InfoSec and Compliance – With 20 years of blogging experience, DISC InfoSec blog is dedicated to providing trusted insights and practical solutions for professionals and organizations navigating the evolving cybersecurity landscape. From cutting-edge threats to compliance strategies, this blog is your reliable resource for staying informed and secure. Dive into the content, connect with the community, and elevate your InfoSec expertise!
Transforming Cybersecurity & Compliance into Strategic Strength
In an era of ever-tightening regulations and ever-evolving threats, Deura InfoSec Consulting (DISC LLC) stands out by turning compliance from a checkbox into a proactive asset.
🛡️ What We Offer: Core Services at a Glance
1. vCISO Services
Access seasoned CISO-level expertise—without the cost of a full-time executive. Our vCISO services provide strategic leadership, ongoing security guidance, executive reporting, and risk management aligned with your business needs.
2. Compliance & Certification Support
Whether you’re targeting ISO 27001, ISO 27701, ISO 42001, NIST, GDPR, SOC 2, HIPAA, or PCI DSS, DISC supports your entire journey—from assessments and gap analysis to policy creation, control implementation, and audit preparation.
3. Security Risk Assessments
Identify risks across infrastructure, cloud, vendors, and business-critical systems using frameworks such as MITRE ATT&CK (via CALDERA), with actionable risk scorecards and remediation roadmaps.
4. Risk‑based Strategic Planning
We bridge the gap from your current (“as‑is”) security state to your desired (“to‑be”) maturity level. Our process includes strategic roadmapping, metrics to measure progress, and embedding business-aligned security into operations.
5. Security Awareness & Training
Equip your workforce and leadership with tailored training programs—ranging from executive briefings to role-based education—in vital areas like governance, compliance, and emerging threats.
6. Penetration Testing & Tool Oversight
Using top-tier tools like Burp Suite Pro and OWASP ZAP, DISC uncovers vulnerabilities in web applications and APIs. These assessments are accompanied by remediation guidance and optional managed detection support.
7. At DISC LLC, we help organizations harness the power of data and artificial intelligence—responsibly. OurAIMS (Artificial Intelligence Management System) & Data Governance solutions are designed to reduce risk, ensure compliance, and build trust. We implement governance frameworks that align with ISO 27001, ISO 27701, ISO 42001, GDPR, EU AI ACT, HIPAA, and CCPA, supporting both data accuracy and AI accountability. From data classification policies to ethical AI guidelines, bias monitoring, and performance audits, our approach ensures your AI and data strategies are transparent, secure, and future-ready. By integrating AI and data governance, DISC empowers you to lead with confidence in a rapidly evolving digital world.
🔍 Why DISC Works
Fixed-fee, hands‑on approach: No bloated documents, just precise and efficient delivery aligned with your needs.
Expert-led services: With 20+ years in security and compliance, DISC’s consultants guide you at every stage.
Audit-ready processes: Leverage frameworks and tools like GRC platform to streamline compliance, reduce overhead, and stay audit-ready.
Tailored to SMBs & enterprises: From startups to established firms, DISC crafts solutions scalable to your size and skillset.
🚀 Ready to Elevate Your Security?
DISC LLC is more than a service provider—it’s your long-term advisor. Whether you’re combating cyber risk or scaling your compliance posture, our services deliver predictable value and empower you to make security a strategic advantage.
Get started today with a free consultation, including a one-hour session with a vCISO, to see where your organization stands—and where it needs to go.
IBM’s latest Cost of a Data Breach Report (2025) highlights a growing and costly issue: “shadow AI”—where employees use generative AI tools without IT oversight—is significantly raising breach expenses. Around 20% of organizations reported breaches tied to shadow AI, and those incidents carried an average $670,000 premium per breach, compared to firms with minimal or no shadow AI exposure IBM+Cybersecurity Dive.
The latest IBM/Ponemon Institute report reveals that the global average cost of a data breach fell by 9% in 2025, down to $4.44 million—the first decline in five years—mainly driven by faster breach identification and containment thanks to AI and automation. However, in the United States, breach costs surged 9%, reaching a record high of $10.22 million, attributed to higher regulatory fines, rising detection and escalation expenses, and slower AI governance adoption. Despite rapid AI deployment, many organizations lag in establishing oversight: about 63% have no AI governance policies, and some 87% lack AI risk mitigation processes, increasing exposure to vulnerabilities like shadow AI. Shadow AI–related breaches tend to cost more—adding roughly $200,000 per incident—and disproportionately involve compromised personally identifiable information and intellectual property. While AI is accelerating incident resolution—which for the first time dropped to an average of 241 days—the speed of adoption is creating a security oversight gap that could amplify long-term risks unless governance and audit practices catch up IBM.
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Although only 13% of organizations surveyed reported breaches involving AI models or tools, a staggering 97% of those lacked proper AI access controls—showing that even a small number of incidents can have profound consequences when governance is poor IBM Newsroom.
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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.
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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.
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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.
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Attackers are integrating AI into their playbooks as well: 16% of breaches studied involved use of AI tools—particularly for phishing schemes and deepfake impersonations, complicating detection and remediation efforts.
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The financial toll remains steep. While the global average breach cost has dropped slightly to $4.44 million, US organizations now average a record $10.22 million per breach. In many cases, businesses reacted by raising prices—with nearly one‑third implementing hikes of 15% or more following a breach.
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IBM recommends strengthening AI governance via root practices: access control, data classification, audit and approval workflows, employee training, collaboration between security and compliance teams, and use of AI‑powered security monitoring. Investing in these practices can help organizations adopt AI safely and responsibly IBM.
🧠 My Take
This report underscores how shadow AI isn’t just a budding IT curiosity—it’s a full-blown risk factor. The allure of convenient AI tools leads to shadow adoption, and without oversight, vulnerabilities compound rapidly. The financial and operational fallout can be severe, particularly when sensitive or proprietary data is exposed. While automation and AI-powered security tools are bringing detection times down, they can’t fully compensate for the lack of foundational governance.
Organizations must treat AI not as an optional upgrade, but as a core infrastructure requiring the same rigour: visibility, policy control, audits, and education. Otherwise, they risk building a house of cards: fast growth over fragile ground. The right blend of technology and policy isn’t optional—it’s essential to prevent shadow AI from becoming a shadow crisis.
President Trump’s long‑anticipated executive 20‑page “AI Action Plan” was unveiled during his “Winning the AI Race” speech in Washington, D.C. The document outlines a wide-ranging federal push to accelerate U.S. leadership in artificial intelligence.
The plan is built around three central pillars: Infrastructure, Innovation, and Global Influence. Each pillar includes specific directives aimed at streamlining permitting, deregulating, and boosting American influence in AI globally.
Under the infrastructure pillar, the plan proposes fast‑tracking data center permitting and modernizing the U.S. electrical grid—including expanding new power sources—to meet AI’s intensive energy demands.
On innovation, it calls for removing regulatory red tape, promoting open‑weight (open‑source) AI models for broader adoption, and federal efforts to pre-empt or symbolically block state AI regulations to create uniform national policy.
The global influence component emphasizes exporting American-built AI models and chips to allies to forestall dependence on Chinese AI technologies such as DeepSeek or Qwen, positioning U.S. technology as the global standard.
A series of executive orders complemented the strategy, including one to ban “woke” or ideologically biased AI in federal procurement—requiring that models be “truthful,” neutral, and free from DEI or political content.
The plan also repealed or rescinded previous Biden-era AI regulations and dismantled the AI Safety Institute, replacing it with a pro‑innovation U.S. Center for AI Standards and Innovation focused on economic growth rather than ethical guardrails.
Workforce development received attention through new funding streams, AI literacy programs, and the creation of a Department of Labor AI Workforce Research Hub. These seek to prepare for economic disruption but are limited in scope compared to the scale of potential AI-driven change.
Observers have praised the emphasis on domestic infrastructure, streamlined permitting, and investment in open‑source models. Yet critics warn that corporate interests, especially from major tech and energy industries, may benefit most—sometimes at the expense of public safeguards and long-term viability.
⚠️ Lack of regulatory guardrails
The AI Action Plan notably lacks meaningful guardrails or regulatory frameworks. It strips back environmental permitting requirements, discourages state‑level regulation by threatening funding withdrawals, bans ideological considerations like DEI from federal AI systems, and eliminates previously established safety standards. While advocating a “try‑first” deployment mindset, the strategy overlooks critical issues ranging from bias, misinformation, copyright and data use to climate impact and energy strain. Experts argue this deregulation-heavy stance risks creating brittle, misaligned, and unsafe AI ecosystems—with little accountability or public oversight
A comparison of Trump’s AI Action Plan and the EU AI Act, focusing on guardrails, safety, security, human rights, and accountability:
1. Regulatory Guardrails
EU AI Act: Introduces a risk-based regulatory framework. High-risk AI systems (e.g., in critical infrastructure, law enforcement, and health) must comply with strict obligations before deployment. There are clear enforcement mechanisms with penalties for non-compliance.
Trump AI Plan: Focuses on deregulation and rapid deployment, removing many guardrails such as environmental and ethical oversight. It rescinds Biden-era safety mandates and discourages state-level regulation, offering minimal federal oversight or compliance mandates.
➡ Verdict: The EU prioritizes regulated innovation, while the Trump plan emphasizes unregulated speed and growth.
2. AI Safety
EU AI Act: Requires transparency, testing, documentation, and human oversight for high-risk AI systems. Emphasizes pre-market evaluation and post-market monitoring for safety assurance.
Trump AI Plan: Shutters the U.S. AI Safety Institute and replaces it with a pro-growth Center for AI Standards, focused more on competitiveness than technical safety. No mandatory safety evaluations for commercial AI systems.
➡ Verdict: The EU mandates safety as a prerequisite; the U.S. plan defers safety to industry discretion.
3. Cybersecurity and Technical Robustness
EU AI Act: Requires cybersecurity-by-design for AI systems, including resilience against manipulation or data poisoning. High-risk AI systems must ensure integrity, robustness, and resilience.
Trump AI Plan: Encourages rapid development and deployment but provides no explicit cybersecurity requirements for AI models or infrastructure beyond vague infrastructure support.
➡ Verdict: The EU embeds security controls, while the Trump plan omits structured cyber risk considerations.
4. Human Rights and Discrimination
EU AI Act: Prohibits AI systems that pose unacceptable risks to fundamental rights (e.g., social scoring, manipulative behavior). Strong safeguards for non-discrimination, privacy, and civil liberties.
Trump AI Plan: Bans AI models in federal use that promote “woke” or DEI-related content, aiming for so-called “neutrality.” Critics argue this amounts to ideological filtering, not real neutrality, and may undermine protections for marginalized groups.
➡ Verdict: The EU safeguards rights through legal obligations; the U.S. approach is politicized and lacks rights-based protections.
5. Accountability and Oversight
EU AI Act: Creates a comprehensive governance structure including a European AI Office and national supervisory authorities. Clear roles for compliance, enforcement, and redress.
Trump AI Plan: No formal accountability mechanisms for private AI developers or federal use beyond procurement preferences. Lacks redress channels for affected individuals.
➡ Verdict: EU embeds accountability through regulation; Trump’s plan leaves accountability vague and market-driven.
6. Transparency Requirements
EU AI Act: Requires AI systems (especially those interacting with humans) to disclose their AI nature. High-risk models must document datasets, performance, and design logic.
Trump AI Plan: No transparency mandates for AI models—either in federal procurement or commercial deployment.
➡ Verdict: The EU enforces transparency, while the Trump plan favors developer discretion.
7. Bias and Fairness
EU AI Act: Demands bias detection and mitigation for high-risk AI, with auditing and dataset scrutiny.
Trump AI Plan: Frames anti-bias mandates (like DEI or fairness audits) as ideological interference, and bans such requirements from federal procurement.
➡ Verdict: EU takes bias seriously as a safety issue; Trump’s plan politicizes and rejects fairness frameworks.
8. Stakeholder and Public Participation
EU AI Act: Drafted after years of consultation with stakeholders: civil society, industry, academia, and governments.
Trump AI Plan: Developed behind closed doors with little public engagement and strong industry influence, especially from tech and energy sectors.
➡ Verdict: The EU Act is consensus-based, while Trump’s plan is executive-driven.
9. Strategic Approach
EU AI Act: Balances innovation with protection, ensuring AI benefits society while minimizing harm.
Trump AI Plan: Views AI as an economic and geopolitical race, prioritizing speed, scale, and market dominance over systemic safeguards.
⚠️ Conclusion: Lack of Guardrails in the Trump AI Plan
The Trump AI Action Plan aggressively promotes AI innovation but does so by removing guardrails rather than installing them. It lacks structured safety testing, human rights protections, bias mitigation, and cybersecurity controls. With no regulatory accountability, no national AI oversight body, and an emphasis on ideological neutrality over ethical safeguards, it risks unleashing AI systems that are fast, powerful—but potentially misaligned, unsafe, and unjust.
In contrast, the EU AI Act may slow innovation at times but ensures it unfolds within a trusted, accountable, and rights-respecting framework. U.S. as prioritizing rapid innovation with minimal oversight, while the EU takes a structured, rules-based approach to AI development. Calling it the “Wild Wild West” of AI governance isn’t far off — it captures the perception that in the U.S., AI developers operate with few legal constraints, limited government oversight, and an emphasis on market freedom rather than public safeguards.
A Nation of Laws or a Race Without Rules?
America has long stood as a beacon of democratic governance, built on the foundation of laws, accountability, and institutional checks. But in the race to dominate artificial intelligence, that tradition appears to be slipping. The Trump AI Action Plan prioritizes speed over safety, deregulation over oversight, and ideology over ethical alignment.
In stark contrast, the EU AI Act reflects a commitment to structured, rights-based governance — even if it means moving slower. This emerging divide raises a critical question: Is the U.S. still a nation of laws when it comes to emerging technologies, or is it becoming the Wild West of AI?
If America aims to lead the world in AI—not just through dominance but by earning global trust—it may need to return to the foundational principles that once positioned it as a leader in setting international standards, rather than treating non-compliance as a mere business expense. Notably, Meta has chosen not to sign the EU’s voluntary Code of Practice for general-purpose AI (GPAI) models.
The penalties outlined in the EU AI Act do enforce compliance. The Act is equipped with substantial enforcement provisions to ensure that operators—such as AI providers, deployers, importers, and distributors—adhere to its rules. example question below, guess what is an appropriate penality for explicitly prohibited use of AI system under EU AI Act.
A technology company was found to be using an AI system for real-time remote biometric identification, which is explicitly prohibited by the AI Act. What is the appropriate penalty for this violation?
A) A formal warning without financial penalties B) An administrative fine of up to €7.5 million or 1% of the total global annual turnover in the previous financial year C) An administrative fine of up to €15 million or 3% of the total global annual turnover in the previous financial year D) An administrative fine of up to €35 million or 7% of the total global annual turnover in the previous financial year
Integrating ISO standards across business functions—particularly Governance, Risk, and Compliance (GRC)—has become not just a best practice but a necessity in the age of Artificial Intelligence (AI). As AI systems increasingly permeate operations, decision-making, and customer interactions, the need for standardized controls, accountability, and risk mitigation is more urgent than ever. ISO standards provide a globally recognized framework that ensures consistency, security, quality, and transparency in how organizations adopt and manage AI technologies.
In the GRC domain, ISO standards like ISO/IEC 27001 (information security), ISO/IEC 38500 (IT governance), ISO 31000 (risk management), and ISO/IEC 42001 (AI management systems) offer a structured approach to managing risks associated with AI. These frameworks guide organizations in aligning AI use with regulatory compliance, internal controls, and ethical use of data. For example, ISO 27001 helps in safeguarding data fed into machine learning models, while ISO 31000 aids in assessing emerging AI risks such as bias, algorithmic opacity, or unintended consequences.
The integration of ISO standards helps unify siloed departments—such as IT, legal, HR, and operations—by establishing a common language and baseline for risk and control. This cohesion is particularly crucial when AI is used across multiple departments. AI doesn’t respect organizational boundaries, and its risks ripple across all functions. Without standardized governance structures, businesses risk deploying fragmented, inconsistent, and potentially harmful AI systems.
ISO standards also support transparency and accountability in AI deployment. As regulators worldwide introduce new AI regulations—such as the EU AI Act—standards like ISO/IEC 42001 help organizations demonstrate compliance, build trust with stakeholders, and prepare for audits. This is especially important in industries like healthcare, finance, and defense, where the margin for error is small and ethical accountability is critical.
Moreover, standards-driven integration supports scalability. As AI initiatives grow from isolated pilot projects to enterprise-wide deployments, ISO frameworks help maintain quality and control at scale. ISO 9001, for instance, ensures continuous improvement in AI-supported processes, while ISO/IEC 27017 and 27018 address cloud security and data privacy—key concerns for AI systems operating in the cloud.
AI systems also introduce new third-party and supply chain risks. ISO standards such as ISO/IEC 27036 help in managing vendor security, and when integrated into GRC workflows, they ensure AI solutions procured externally adhere to the same governance rigor as internal developments. This is vital in preventing issues like AI-driven data breaches or compliance gaps due to poorly vetted partners.
Importantly, ISO integration fosters a culture of risk-aware innovation. Instead of slowing down AI adoption, standards provide guardrails that enable responsible experimentation and faster time to trust. They help organizations embed privacy, ethics, and accountability into AI from the design phase, rather than retrofitting compliance after deployment.
In conclusion, ISO standards are no longer optional checkboxes; they are strategic enablers in the age of AI. For GRC leaders, integrating these standards across business functions ensures that AI is not only powerful and efficient but also safe, transparent, and aligned with organizational values. As AI’s influence grows, ISO-based governance will distinguish mature, trusted enterprises from reckless adopters.
What does BS ISO/IEC 42001 – Artificial intelligence management system cover? BS ISO/IEC 42001:2023 specifies requirements and provides guidance for establishing, implementing, maintaining and continually improving an AI management system within the context of an organization.
ISO/IEC 42001:2023 – from establishing to maintain an AI management system.
ISO/IEC 27701 2019 Standard – Published in August of 2019, ISO 27701 is a new standard for information and data privacy. Your organization can benefit from integrating ISO 27701 with your existing security management system as doing so can help you comply with GDPR standards and improve your data security.
In today’s landscape, cyber threats are no longer a question of “if” but “when.” The financial and reputational costs of data breaches can be devastating. Traditionally, encryption has served as the frontline defense—locking data away. But tokenization offers a different—and arguably superior—approach: remove sensitive data entirely, and hackers end up breaking into an empty vault
Tokenization works much like casino chips. Instead of walking around with cash, players use chips that only hold value within the casino. If stolen, these chips are useless outside the establishment. Similarly, sensitive information (like credit card numbers) is stored in a highly secure “token vault.” The system returns a non-sensitive, randomized token to your application—a placeholder with zero intrinsic value
Once your systems are operating solely with tokens, real data never touches them. This minimizes the risk: even if your servers are compromised, attackers only obtain meaningless tokens. The sensitive data remains locked away, accessible only through secure channels to the token vault
Tokenization significantly reduces your “risk profile.” Without sensitive data in your environment, the biggest asset that cybercriminals target disappears. This process, often referred to as “data de-scoping,” eliminates your core liability—if you don’t store sensitive data, you can’t lose it
For businesses handling payment cards, tokenization simplifies compliance with PCI DSS. Most mandates apply only when real cardholder data enters your systems. By outsourcing tokenization to a certified provider, you dramatically shrink your audit scope and compliance burden, translating into cost and time savings
Unlike many masking methods, tokenization preserves the utility of data. Tokens can mirror the format of the original data—such as 16-digit numbers preserving the last four digits. This allows you to perform analytics, generate reports, and support loyalty systems without ever exposing the actual data
More than just an enhanced security layer, tokenization is a strategic data management tool. It fundamentally reduces the value of what resides in your systems, making them less enticing and more resilient. This dual benefit—heightened security and operational efficiency—forms the basis for a more robust and trustworthy enterprise
🔒 Key Benefits of Tokenization
Risk Reduction: Sensitive data is removed from core systems, minimizing exposure to breaches.
Simplified Compliance: Limits PCI DSS scope and lowers audit complexity and costs.
Operational Flexibility: Maintains usability of data for analytics and reporting.
Security by Design: Reduces attack surface—no valuable data means no incentive for theft.
🔄 Step-by-Step Example (Credit Card Payment)
Scenario: A customer enters their credit card number on an e-commerce site.
Original Data Collected: Customer enters: 4111 1111 1111 1111.
Tokenization Process Begins: The payment processor sends the card number to a tokenization service.
Token Issued: The service generates a random token, like A94F-Z83D-J1K9-X72B, and stores the actual card number securely in its token vault.
Token Returned: The merchant’s system only stores and uses the token (A94F-Z83D-J1K9-X72B)—not the real card number.
Transaction Authorization: When needed (e.g. to process a refund), the merchant sends the token to the tokenization provider, which maps it back to the original card and processes the transaction securely.
Most risk assessments fail to support real decisions. Learn how to turn risk management into a strategic advantage, not just a compliance task.
1. In many organizations, risk assessments are treated as checklist exercises—completed to meet compliance requirements, not to drive action. They often lack relevance to current business decisions and serve more as formalities than strategic tools.
2. When no real decision is being considered, a risk assessment becomes little more than paperwork. It consumes time, effort, and even credibility without providing meaningful value to the business. In such cases, risk teams risk becoming disconnected from the core priorities of the organization.
3. This disconnect is reflected in recent research. According to PwC’s 2023 Global Risk Survey, while 73% of executives agree that risk management is critical to strategic decisions, only 22% believe it is effectively influencing those decisions. Gartner’s 2023 survey also found that over half of organizations see risk functions as too siloed to support enterprise-wide decisions.
4. Even more concerning is the finding from NC State’s ERM Initiative: over 60% of risk assessments are performed without a clear decision-making context. This means that most risk work happens in a vacuum, far removed from the actual choices business leaders are making.
5. Risk management should not be a separate track from business—it should be a core driver of decision-making under uncertainty. Its value lies in making trade-offs explicit, identifying blind spots, and empowering leaders to act with clarity and confidence.
6. Before launching into a new risk register update or a 100 plus page report, organizations should ask a sharper business related question: What business decision are we trying to support with this assessment? When risk is framed this way, it becomes a strategic advantage, not an overhead cost.
7. By shifting focus from managing risks to enabling better decisions, risk management becomes a force multiplier for strategy, innovation, and resilience. It helps business leaders act not just with caution—but with confidence.
Conclusion A well-executed risk assessment helps businesses prioritize what matters, allocate resources wisely, and protect value while pursuing growth. To be effective, risk assessments must be decision-driven, timely, and integrated into business conversations. Don’t treat them as routine reports—use them as decision tools that connect uncertainty to action.
ASM Is Evolving Into Holistic, Proactive Defense Attack Surface Management has grown from merely tracking exposed vulnerabilities to encompassing all digital assets—cloud systems, IoT devices, internal apps, corporate premises, and supplier infrastructure. Modern ASM solutions don’t just catalog known risks; they continuously discover new assets and alert on changes in real time. This shift from reactive to proactive defense helps organizations anticipate threats before they materialize.
AI, Machine Learning & Threat Intelligence Drive Detection AI/ML is now foundational in ASM tools, capable of scanning vast data sets to find misconfigurations, blind spots, and chained vulnerabilities faster than human operators could. Integrated threat-intel feeds then enrich these findings, enabling contextual prioritization—your team can focus on what top adversaries are actively attacking.
Zero Trust & Continuous Monitoring Are Essential ASM increasingly integrates with Zero Trust principles, ensuring every device, user, or connection is verified before granting access. Combined with ongoing asset monitoring—both EASM (external) and CAASM (internal)—this provides a comprehensive visibility framework. Such alignment enables security teams to detect unexpected changes or suspicious behaviors in hybrid environments.
Third-Party, IoT/OT & Shadow Assets in Focus Attack surfaces are no longer limited to corporate servers. IoT and OT devices, along with shadow IT and third-party vendor infrastructure, are prime targets. ASM platforms now emphasize uncovering default credentials, misconfigured firmware, and regularizing access across partner ecosystems. This expanded view helps mitigate supply-chain and vendor-based risks
ASM Is a Continuous Service, Not a One-Time Scan Today’s ASM is about ongoing exposure assessment. Whether delivered in-house or via ASM-as-a-Service, the goal is to map, monitor, validate, and remediate 24/7. Context-rich alerts backed by human-friendly dashboards empower teams to tackle the most critical risks first. While tools offer automation, the human element remains vital—security teams need to connect ASM findings to business context
In short, ASM in 2025 is about persistent, intelligent, and context-aware attack surface management spanning internal environments, cloud, IoT, and third-party ecosystems. It blends AI-powered insights, Zero Trust philosophy, and continuous monitoring to detect vulnerabilities proactively and prioritize them based on real-world threat context.
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.”
At Deura InfoSec, we help small to mid-sized businesses navigate the complex world of cybersecurity and compliance—without the confusion, cost, or delays of traditional approaches. Whether you’re facing a looming audit, need to meet ISO 27001, NIST, HIPAA, or other regulatory standards, or just want to know where your risks are—we’ve got you covered.
We offer fixed-price compliance assessments, vCISO services, and easy-to-understand risk scorecards so you know exactly where you stand and what to fix—fast. No bloated reports. No endless consulting hours. Just actionable insights that move you forward.
Our proven SGRC frameworks, automated tools, and real-world expertise help you stay audit-ready, reduce business risk, and build trust with customers.
📌 ISO 27001 | ISO 42001 | SOC 2 | HIPAA | NIST | Privacy | TPRM | M&A 📌 Risk & Gap Assessments | vCISO | Internal Audit 📌 Security Roadmaps | AI & InfoSec Governance | Awareness Training
Start with our Compliance Self-Assessment and discover how secure—and compliant—you really are.
Several posts published recently discuss AI security and privacy, highlighting different perspectives and concerns. Here’s a summary of the most prominent themes and posts:
Emerging Concerns and Risks:
Growing Anxiety around AI Data Privacy: A recent survey found that a significant majority of Americans (91%) are concerned about social media platforms using their data to train AI models, with 69% aware of this practice.
AI-Powered Cyber Threats on the Rise: AI is increasingly being used to generate sophisticated phishing attacks and malware, making it harder to distinguish between legitimate and malicious content.
Gap between AI Adoption and Security Measures: Many organizations are quickly adopting AI but lag in implementing necessary security controls, creating a major vulnerability for data leaks and compliance issues.
Deepfakes and Impersonation Scams: The use of AI in creating realistic deepfakes is fueling a surge in impersonation scams, increasing privacy risks.
Opaque AI Models and Bias: The “black box” nature of some AI models makes it difficult to understand how they make decisions, raising concerns about potential bias and discrimination.
Regulatory Developments:
Increasing Regulatory Scrutiny: Governments worldwide are focusing on regulating AI, with the EU AI Act setting a risk-based framework and China implementing comprehensive regulations for generative AI.
Focus on Data Privacy and User Consent: New regulations emphasize data minimization, purpose limitation, explicit user consent for data collection and processing, and requirements for data deletion upon request.
Best Practices and Mitigation Strategies:
Robust Data Governance: Organizations must establish clear data governance frameworks, including data inventories, provenance tracking, and access controls.
Privacy by Design: Integrating privacy considerations from the initial stages of AI system development is crucial.
Utilizing Privacy-Preserving Techniques: Employing techniques like differential privacy, federated learning, and synthetic data generation can enhance data protection.
Continuous Monitoring and Threat Detection: Implementing tools for continuous monitoring, anomaly detection, and security audits helps identify and address potential threats.
Employee Training: Educating employees about AI-specific privacy risks and best practices is essential for building a security-conscious culture.
Specific Mentions:
NSA’s CSI Guidance: The National Security Agency (NSA) released joint guidance on AI data security, outlining best practices for organizations.
Stanford’s 2025 AI Index Report: This report highlighted a significant increase in AI-related privacy and security incidents, emphasizing the need for stronger governance frameworks.
DeepSeek AI App Risks: Experts raised concerns about the DeepSeek AI app, citing potential security and privacy vulnerabilities.
Based on current trends and recent articles, it’s evident that AI security and privacy are top-of-mind concerns for individuals, organizations, and governments alike. The focus is on implementing strong data governance, adopting privacy-preserving techniques, and adapting to evolving regulatory landscapes.Â
The rapid rise of AI has introduced new cyber threats, as bad actors increasingly exploit AI tools to enhance phishing, social engineering, and malware attacks. Generative AI makes it easier to craft convincing deepfakes, automate hacking tasks, and create realistic fake identities at scale. At the same time, the use of AI in security tools also raises concerns about overreliance and potential vulnerabilities in AI models themselves. As AI capabilities grow, so does the urgency for organizations to strengthen AI governance, improve employee awareness, and adapt cybersecurity strategies to meet these evolving risks.
There is a lack of comprehensive federal security and privacy regulations in the U.S., but violations of international standards often lead to substantial penalties abroadfor U.S. organizations. Penalties imposed abroad effectively become a cost of doing business for U.S. organizations.
Meta has faced dozens of fines and settlements across multiple jurisdictions, with at least a dozen significant penalties totaling tens of billions of dollars/euros cumulatively.
Artificial intelligence (AI) and large language models (LLMs) emerging as the top concern for security leaders. For the first time, AI, including tools such as LLMs, has overtaken ransomware as the most pressing issue.
“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 NIST Gap Assessment Tool is a structured resource—typically a checklist, questionnaire, or software tool—used to evaluate an organization’s current cybersecurity or risk management posture against a specific NIST framework. The goal is to identify gaps between existing practices and the standards outlined by NIST, so organizations can plan and prioritize improvements.
The NIST SP 800-171 standard is primarily used by non-federal organizations—especially contractors and subcontractors—that handle Controlled Unclassified Information (CUI) on behalf of the U.S. federal government.
Specifically, it’s used by:
Defense Contractors – working with the Department of Defense (DoD).
Contractors/Subcontractors – serving other civilian federal agencies (e.g., DOE, DHS, GSA).
Universities & Research Institutions – receiving federal research grants and handling CUI.
IT Service Providers – managing federal data in cloud, software, or managed service environments.
Manufacturers & Suppliers – in the Defense Industrial Base (DIB) who process CUI in any digital or physical format.
Why it matters:
Compliance with NIST 800-171 is required under DFARS 252.204-7012 for DoD contractors and is becoming a baseline for other federal supply chains. Organizations must implement the 110 security controls outlined in NIST 800-171 to protect the confidentiality of CUI.
✅ NIST 800-171 Compliance Checklist
1. Access Control (AC)
Limit system access to authorized users.
Separate duties of users to reduce risk.
Control remote and internal access to CUI.
Manage session timeout and lock settings.
2. Awareness & Training (AT)
Train users on security risks and responsibilities.
Provide CUI handling training.
Update training regularly.
3. Audit & Accountability (AU)
Generate audit logs for events.
Protect audit logs from modification.
Review and analyze logs regularly.
4. Configuration Management (CM)
Establish baseline configurations.
Control changes to systems.
Implement least functionality principle.
5. Identification & Authentication (IA)
Use unique IDs for users.
Enforce strong password policies.
Implement multifactor authentication.
6. Incident Response (IR)
Establish an incident response plan.
Detect, report, and track incidents.
Conduct incident response training and testing.
7. Maintenance (MA)
Perform system maintenance securely.
Control and monitor maintenance tools and activities.
8. Media Protection (MP)
Protect and label CUI on media.
Sanitize or destroy media before disposal.
Restrict media access and transfer.
9. Physical Protection (PE)
Limit physical access to systems and facilities.
Escort visitors and monitor physical areas.
Protect physical entry points.
10. Personnel Security (PS)
Screen individuals prior to system access.
Ensure CUI access is revoked upon termination.
11. Risk Assessment (RA)
Conduct regular risk assessments.
Identify and evaluate vulnerabilities.
Document risk mitigation strategies.
12. Security Assessment (CA)
Develop and maintain security plans.
Conduct periodic security assessments.
Monitor and remediate control effectiveness.
13. System & Communications Protection (SC)
Protect CUI during transmission.
Separate system components handling CUI.
Implement boundary protections (e.g., firewalls).
14. System & Information Integrity (SI)
Monitor systems for malicious code.
Apply security patches promptly.
Report and correct flaws quickly.
The NIST Gap Assessment Toolkit will cost-effectively assess your organization against the NIST SP 800-171 standard. It will help you to:
Understand the NIST SP 800-171 requirements for storing, processing, and transmitting CUI (Controlled Unclassified Information)
Quickly identify your NIST SP 800-171 compliance gaps
Plan and prioritise your NIST SP 800-171 project to ensure data handling meets U.S. DoD (Department of Defense) requirements
Many winery owners and executives—particularly those operating small to mid-sized, family-run estates—underestimate their exposure to cyber threats. Yet with the rise of direct-to-consumer channels like POS systems, wine clubs, and ecommerce platforms, these businesses now collect and store sensitive customer and employee data, including payment details, birthdates, and Social Security numbers. This makes them attractive targets for cybercriminals.
The Emerging Threat of Cyber-Physical Attacks
Wineries increasingly rely on automated production systems and IoT sensors to manage fermentation, temperature control, and chemical dosing. These digital tools can be manipulated by hackers to:
Disrupt production by altering temperature or chemical settings.
Spoil inventory through false sensor data or remote tampering.
Undermine trust by threatening product safety and quality.
A Cautionary Tale
While there are no public reports of terrorist attacks on the wine industry’s supply chain, the 1985 Austrian wine scandal is a stark reminder of what can happen when integrity is compromised. In that case, wine was adulterated with antifreeze (diethylene glycol) to manipulate taste—resulting in global recalls, destroyed reputations, and public health risks.
The lesson is clear: cyber and physical safety in the winery business are now deeply intertwined.
2. Why Vineyards and Wineries Are at Risk
High-value data: Personal and financial details stored in club databases or POS systems can be exploited and sold on the dark web.
Legacy systems & limited expertise: Many wineries rely on outdated IT infrastructure and lack in-house cybersecurity staff.
Regulatory complexity: Compliance with data privacy regulations like CCPA/CPRA adds to the burden, and gaps can lead to penalties.
Charming targets: Boutique and estate brands, which often emphasize hospitality and trust, can be unexpectedly appealing to attackers seeking vulnerable entry points.
3. Why It Matters
Reputation risk: A breach can shatter consumer trust—especially among affluent wine club customers who expect discretion and reliability.
Financial & legal exposure: Incidents may invite steep fines, ransomware costs, and lawsuits under privacy laws.
Operational disruption: Outages or ransomware can cripple point-of-sale and club systems, causing revenue loss and logistical headaches.
Competitive advantage: Secure operations can boost customer confidence, support audit and M&A readiness, and unlock better insurance or investor opportunities.
4. What You Can Do About It
Risk & compliance assessment: Discover vulnerabilities in systems, Wi‑Fi, and employee habits. Score your risk with a 10-page report for stakeholders.
Privacy compliance support: Navigate CCPA/CPRA (and PCI/GDPR as needed) to keep your winery legally sound.
Defense against phishing & ransomware: Conduct employee training, simulations, and implement defenses.
Security maturity roadmap: Prioritize improvements—like endpoint protection, firewalls, 2FA setups—and phase them according to your brand and budget.
Fractional vCISO support: Access quarterly executive consultations to align compliance and tech strategy without hiring full-time experts.
Optional services: Pen testing, PCI-DSS support, vendor reviews, and business continuity planning for deeper security.
DISC WinerySecure™ offers a tailored roadmap to safeguard your winery:
You don’t need to face this alone. We offer Free checklist + consultation.
DISC InfoSec Virtual CISO | Wine Industry Security & Compliance
Investing in a proactive security strategy isn’t just about avoiding threats—it’s about protecting your brand, securing compliance, and empowering growth. Contact DISC WinerySecure™ today for a free consultation.
With ShareVault, your sensitive data is protected by enterprise-grade security, built-in privacy controls, and industry-leading availability—so you can share critical information with confidence. Whether you’re managing M&A, compliance, or strategic partnerships, ShareVault ensures your data stays safe, your access stays private, and your operations never miss a beat.
Trust ShareVault—where security, privacy, and uptime come standard.
Top benefits of ShareVault:
Advanced Document Security ShareVault offers robust encryption, dynamic watermarking, and granular access controls to ensure that sensitive documents remain secure—whether viewed, downloaded, or shared.
Granular User Permissions Control who sees what, when, and how. ShareVault enables administrators to define user roles, set expiration dates, and restrict actions like printing or screen captures.
Real-Time Activity Monitoring Detailed audit trails and real-time analytics provide full visibility into who accessed what and when—crucial for compliance, due diligence, and risk management.
Seamless Collaboration Collaborate across teams and organizations with ease, using a user-friendly interface and support for secure Q&A, document versioning, and threaded commenting.
High Availability and Scalability ShareVault is cloud-based with 99.99% uptime, offering reliable access anytime, anywhere—ideal for fast-paced deals, global teams, and critical business operations.
ShareVault holds an ISO 27001 certification for its Security Management Program and undergoes annual third-party audits to validate its security controls, governance, and compliance. These assessments ensure continued adherence to ISO 27001, NIST 800-53r5, and 21 CFR Part 11 standards.
Sharvault Application Security
Operating Systems: A mix of open-source and proprietary server operating systems
Architecture: Multi-tenant design for data isolation
Application Server: Industry-standard Java-based application server
Database: Enterprise-grade relational database management system
Authentication: Robust security framework for user authentication and access control
Key Management: Cloud-based key management service
Data Transfer Security: Strong encryption for all data transfers
Global Performance: Content delivery network for optimized global access
Document Handling: Various tools for document processing and viewing
Search and Logging: Advanced search and logging capabilities
Email Services: Professional email delivery service
Video Security: Secure video streaming with digital rights management
Additional Database: NoSQL database for specific functionality
AI Integration: AI-powered services for document analysis and processing
Feedback: Overall ShareVault appears to have a robust and comprehensive security architecture, leveraging a range of industry-standard technologies and best practices. The use of encryption, two-factor authentication, access controls, and secure data transfer protocols demonstrates a strong commitment to data security and privacy. Additionally, the integration of AI and machine learning capabilities for tasks like redaction and OCR highlights ShareVault’s adoption of modern technologies. Overall, the application security measures described seem well-designed and appropriate for a highly secure document sharing platform.
The Open Web Application Security Project (OWASP) has released the AI Testing Guide (AITG)—a structured, technology-agnostic framework to test and secure artificial intelligence systems. Developed in response to the growing adoption of AI in sensitive and high-stakes sectors, the guide addresses emerging AI-specific threats, such as adversarial attacks, model poisoning, and prompt injection. It is led by security experts Matteo Meucci and Marco Morana and is designed to support a wide array of stakeholders, including developers, architects, data scientists, and risk managers.
The guide provides comprehensive resources across the AI lifecycle, from design to deployment. It emphasizes the need for rigorous and repeatable testing processes to ensure AI systems are secure, trustworthy, and aligned with compliance requirements. The AITG also helps teams formalize testing efforts through structured documentation, thereby enhancing audit readiness and regulatory transparency. It supports due diligence efforts that are crucial for organizations operating in heavily regulated sectors like finance, healthcare, and critical infrastructure.
A core premise of the guide is that AI testing differs significantly from conventional software testing. Traditional applications exhibit deterministic behavior, while AI systems—especially machine learning models—are probabilistic in nature. They produce varying outputs depending on input variability and data distribution. Therefore, testing must account for issues such as data drift, fairness, transparency, and robustness. The AITG stresses that evaluating model performance alone is insufficient; testers must probe how models react to both benign and malicious changes in data.
Another standout feature of the AITG is its deep focus on adversarial robustness. AI systems can be deceived through carefully engineered inputs that appear normal to humans but cause erroneous model behavior. The guide provides methodologies to assess and mitigate such risks. Additionally, it includes techniques like differential privacy to protect individual data within training sets—critical in the age of stringent data protection regulations. This holistic testing approach strengthens confidence in AI systems both internally and among external stakeholders.
The AITG also acknowledges the fluid nature of AI environments. Models can silently degrade over time due to data drift or concept shift. To address this, the guide recommends implementing continuous monitoring frameworks that detect such degradation early and trigger automated responses. It incorporates fairness assessments and bias mitigation strategies, which are particularly important in ensuring that AI systems remain equitable and inclusive over time.
Importantly, the guide equips security professionals with specialized AI-centric penetration testing tools. These include tests for membership inference (to determine if a specific record was in the training data), model extraction (to recreate or steal the model), and prompt injection (particularly relevant for LLMs). These techniques are crucial for evaluating AI’s real-world attack surface, making the AITG a practical resource not just for developers, but also for red teams and security auditors.
Feedback: The OWASP AI Testing Guide is a timely and well-structured contribution to the AI security landscape. It effectively bridges the gap between software engineering practices and the emerging realities of machine learning systems. Its technology-agnostic stance and lifecycle coverage make it broadly applicable across industries and AI maturity levels. However, the guide’s ultimate impact will depend on how well it is adopted by practitioners, particularly in fast-paced AI environments. OWASP might consider developing companion tools, templates, and case studies to accelerate practical adoption. Overall, this is a foundational step toward building secure, transparent, and accountable AI systems.
AI isn’t just another tool—it’s a paradigm shift. CISOs must now integrate AI-driven analytics into real-time threat detection and incident response. These systems analyze massive volumes of data faster and surface patterns humans might miss.
2. New vulnerabilities from AI use
Deploying AI creates unique risks: biased outputs, prompt injection, data leakage, and compliance challenges across global jurisdictions. CISOs must treat models themselves as attack surfaces, ensuring robust governance.
3. AI amplifies offensive threats
Adversaries now weaponize AI to automate reconnaissance, craft tailored phishing lures or deepfakes, generate malicious code, and launch fast-moving credential‑stuffing campaigns.
4. Building an AI‑enabled cyber team
Moving beyond tool adoption, CISOs need to develop core data capabilities: quality pipelines, labeled datasets, and AI‑savvy talent. This includes threat‑hunting teams that grasp both AI defense and AI‑driven offense.
5. Core capabilities & controls
The playbook highlights foundational strategies:
Data governance (automated discovery and metadata tagging).
Zero trust and adaptive access controls down to file-system and AI pipelines.
AI-powered XDR and automated IR workflows to reduce dwell time.
6. Continuous testing & offensive security
CISOs must adopt offensive measures—AI pen testing, red‑teaming models, adversarial input testing, and ongoing bias audits. This mirrors traditional vulnerability management, now adapted for AI-specific threats.
7. Human + machine synergy
Ultimately, AI acts as a force multiplier—not a surrogate. Humans must oversee, interpret, understand model limitations, and apply context. A successful cyber‑AI strategy relies on continuous training and board engagement .
🧩 Feedback
Comprehensive: Excellent balance of offense, defense, data governance, and human oversight.
Actionable: Strong emphasis on building capabilities—not just buying tools—is a key differentiator.
Enhance with priorities: Highlighting fast-moving threats like prompt‑injection or autonomous AI agents could sharpen urgency.
Communications matter: Reminding CISOs to engage leadership with justifiable ROI and scenario planning ensures support and budget.
AI transforms the cybersecurity role—especially for CISOs—in several fundamental ways:
1. From Reactive to Predictive
Traditionally, security teams react to alerts and known threats. AI shifts this model by enabling predictive analytics. AI can detect anomalies, forecast potential attacks, and recommend actions before damage is done.
2. Augmented Decision-Making
AI enhances the CISO’s ability to make high-stakes decisions under pressure. With tools that summarize incidents, prioritize risks, and assess business impact, CISOs move from gut instinct to data-informed leadership.
3. Automation of Repetitive Tasks
AI automates tasks like log analysis, malware triage, alert correlation, and even generating incident reports. This allows security teams to focus on strategic, higher-value work, such as threat modeling or security architecture.
4. Expansion of Threat Surface Oversight
With AI deployed in business functions (e.g., chatbots, LLMs, automation platforms), the CISO must now secure AI models and pipelines themselves—treating them as critical assets subject to attack and misuse.
5. Offensive AI Readiness
Adversaries are using AI too—to craft phishing campaigns, generate polymorphic malware, or automate social engineering. The CISO’s role expands to understanding offensive AI tactics and defending against them in real time.
6. AI Governance Leadership
CISOs are being pulled into AI governance: setting policies around responsible AI use, bias detection, explainability, and model auditing. Security leadership now intersects with ethical AI oversight and compliance.
7. Cross-Functional Influence
Because AI touches every function—HR, legal, marketing, product—the CISO must collaborate across departments, ensuring security is baked into AI initiatives from the ground up.
Summary: AI transforms the CISO from a control enforcer into a strategic enabler who drives predictive defense, leads governance, secures machine intelligence, and shapes enterprise-wide digital resilience. It’s a shift from gatekeeping to guiding responsible, secure innovation.
Mapping against ISO 42001:2023 and/or the EU Artificial Intelligence (AI) Act
The AI Act & ISO 42001 Gap Analysis Tool is a dual-purpose resource that helps organizations assess their current AI practices against both legal obligations under the EU AI Act and international standards like ISO/IEC 42001:2023. It allows users to perform a tailored gap analysis based on their specific needs, whether aligning with ISO 42001, the EU AI Act, or both. The tool facilitates early-stage project planning by identifying compliance gaps and setting actionable priorities.
With the EU AI Act now in force and enforcement of its prohibitions on high-risk AI systems beginning in February 2025, organizations face growing pressure to proactively manage AI risk. Implementing an AI management system (AIMS) aligned with ISO 42001 can reduce compliance risk and meet rising international expectations. As AI becomes more embedded in business operations, conducting a gap analysis has become essential for shaping a sound, legally compliant, and responsible AI strategy.
Feedback: This tool addresses a timely and critical need in the AI governance landscape. By combining legal and best-practice assessments into one streamlined solution, it helps reduce complexity for compliance teams. Highlighting the upcoming enforcement deadlines and the benefits of ISO 42001 certification reinforces urgency and practicality.
The AI Act & ISO 42001 Gap Analysis Tool is a user-friendly solution that helps organizations quickly and effectively assess their current AI practices against both the EU AI Act and the ISO/IEC 42001:2023 standard. With intuitive features, customizable inputs, and step-by-step guidance, the tool adapts to your organization’s specific needs—whether you’re looking to meet regulatory obligations, align with international best practices, or both. Its streamlined interface allows even non-technical users to conduct a thorough gap analysis with minimal training.
Designed to integrate seamlessly into your project planning process, the tool delivers clear, actionable insights into compliance gaps and priority areas. As enforcement of the EU AI Act begins in early 2025, and with increasing global focus on AI governance, this tool provides not only legal clarity but also practical, accessible support for developing a robust AI management system. By simplifying the complexity of AI compliance, it empowers teams to make informed, strategic decisions faster.
What does the tool provide?
Split into two sections, EU AI Act and ISO 42001, so you can perform analyses for both or an individual analysis.
The EU AI Act section is divided into six sets of questions: general requirements, entity requirements, assessment and registration, general-purpose AI, measures to support innovation and post-market monitoring.
Identify which requirements and sections of the AI Act are applicable by completing the provided screening questions. The tool will automatically remove any non-applicable questions.
The ISO 42001 section is divided into two sets of questions: ISO 42001 six clauses and ISO 42001 controls as outlined in Annex A.
Executive summary pages for both analyses, including by section or clause/control, the number of requirements met and compliance percentage totals.
A clear indication of strong and weak areas through colour-coded analysis graphs and tables to highlight key areas of development and set project priorities.
The tool is designed to work in any Microsoft environment; it does not need to be installed like software, and does not depend on complex databases. It is reliant on human involvement.
Items that can support an ISO 42001 (AIMS) implementation project
Overview: DISC WinerySecure™ is a tailored cybersecurity and compliance service for small and mid-sized wineries. These businesses are increasingly reliant on digital systems (POS, ecommerce, wine clubs), yet often lack dedicated security staff. Our solution is cost-effective, easy to adopt, and customized to the wine industry.
Wineries may not seem like obvious cyber targets, but they hold valuable data—customer and employee details like social security numbers, payment info, and birthdates—that cybercriminals can exploit for identity theft and sell on the dark web. Even business financials are at risk.
Target Clients:
We care for the planet and your data
Wineries invest in luxury branding
Wineries considering mergers and acquisitions.
Wineries with 50–1000 employees
Using POS, wine club software, ecommerce, or logistics systems
Limited or no in-house IT/security expertise
🍷 Cyber & Compliance Protection for Wineries
Helping Napa & Sonoma Wineries Stay Secure, Compliant, and Trusted
🛡️ Why Wineries Are at Risk
Wineries today handle more sensitive data than ever—credit cards, wine club memberships, ecommerce sales, shipping details, and supplier records. Yet many rely on legacy systems, lack dedicated IT teams, and operate in a complex regulatory environment.
Cybercriminals know this. Wineries have become easy, high-value targets.
✅ Our Services
We offer fractional vCISO and compliance consulting tailored for small and mid-sized wineries:
🔒 Cybersecurity Risk Assessment – Discover hidden vulnerabilities in your systems, Wi-Fi, and employee habits.
📜 CCPA/CPRA Privacy Compliance – Ensure you’re protecting your customers’ personal data the California way.
🧪 Phishing & Ransomware Defense – Train your team to spot threats and test your defenses before attackers do.
🧰 Security Maturity Roadmap – Practical, phased improvements aligned with your business goals and brand.
🧾 Simple Risk Scorecard – A 10-page report you can share with investors, insurers, or partners.
🎯 Who This Is For
Family-run or boutique wineries with direct-to-consumer operations
Wineries investing in digital growth, but unsure how secure it is
Teams managing POS, ecommerce, club CRMs, M&A and vendor integrations
💡 Why It Matters
🏷️ Protect your brand reputation—especially with affluent wine club customers
💸 Avoid fines and lawsuits from privacy violations or breaches
🛍️ Boost customer confidence—safety sells
📉 Reduce downtime, ransomware risk, and compliance headaches
📞 Let’s Talk
Get a free 30-minute consultation or try our $49 Self-Assessment + 10-Page Risk Scorecard to see where you stand.
1. Strategic importance of discretion When two major companies are negotiating a merger or acquisition, even a minor leak can damage stock prices, derail the process, or collapse the deal entirely. A confidential environment is essential to preserve each party’s strategic advantage during secretive stages of the negotiation.
2. Maintaining competitive secrecy By keeping a forthcoming deal under wraps, a company can gain from stealthy operations—honing tactics and announcements without alerting rivals or disrupting the market prematurely.
3. Protecting sensitive materials during due diligence The due diligence stage demands access to proprietary analytics, trade secrets, and financial documents. A properly secured virtual data room (VDR) ensures these materials can be reviewed without risking unwanted exposure.
4. Internal stability amid uncertainty Beyond market reactions, confidentiality helps stabilize employee morale. Rumors of acquisitions can breed anxiety among staff; controlled disclosure helps maintain calm until formal announcements are made .
5. Why virtual is preferred over physical rooms Compared to traditional physical data rooms or email-based exchanges, VDRs offer encrypted, centralized, and remotely accessible document storage. They support multiple users across time zones and locales, making them far more efficient and secure
6. Advanced organization and control tools Modern VDRs include features like hierarchical tagging (as in ShareVault’s platform), robust document indexing, full-text search, and flexible file rights. Admins can finely tune access—for instance, disabling copying, printing, or even screenshots—and apply watermarks with expiration settings .
7. Enhanced transparency, auditability, and efficiency These platforms offer complete audit trails, Q&A sections, real-time alerts, and analytics. Participants can track activity, identify engagement patterns, and streamline due diligence, speeding up deal completion and improving oversight
Virtual Data Rooms (VDRs) are essential tools in mergers and acquisitions, providing a secure platform for sharing confidential documents during due diligence. They enable controlled access to sensitive information, supporting informed decision-making and effective risk management. In today’s digital landscape, where information is a critical asset, VDRs enhance corporate governance by promoting transparency, accountability, and compliance. As businesses face increasing regulatory and operational demands, adopting VDRs is not just a smart choice but a strategic necessity for maintaining strong governance and operational integrity.
Virtual data rooms are indispensable in confidential M&A contexts. They effectively combine security, efficiency, and collaboration in ways that physical or email-based systems simply cannot. The advanced features—granular permissions, audit logs, analytics, and query tools—are not just conveniences; they’re game-changers that help drive deals forward more smoothly and securely.
To truly elevate the experience, VDR providers Sharevault prioritize user-friendly interfaces—think intuitive document sorting, drag & drop, clear timestamps—and strike a better balance between robust security measures and seamless usability. When technical strength aligns with an intuitive user experience, virtual data rooms fulfill their potential, making complex, high-stakes M&A processes feel nearly effortless.
Information Security & Privacy aspect of the M&A process, especially focusing on how confidentiality, integrity, and controlled access are preserved throughout.
1. Confidentiality of Deal Intentions and Parties Involved
In early M&A stages, even the existence of negotiations must be tightly guarded. Leakage of deal discussions can lead to:
Stock volatility
Competitor disruption
Supplier or customer anxiety
Employee attrition
To prevent this, non-disclosure agreements (NDAs) are signed before sharing even basic information. VDRs enforce this by granting access only to vetted parties and logging all user activity, discouraging leaks.
2. Due Diligence Security
This is the most data-sensitive phase. Buyers review:
Financial statements
Tax filings
Contracts
Intellectual property details
Litigation history
Cyber risk posture
Each document represents potential liability if exposed. A secure VDR ensures:
End-to-end encryption (AES-256 or higher)
Multi-factor authentication (MFA)
Granular access control down to the file or section level
View-only access with no downloads, printing, or screen capture
Watermarks with user IPs and timestamps
3. Auditability and Legal Traceability
To defend the integrity of the deal and respond to any post-deal disputes, every interaction must be tracked:
Who viewed what, when, and for how long
Questions asked and answered (Q&A logs)
Document version histories
These logs are part of legal documentation and are often retained long after the deal closes.
4. Cybersecurity Risk Assessment as a Deal Factor
Buyers often assess the seller’s cybersecurity posture as part of due diligence. Poor security (e.g., history of breaches, lax controls, outdated tech) may reduce valuation or kill the deal. Common items reviewed include:
Security policies
Incident response history
SOC 2 / ISO 27001 certifications
Penetration test results
Data breach disclosures
In this case, the VDR may host security documentation that itself must be securely handled.
5. Insider Risk and Privilege Escalation Control
Not all threats are external. Internal actors—disgruntled employees, opportunists, or even curious insiders—can leak or misuse information. VDRs address this by:
Role-based access (e.g., legal, finance, HR teams see only what’s necessary)
IP restriction (limit access by location)
Time-bound access with auto-expiry
Real-time alerts on suspicious behavior (e.g., large downloads)
6. Data Sovereignty and Compliance Risks
Cross-border M&A may involve GDPR, HIPAA, CCPA, or local data protection laws. VDRs must:
Store data in approved jurisdictions
Enable redaction tools
Offer data retention and deletion policies in compliance with local law
Failing to do this may introduce legal exposure before the deal even closes.
7. Post-Deal Data Handoff and Secure Closure
After the deal, secure handoff of all data—including audit trails—is essential. VDRs often allow data archiving in encrypted format for legal teams. Proper exit procedures also include:
Revoking third-party access
Exporting logs for compliance
Certifying destruction of temporary working copies
Final Thoughts
Security in M&A isn’t just about locking down data—it’s about enabling trust between parties while protecting the value of the transaction. A single breach could derail a deal or cause post-acquisition litigation. VDRs that offer bank-grade security, forensic logging, regulatory compliance, and intuitive access control are non-negotiable in high-stakes deals. However, companies must complement technology with clear policies and trained personnel to truly secure the process.
Would you like a framework (e.g., ISO 27001-aligned) to assess the security readiness of an M&A deal? info@deurainfosec.com
If you prefer not to use Microsoft Office in the U.S., you can try WPS Office instead, which is a free alternative offering many of the same features.
For users who do not wish to use Microsoft Office, WPS Office is a strong alternative worth considering. It’s a free office suite compatible with Word, Excel, and PowerPoint files, and offers a user-friendly interface along with cloud integration, PDF tools, and cross-platform support. It’s especially useful for individuals or small businesses looking to cut software costs without sacrificing essential functionality.
If you don’t want to use Microsoft Office, consider WPS Office — a free, lightweight, and fully compatible alternative. It’s ideal for individual users and small businesses (SMBs) who need powerful tools without the high licensing cost. WPS Office supports Word, Excel, and PowerPoint formats, and includes PDF editing, cloud storage integration, and templates for everyday business tasks. Its clean interface, cross-platform availability (Windows, macOS, Linux, Android, iOS), and low system requirements make it a great fit for teams working remotely or on a budget.
KaliGPT: Revolutionizing Cybersecurity With AI-Powered Intelligence In Kali Linux
Kali GPT doesn’t just support one set number of tools — it integrates deeply with all tools available in the Kali Linux ecosystem, which currently includes over 600 pre-installed security tools in the official Kali repositories – If it’s on Kali, Kali GPT supports it…
Kali GPT isn’t just an AI assistant — it’s a next-gen cybersecurity learning engine. For students aiming to enter the fields of ethical hacking, penetration testing, or digital forensics, here’s why Kali GPT is your ultimate study companion.
🧠 1. Learn by Doing, Not Just Reading
Kali GPT promotes hands-on, interactive learning, guiding students through:
Setting up Kali Linux environments (VMs, NetHunter, cloud)
Running and understanding real tools like Nmap, Wireshark, Metasploit
Building labs with targets like Metasploitable, Juice Shop, DVWA
This turns passive theory into active skill development.
In today’s rapidly changing cybersecurity landscape, staying ahead of threats demands more than just cutting-edge tools—it requires smart, real-time guidance.
Kali GPT is an AI assistant based on the GPT-4 architecture and is integrated with Kali Linux to support offensive security professionals and students. This groundbreaking tool marks a new era in penetration testing, acting as an intelligent co-pilot that redefines the cybersecurity workflow.
This new tool provides intelligent automation and real-time assistance. It can generate payloads, explain tools like Metasploit and Nmap, and recommend appropriate exploits—all directly within the terminal.
Key Features
Interactive Learning: Kali GPT acts as a tutor, guiding users through various cybersecurity tools and techniques. For example, if you want to master Metasploit, Kali GPT provides clear, step-by-step instructions, explanations, and best practices to accelerate your learning.
Real-Time Troubleshooting: Facing issues like a failed Nmap scan? Kali GPT diagnoses the problem, offers possible reasons, and suggests solutions to keep your tasks running smoothly.
Command Generation: Need a Linux command tailored to a specific task? Simply ask Kali GPT, such as “How can I find all files larger than 100MB in a directory?” and it will generate the precise command you need.
Seamless Tool Integration: Kali GPT connects directly with Kali Linux tools, enabling users to execute commands and receive feedback right within the interface—streamlining workflows and increasing productivity.
🐉Kali GPT’s methodology is primarily influenced by a synthesis of industry-proven methodologies and elite-level documentation, including:
