabilities are expanding rapidly, we’re lacking the global coordination needed to manage it safely.
2. The Rise of Fragmented Intelligence
Hinton highlighted how AI is being deployed across diverse sectors—healthcare, transportation, finance, and military systems. Each application grows more autonomous, yet most are developed in isolation. This fragmented evolution, he argued, increases the risk of incompatible systems, competing goals, and unintended consequences—ranging from biased decisions to safety failures.
3. Introducing the Concept of “Mother AI”
To address this fragmentation, Hinton proposed a controversial but compelling idea: a centralized supervisory intelligence, which he dubbed “Mother AI.” This system would act as a layer of governance above all other AIs, helping to coordinate their behavior, ensure ethical standards, and maintain alignment with human values.
4. A Striking Analogy
Hinton used a vivid metaphor to describe this supervisory model: “The only example of a more intelligent being being controlled by a less intelligent one is a mother being controlled by her baby.” In this analogy, individual AIs are the children—powerful yet immature—while “Mother AI” provides the wisdom, discipline, and ethical guidance necessary to keep them in check.
5. Ethics, Oversight, and Coordination
The key role of this Mother AI, according to Hinton, would be to serve as a moral and operational compass. It would enforce consistency across various systems, prevent destructive behavior, and address the growing concern that AI systems might evolve in ways that humans cannot predict or control. Such oversight would help mitigate risks like surveillance misuse, algorithmic bias, or even accidental harm.
6. Innovation vs. Control
Despite his warnings, Hinton acknowledged AI’s immense benefits—particularly in areas like medicine, where it could revolutionize diagnostics, personalize treatments, and even cure previously untreatable diseases. His core argument wasn’t to slow progress, but to steer it—ensuring innovation is paired with global governance to avoid reckless development.
7. The Bigger Picture
Hinton’s call for a unifying AI framework is a challenge to the current laissez-faire approach in the tech industry. His concept of a “Mother AI” is less about creating a literal super-AI and more about instilling centralized accountability in a world of distributed algorithms. The broader implication: if we don’t proactively guide AI’s development, it may evolve in ways that slip beyond our control.
My Opinion
Hinton’s proposal is bold, thought-provoking, and increasingly necessary. The idea of a “Mother AI” might sound dramatic, but it reflects a deep truth: today’s AI systems are being built faster than society can regulate or understand them. While the metaphor may not translate into a practical solution immediately, it effectively underscores the urgent need for coordination, oversight, and ethical alignment. Without that, we risk building a powerful ecosystem of machines that may not share—or even recognize—our values. The future of AI isn’t just about intelligence; it’s about wisdom, and that starts with humans taking responsibility now…
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:
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.
Introduction: The Double-Edged Sword of Agentic AI
The adoption of agentic AI is accelerating, promising unprecedented automation, operational efficiency, and innovation. But without robust security controls, enterprises are venturing into a high-risk environment where traditional cybersecurity safeguards no longer apply. These risks go far beyond conventional threat models and demand new governance, oversight, and technical protections.
1. Autonomous Misbehavior and Operational Disruption
Agentic AI systems can act without human intervention, making real-time decisions in business-critical environments. Without precise alignment and defined boundaries, these systems could:
Overwrite or delete critical data
Make unauthorized purchases or trigger processes
Misconfigure environments or applications
Interact with employees or customers in unintended ways
Business Impact: This can lead to costly downtime, compliance violations, and serious reputational damage. The unpredictable nature of autonomous agents makes operational resilience planning essential.
2. Regulatory Compliance Failures
Agentic AI introduces unique compliance risks that go beyond common IT governance issues. Misconfigured or unmonitored systems can violate:
Privacy laws such as GDPR or HIPAA
Financial regulations like SOX or PCI-DSS
Emerging AI-specific laws like the EU AI Act
Business Impact: These violations can trigger heavy fines, legal disputes, and delayed AI-driven product launches due to failed audits or remediation needs.
3. Shadow AI and Unmanaged Access
The rapid growth of shadow AI—unapproved, employee-deployed AI tools—creates an invisible attack surface. Examples include:
Public LLM agents granted internal system access
Code-generating agents deploying unvetted scripts
Plugin-enabled AI tools interacting with production APIs
Business Impact: These unmanaged agents can serve as hidden backdoors, leaking sensitive data, exposing credentials, or bypassing logging and authentication controls.
4. Data Exposure Through Autonomous Agents
When agentic AI interacts with public tools or plugins without oversight, data leakage risks multiply. Common scenarios include:
AI agents sending confidential data to public LLMs
Bypassing existing DLP (Data Loss Prevention) controls
Business Impact: Unauthorized data exfiltration can result in IP theft, compliance failures, and loss of customer trust.
5. Supply Chain and Partner Vulnerabilities
Autonomous agents often interact with third-party systems, APIs, and vendors, which creates supply chain risks. A misconfigured agent could:
Propagate malware via insecure APIs
Breach partner data agreements
Introduce liability into downstream environments
Business Impact: Such incidents can erode strategic partnerships, cause contractual disputes, and damage market credibility.
Conclusion: Agentic AI Needs First-Class Security Governance
The speed of agentic AI adoption means enterprises must embed security into the AI lifecycle—not bolt it on afterward. This includes:
Governance frameworks for AI oversight
Continuous monitoring and risk assessment
Phishing-resistant authentication and access controls
Cross-functional collaboration between security, compliance, and operational teams
My Take: Agentic AI can be a powerful competitive advantage, but unmanaged, it can also act as an unpredictable insider threat. Enterprises should approach AI governance with the same seriousness as financial controls—because in many ways, the risks are even greater.
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The US National Institute of Standards and Technology (NIST) has issued its first major update to the Digital Identity Guidelines since 2017, responding to new cybersecurity challenges such as AI-enhanced phishing, deepfake fraud, and evolving identity attacks. The revision reflects how digital identity threats have grown more sophisticated and how organizations must adapt both technically and operationally to counter them.
The updated guidelines combine technical specifications and organizational recommendations to strengthen identity and access management (IAM) practices. While some elements refine existing methods, others introduce a fundamentally different approach to authentication and risk management, encouraging broader adoption of phishing-resistant and fraud-aware security measures.
A major focus is on AI-driven attack vectors. Advances in artificial intelligence have made phishing harder to detect, while deepfakes and synthetic identities challenge traditional identity verification processes. Although passwordless authentication, such as passkeys, offers a promising solution, adoption has been slowed by integration and compatibility hurdles. NIST now emphasizes stronger fraud detection, media forgery detection, and the use of FIDO-based phishing-resistant authentication.
This revision—NIST Special Publication 800-63, Revision 4—is the result of nearly four years of research, public drafts, and feedback from about 6,000 comments. It addresses identity proofing, authentication, and federation requirements, aiming to enhance security, privacy, and user experience. Importantly, it positions identity management as a shared responsibility, engaging cybersecurity, privacy, usability, program integrity, and mission operations teams in coordinated governance.
Key updates include revised risk management processes, continuous evaluation metrics, expanded fraud prevention measures, restructured identity proofing controls with clearer roles, safeguards against injection attacks and forged media, support for synced authenticators like passkeys, recognition of subscriber-controlled wallets, and updated password rules. These additions aim to balance robust protection with usability.
Overall, the revision represents a strategic shift from the previous edition, incorporating lessons from real-world breaches and advancements in identity technology. By setting a more comprehensive and collaborative framework, NIST aims to help organizations make digital interactions safer, more trustworthy, and more user-friendly while maintaining strong defenses against rapidly evolving threats.
“It is increasingly important for organizations to assess and manage digital identity security risks, such as unauthorized access due to impersonation. As organizations consult these guidelines, they should consider potential impacts to the confidentiality, integrity, and availability of information and information systems that they manage, and that their service providers and business partners manage, on behalf of the individuals and communities that they serve. Federal agencies implementing these guidelines are required to meet statutory responsibilities, including those under the Federal Information Security Modernization Act (FISMA) of 2014 [FISMA] and related NIST standards and guidelines. NIST recommends that non-federal organizations implementing these guidelines follow comparable standards (e.g., ISO/IEC 27001) to ensure the secure operation of their digital systems.”
Agentic AI—systems capable of planning, taking initiative, and pursuing goals with minimal oversight—represents a major shift from traditional, narrow AI tools. This autonomy enables powerful new capabilities but also creates unprecedented security risks. Autonomous agents can adapt in real time, set their own subgoals, and interact with complex systems in ways that are harder to predict, control, or audit.
Key challenges include unpredictable emergent behaviors, coordinated actions in multi-agent environments, and goal misalignment that leads to reward hacking or exploitation of system weaknesses. An agent that seems safe in testing may later bypass security controls, manipulate inputs, or collude with other agents to gain unauthorized access or disrupt operations. These risks are amplified by continuous operation, where small deviations can escalate into severe breaches over time.
Further, agentic systems can autonomously use tools, integrate with third-party services, and even modify their own code—blurring security boundaries. Without strict oversight, these capabilities risk leaking sensitive data, introducing unvetted dependencies, and enabling sophisticated supply chain or privilege escalation attacks. Managing these threats will require new governance, monitoring, and control strategies tailored to the autonomous and adaptive nature of agentic AI.
Agentic AI has the potential to transform industries—from software engineering and healthcare to finance and customer service. However, without robust security measures, these systems could be exploited, behave unpredictably, or trigger cascading failures across both digital and physical environments.
As their capabilities grow, security must be treated as a foundational design principle, not an afterthought—integrated into every stage of development, deployment, and ongoing oversight.
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.
Agentic AI: The Future Is Autonomous — and Risky
Agentic AI is no longer a lab experiment—it’s rapidly becoming the foundation of next-gen software, where autonomous agents reason, make decisions, and execute multi-step tasks across APIs and tools. While the economic upside is massive, so is the risk. As OWASP’s State of Agentic AI Security and Governance report highlights, these systems require a complete rethink of security, compliance, and operational control.
1. Agents Are Not Just Smarter—They’re Also Riskier
Unlike traditional AI, Agentic AI systems operate with memory, access privileges, and autonomy. This makes them vulnerable to manipulation: prompt injection, memory poisoning, and abuse of tool integrations. Left unchecked, they can expose sensitive data, trigger unauthorized actions, and bypass conventional monitoring entirely.
2. New Tech, New Threat Surface
Agentic AI introduces risks that traditional security models weren’t built for. Agents can be hijacked or coerced into harmful behavior. Insider threats grow more complex when users exploit agents to perform actions under the radar. With dynamic RAG pipelines and tool calling, a single prompt can become a powerful exploit vector.
3. Frameworks and Protocols Lag Behind
Popular open-source and SaaS frameworks like AutoGen, crewAI, and LangGraph are powerful—but most lack native security features. Protocols like A2A and MCP enable cross-agent communication, but they introduce new vulnerabilities like spoofed identities, data leakage, and action misalignment. Developers are now responsible for stitching together secure systems from pieces that were never designed with security first.
4. A New Compliance Era Has Begun
Static compliance is obsolete. Regulations like the EU AI Act, NIST AI RMF, and ISO/IEC 42001 call for real-time oversight, red-teaming, human-in-the-loop (HITL) controls, and signed audit logs. States like Texas and California are already imposing fines, audit mandates, and legal accountability for autonomous decisions.
5. Insiders Now Have Superpowers
Agents deployed inside organizations often carry privileged access. A malicious insider can abuse that access—exfiltrating data, poisoning RAG sources, or hijacking workflows—all through benign-looking prompts. Worse, most traditional monitoring tools won’t catch these abuses because the agent acts on the user’s behalf.
6. Adaptive Governance Is Now Mandatory
The report calls for adaptive governance models. Think: real-time dashboards, tiered autonomy ladders, automated policy updates, and kill switches. Governance must move at the speed of the agents themselves, embedding ethics, legal constraints, and observability into the code—not bolting them on afterward.
7. Benchmarks and Tools Are Emerging
Security benchmarking is still evolving, but tools like AgentDojo, DoomArena, and Agent-SafetyBench are laying the groundwork. They focus on adversarial robustness, intrinsic safety, and behavior under attack. Expect continuous red-teaming to become as common as pen testing.
8. Self-Governing AI Systems Are the Future
AI agents that evolve and self-learn can’t be governed manually. The report urges organizations to build systems that self-monitor, self-report, and self-correct—all while meeting emerging global standards. Static risk models, annual audits, and post-incident reviews just won’t cut it anymore.
🧠 Final Thought
Agentic AI is here—and it’s powerful, productive, and dangerous if not secured properly. OWASP’s guidance makes it clear: the future belongs to those who embrace proactive security, continuous governance, and adaptive compliance. Whether you’re a developer, CISO, or AI product owner, now is the time to act.
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.”
