DISC InfoSec blog

Jan 10 2026

When Security Is Optional—Until It Isn’t

Category: cyber security,data security,Information Security,ISO 27k — disc7 @ 2:21 pm

ISO/IEC 27001 is often described as “essential,” but in reality, it remains a voluntary standard rather than a mandatory requirement. Its value depends less on obligation and more on organizational intent.

When leadership genuinely understands how deeply the business relies on information, the importance of managing information risk becomes obvious. In such cases, adopting 27001 is simply a logical extension of good governance.

For informed management teams, information security is not a technical checkbox but a business enabler. They recognize that protecting data protects revenue, reputation, and operational continuity.

In these environments, frameworks like 27001 support disciplined decision-making, accountability, and long-term resilience. The standard provides structure, not bureaucracy.

However, when leadership does not grasp the organization’s information dependency, advocacy often falls on deaf ears. No amount of persuasion will compensate for a lack of awareness.

Pushing too hard in these situations can be counterproductive. Without perceived risk, security efforts are seen as cost, friction, or unnecessary compliance.

Sometimes, the most effective catalyst is experience rather than explanation. A near miss or a real incident often succeeds where presentations and risk registers fail.

Once the business feels tangible pain—financial loss, customer impact, or reputational damage—the conversation changes quickly. Security suddenly becomes urgent and relevant.

That is when security leaders are invited in as problem-solvers, not prophets—stepping forward to help stabilize, rebuild, and guide the organization toward stronger governance and risk management.

My opinion:

This perspective is pragmatic, realistic, and—while a bit cynical—largely accurate in how organizations actually behave.

In an ideal world, leadership would proactively invest in ISO 27001 because they understand information risk as a core business risk. In practice, many organizations only act when risk becomes experiential rather than theoretical. Until there is pain, security feels optional.

That said, waiting for an incident should never be the strategy—it’s simply the pattern we observe. Incidents are expensive teachers, and the damage often exceeds what proactive governance would have cost. From a maturity standpoint, reactive adoption signals weak risk leadership.

The real opportunity for security leaders and vCISOs is to translate information risk into business language before the crisis: revenue impact, downtime, legal exposure, and trust erosion. When that translation lands, 27001 stops being “optional” and becomes a management tool.

Ultimately, ISO 27001 is not about compliance—it’s about decision quality. Organizations that adopt it early tend to be deliberate, resilient, and better governed. Those that adopt it after an incident are often doing damage control.

At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: iso 27001, Real Risk

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Jan 09 2026

The Hidden Frontlines: How Awareness, Intellectual Property, and Environment Shape Today’s Greatest Risks

Category: Risk Assessment,Security Awareness — disc7 @ 2:40 pm

Today’s most serious risks are no longer loud or obvious. Whether you are protecting an organization, leading people, or building resilience in your own life, the real threats — and opportunities — increasingly exist below the surface, hidden in systems, environments, and assumptions we rarely question.

Leadership, cybersecurity, and performance are being reshaped quietly. The rules aren’t changing overnight; they’re shifting gradually, often unnoticed, until the impact becomes unavoidable. Staying ahead now requires understanding these subtle shifts before they turn into crises. Everything begins with awareness. Not just awareness of cyber threats, but of the deeper drivers of vulnerability and strength. Intellectual property, environmental influence, and decision-making systems are emerging as critical factors that determine long-term success or failure.

This shift demands a move away from late-stage reaction. Instead of responding after alarms go off, leaders must understand the battlefield in advance — identifying where value truly lives and how it can be exposed without obvious warning signs. Intellectual property has become one of the most valuable — and most targeted — assets in the modern threat landscape. As traditional perimeter defenses weaken, attackers are no longer just chasing systems and data; they are pursuing ideas, research, trade secrets, and innovation itself.

IP protection is no longer a legal checkbox or an afterthought. Nation-states, competitors, and sophisticated actors are exploiting digital access to siphon knowledge and strategic advantage. Defending intellectual capital now requires executive attention, governance, and security alignment.
Cybersecurity is also deeply personal. Our environments — digital and physical — quietly shape how we think, decide, perform, and recover. Factors like constant digital noise, poor system design, and unhealthy surroundings compound over time, leading to fatigue, errors, and burnout.

This perspective challenges leaders to design not only secure systems, but sustainable lives. Clear thinking, sound judgment, and consistent performance depend on mastering the environment around us as much as mastering technology or strategy. When change happens quietly, awareness becomes the strongest form of defense. Whether protecting intellectual property, navigating uncertainty, or strengthening personal resilience, the greatest risks — and advantages — are often the ones we fail to see at first glance.

Opinion
In my view, this shift marks a critical evolution in how we think about risk and leadership. The organizations and individuals who win won’t be those with the loudest tools, but those with the deepest awareness. Seeing beneath the surface — of systems, environments, and value — is no longer optional; it’s the defining capability of modern resilience and strategic advantage.

Tags: Environment, Intellectual Property

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Jan 09 2026

AI Can Help Our Health — But at What Cost to Privacy?

Category: AI,AI Governance,Information Security — disc7 @ 8:34 am

Potential risks of sharing medical records with a consumer AI platform

OpenAI recently introduced “ChatGPT Health,” a specialized extension of ChatGPT designed to handle health-related conversations and enable users to link their medical records and wellness apps for more personalized insights. The company says this builds on its existing security framework.
According to OpenAI, the new health feature includes “additional, layered protections” tailored to sensitive medical information — such as purpose-built encryption and data isolation that aims to separate health data from other chatbot interactions.
The company also claims that data shared in ChatGPT Health won’t be used to train its broader AI models, a move intended to keep medical information out of the core model’s training dataset.
OpenAI says millions of users widely ask health and wellness questions on its platform already, which it uses to justify a dedicated space where those interactions can be more contextualized and, allegedly, safer.
Privacy advocates, however, are raising serious concerns. They note that medical records uploaded to ChatGPT Health are no longer protected by HIPAA, the U.S. law that governs how healthcare providers safeguard patients’ private health information.
Experts like Sara Geoghegan from the Electronic Privacy Information Center warn that releasing sensitive health data into OpenAI’s systems removes legal privacy protections and exposes users to risk. Without a law like HIPAA applying to ChatGPT, the company’s own policies are the only thing standing between users and potential misuse.
Critics also caution that OpenAI’s evolving business model, particularly if it expands into personalization or advertising, could create incentives to use health data in ways users don’t expect or fully understand.
Key questions remain unanswered, such as how exactly the company would respond to law enforcement requests for health data and how effectively health data is truly isolated from other systems if policies change.
The feature’s reliance on connected wellness apps and external partners also introduces additional vectors where sensitive information could potentially be exposed or accessed if there’s a breach or policy change.
In summary, while OpenAI pitches ChatGPT Health as an innovation with enhanced safeguards, privacy advocates argue that without robust legal protections and clear transparency, sharing medical records with a consumer AI platform remains risky.

My Opinion

AI has immense potential to augment how people manage and understand their health, especially for non-urgent questions or preparing for doctor visits. But giving any tech company access to medical records without the backing of strong legal protections like HIPAA feels premature and potentially unsafe. Technical safeguards such as encryption and data isolation matter — but they don’t replace enforceable privacy laws that restrict how health data can be used, shared, or disclosed. In healthcare, trust and accountability are paramount, and without those, even well-intentioned tools can expose individuals to privacy risks or misuse of deeply personal information. Until regulatory frameworks evolve to explicitly protect AI-mediated health data, users should proceed with caution and understand the privacy trade-offs they’re making.

Tags: AI Health, ChatGPT Health, privacy concerns

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Jan 09 2026

AI Agent Security: The Next Frontier of Cyber Risk and Defense

Category: AI,AI Governance — disc7 @ 7:30 am

10 key reasons why securing AI agents is essential

1. Artificial intelligence is rapidly becoming embedded in everyday digital tools — from chatbots to virtual assistants — and this evolution has introduced a new class of autonomous systems called AI agents that can understand, respond, and even make decisions independently.

2. Unlike traditional AI, which simply responds to commands, AI agents can operate continuously, interact with multiple systems, and perform complex tasks on behalf of users, making them extremely powerful helpers.

3. But with that autonomy comes risk: agents often access sensitive data, execute actions, and connect to other applications with minimal human oversight — which means attackers could exploit these capabilities to do significant harm.

4. Hackers no longer have to “break in” through conventional vulnerabilities like weak passwords. Instead, they can manipulate how an AI agent interprets instructions, using crafted inputs to trick the agent into revealing private information or taking harmful actions.

5. These new attack vectors are fundamentally different from classic cyberthreats because they exploit the behavioral logic of the AI rather than weaknesses in software code or network defenses.

6. Traditional security tools — firewalls, antivirus software, and network encryption — are insufficient for defending such agents, because they don’t monitor the intent behind what the AI is doing or how it can be manipulated by inputs.

7. Additionally, security is not just a technology issue; humans influence AI through data and instructions, so understanding how people interact with agents and training users to avoid unsafe inputs is also part of securing these systems.

8. The underlying complexity of AI — its ability to learn and adapt to new information — means that its behavior can be unpredictable and difficult to audit, further complicating security efforts.

9. Experts argue that AI agents need guardrails similar to traffic rules for autonomous vehicles: clear limits, behavior monitoring, access controls, and continuous oversight to prevent misuse or unintended consequences.

10. Looking ahead, securing AI agents will require new defensive strategies — from building security into AI design to implementing runtime behavior monitoring and shaping governance frameworks — because agent security is becoming a core pillar of overall cyber defense.

Opinion

AI agents represent one of the most transformative technological shifts in modern computing — and their security challenges are equally transformative. While their autonomy unlocks efficiency and capability, it also introduces entirely new attack surfaces that traditional cybersecurity tools weren’t designed to handle. Investing in agent-specific security measures isn’t just proactive, it’s essential — the sooner organizations treat AI security as a strategic priority rather than an afterthought, the better positioned they’ll be to harness AI safely and responsibly.

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Jan 08 2026

California Opt Me Out Act: A New Era of Automated Privacy Control

Category: Information Privacy,Security and privacy Law — disc7 @ 10:00 am

In October, California enacted the California Opt Me Out Act, a new privacy law designed to strengthen consumer control over personal data. The legislation officially came into effect on January 1 of this year.

The core goal of the Act is to make data privacy rights easier to exercise, not just easier to understand. It shifts the burden away from consumers having to navigate complex privacy settings on individual websites.

A key requirement of the law is that web browsers operating in California must support simple, standardized opt-out preference signals. These signals allow users to automatically communicate their privacy choices to websites they visit.

Instead of repeatedly clicking “Do Not Sell or Share My Personal Information” links, users can rely on browser-level signals to express their preferences consistently across the web.

The Act goes beyond traditional web tracking by recognizing the growing role of device-based identifiers. Californians are now able to opt out using marketing identifiers from mobile phones, smart TVs, and other connected devices.

Notably, the law also allows consumers to include vehicle identification numbers (VINs), acknowledging that modern vehicles generate and share significant amounts of personal and behavioral data.

By expanding opt-out rights across browsers, devices, and vehicles, the Act reflects a broader understanding of how personal data is collected in today’s connected ecosystem.

For businesses, this introduces new compliance expectations. Organizations must be able to recognize and honor these opt-out signals reliably, or risk falling out of compliance with California privacy regulations.

Overall, the California Opt Me Out Act represents a shift toward automated, user-centric privacy controls that reduce friction and increase transparency in how personal data is handled.

Delete your data with DROP

Opinion
In my view, this law is an important evolution in privacy regulation. It moves privacy from static policies and manual consent banners toward enforceable, machine-readable signals. While it raises the compliance bar for organizations, it also sets a clear direction: privacy controls must be practical, scalable, and built into the technology people use every day—not buried behind legal jargon and multiple clicks.

On Privacy and Technology

Tags: Automated Privacy Control, California Opt Me Out Act

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Jan 07 2026

Agentic AI: Why Autonomous Systems Redefine Enterprise Risk

Category: AI,AI Governance,Information Security — disc7 @ 1:24 pm

Evolution of Agentic AI

1. Machine Learning

Machine Learning represents the foundation of modern AI, focused on learning patterns from structured data to make predictions or classifications. Techniques such as regression, decision trees, support vector machines, and basic neural networks enable systems to automate well-defined tasks like forecasting, anomaly detection, and image or object recognition. These systems are effective but largely reactive—they operate within fixed boundaries and lack reasoning or adaptability beyond their training data.

2. Neural Networks

Neural Networks expand on traditional machine learning by enabling deeper pattern recognition through layered architectures. Convolutional and recurrent neural networks power image recognition, speech processing, and sequential data analysis. Capabilities such as deep reinforcement learning allow systems to improve through feedback, but decision-making is still task-specific and opaque, with limited ability to explain reasoning or generalize across domains.

3. Large Language Models (LLMs)

Large Language Models introduce reasoning, language understanding, and contextual awareness at scale. Built on transformer architectures and self-attention mechanisms, models like GPT enable in-context learning, chain-of-thought reasoning, and natural language interaction. LLMs can synthesize knowledge, generate code, retrieve information, and support complex workflows, marking a shift from pattern recognition to generalized cognitive assistance.

4. Generative AI

Generative AI extends LLMs beyond text into multimodal creation, including images, video, audio, and code. Capabilities such as diffusion models, retrieval-augmented generation, and multimodal understanding allow systems to generate realistic content and integrate external knowledge sources. These models support automation, creativity, and decision support but still rely on human direction and lack autonomy in planning or execution.

5. Agentic AI

Agentic AI represents the transition from AI as a tool to AI as an autonomous actor. These systems can decompose goals, plan actions, select and orchestrate tools, collaborate with other agents, and adapt based on feedback. Features such as memory, state persistence, self-reflection, human-in-the-loop oversight, and safety guardrails enable agents to operate over time and across complex environments. Agentic AI is less about completing individual tasks and more about coordinating context, tools, and decisions to achieve outcomes.

Key Takeaway

The evolution toward Agentic AI is not a single leap but a layered progression—from learning patterns, to reasoning, to generating content, and finally to autonomous action. As organizations adopt agentic systems, governance, risk management, and human oversight become just as critical as technical capability.

Security and governance lens (AI risk, EU AI Act, NIST AI RMF)

Zero Trust Agentic AI Security: Runtime Defense, Governance, and Risk Management for Autonomous Systems

Tags: Agentic AI, Autonomous syatems, Enterprise Risk Management

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Jan 07 2026

7 Essential CISO Capabilities for Board-Level Cyber Risk Oversight

Category: AI Governance,CISO,Data Governance,IT Governance,Risk Assessment,vCISO — disc7 @ 9:19 am

1. Governance Oversight

A CISO must design and operate a security governance model that aligns with corporate governance, regulatory requirements, and the organization’s risk appetite. This ensures security controls are consistent, auditable, and defensible. Without strong governance, organizations face regulatory penalties, audit failures, and fragmented or overlapping controls that create risk instead of reducing it.

2. Cybersecurity Maturity Management

The CISO should continuously assess the organization’s security posture using recognized maturity models such as NIST CSF or ISO 27001, and define a clear target state. This capability enables prioritization of investments and long-term improvement. Lacking maturity management leads to reactive, ad-hoc spending and an inability to justify or sequence security initiatives.

3. Incident Response (Response Readiness)

A core responsibility of the CISO is ensuring the organization is prepared for incidents through tested playbooks, simulations, and war-gaming. Effective response readiness minimizes impact when breaches occur. Without it, detection is slow, downtime is extended, and financial and reputational damage escalates rapidly.

4. Detection, Response & Automation (SOC / SOAR Capability)

The CISO must ensure the organization can rapidly detect threats, alert the right teams, and automate responses where possible. Strong SOC and SOAR capabilities reduce mean time to detect (MTTD) and mean time to respond (MTTR). Weakness here results in undetected breaches, slow manual responses, and delayed forensic investigations.

5. Business & Financial Acumen

A modern CISO must connect cyber risk to business outcomes—revenue, margins, valuation, and enterprise risk. This includes articulating ROI, payback, and value creation. Without this skill, security is viewed purely as a cost center, and investments fail to align with business strategy.

6. Risk Communication

The CISO must translate complex technical risks into clear, business-impact narratives that boards and executives can act on. Effective risk communication enables informed decision-making. When this capability is weak, risks remain misunderstood or hidden until a major incident forces attention.

7. Culture & Cross-Functional Leadership

A successful CISO builds strong security teams, fosters a security-aware culture, and collaborates across IT, legal, finance, product, and operations. Security cannot succeed in silos. Poor leadership here leads to misaligned priorities, weak adoption of controls, and ineffective onboarding of new staff into security practices.

My Opinion: The Three Most Important Capabilities

If forced to prioritize, the top three are:

Risk Communication
If the board does not understand risk, no other capability matters. Funding, priorities, and executive decisions all depend on how well the CISO communicates risk in business terms.
Governance Oversight
Governance is the foundation. Without it, security efforts are fragmented, compliance fails, and accountability is unclear. Strong governance enables everything else to function coherently.
Incident Response (Response Readiness)
Breaches are inevitable. What separates resilient organizations from failed ones is how well they respond. Preparation directly limits financial, operational, and reputational damage.

Bottom line:
Technology matters, but leadership, governance, and communication are what boards ultimately expect from a CISO. Tools support these capabilities—they don’t replace them.

Tags: CISO Capabilities

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Jan 06 2026

Why Continuous Risk Management Is the Future of AppSec

Category: App Security — disc7 @ 2:22 pm

Continuous risk management in AppSec

The video stresses that continuous risk management is now essential in application security. Rather than treating security as a one-time task, risk needs to be monitored and managed continuously as code changes.
In modern software environments where codebases evolve rapidly, traditional static risk assessments can quickly become outdated. This means vulnerabilities may emerge after assessments are done.
The video suggests that ignoring continuous approaches can leave applications exposed because fixing risks only after the fact is too slow for agile development cycles.
Adopting a continuous risk management mindset helps teams stay aligned with evolving threats and development changes, improving overall security posture.
Continuous risk management in AppSec also supports better decision-making, since teams have up-to-date risk insights rather than relying on periodic snapshots.

Application security can’t rely on point-in-time risk assessments anymore.

Code changes constantly. Threats evolve daily. Yet many organizations still treat risk as a one-and-done exercise. That gap is where real exposure lives.

Continuous risk management shifts AppSec from static reporting to real-time awareness. It helps teams see risk as it emerges, prioritize what matters now, and make faster, better security decisions aligned with modern development cycles.

In today’s environment, security that isn’t continuous is already outdated.

#AppSec
#CyberRisk
#SecureDevelopment
#DevSecOps
#Cybersecurity

Fundamentals of Risk Management: Understanding, Evaluating and Implementing Effective Enterprise Risk Management

Tags: AppSec, LLM

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Jan 06 2026

Zero Trust Isn’t About Distrust — It’s About Intentional Access

Category: Zero trust — disc7 @ 1:52 pm

Zero Trust is often misunderstood in cybersecurity discussions. Many assume it means trusting no one at all or treating every user as a threat. In reality, its purpose is much simpler and more practical: replacing assumptions with explicit decisions.

Traditional enterprise environments tend to accumulate trust over time. Networks become flatter, exceptions pile up, and access grows because legacy processes are rarely revisited. What once made sense eventually becomes risk through inertia.

Zero Trust challenges this pattern by forcing deliberate thinking. Instead of default access, organizations are encouraged to clearly define who needs access, from where they should connect, under what conditions access is granted, and how long it should last.

This shift brings unexpected benefits. When applied correctly, Zero Trust can actually reduce complexity. Access rules become clearer, security decisions are easier to justify, and audits are smoother because intent is documented rather than assumed.

The hardest parts of Zero Trust are rarely technical. Tools can enable it, but they don’t define it. The real challenges lie in ownership, alignment across teams, and having shared clarity on access decisions.

Without organizational buy-in, Zero Trust initiatives often stall or become checkbox exercises. With it, the approach integrates naturally into daily operations.

Ultimately, Zero Trust works best when it’s treated as an architectural mindset, not a product to be purchased. When organizations think this way, Zero Trust becomes sustainable and effective rather than complex and fragile.

My opinion: Zero Trust is one of the few security concepts that actually improves both security and operational clarity when done right.

In practice, I’ve seen Zero Trust succeed not because of sophisticated tools, but because it forces organizations to confront uncomfortable questions about access, ownership, and accountability. That discipline alone eliminates a surprising amount of hidden risk. When teams can clearly explain why someone has access, security stops being reactive and becomes intentional.

Where Zero Trust fails is when it’s treated as a vendor-driven initiative or a network-only problem. Slapping a “Zero Trust” label on identity tools or segmentation projects without changing decision-making habits just recreates old trust models with new technology.

When leaders embrace Zero Trust as a mindset—explicit access, time-bound decisions, and shared ownership—it scales well and ages gracefully. In that sense, Zero Trust isn’t a destination; it’s a way to keep security architecture honest as organizations grow and change.

Agentic AI + Zero Trust: A Guide for Business Leaders

Tags: Intentional Access, Zero Trust

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Jan 06 2026

The Best Cybersecurity Investment Strategy: Balance Fast Wins with Long-Term Resilience

Category: Cyber Investment,Cyber resilience,Cyber Strategy,Information Security — disc7 @ 9:44 am

Cybersecurity Investment Strategy: Investing With Intent

One of the most common mistakes organizations make in cybersecurity is investing in tools and controls without a clear, outcome-driven strategy. Not every security investment delivers value at the same speed, and not all controls produce the same long-term impact. Without prioritization, teams often overspend on complex solutions while leaving foundational gaps exposed.

A smarter approach is to align security initiatives based on investment level versus time to results. This is where a Cybersecurity Investment Strategy Matrix becomes valuable—it helps leaders visualize which initiatives deliver immediate risk reduction and which ones compound value over time. The goal is to focus resources on what truly moves the needle for the business.

Some initiatives provide fast results but require higher investment. Capabilities like EDR/XDR, SIEM and SOAR platforms, incident response readiness, and next-generation firewalls can rapidly improve detection and response. These are often critical for organizations facing active threats or regulatory pressure, but they demand both financial and operational commitment.

Other controls deliver fast results with relatively low investment. Measures such as multi-factor authentication, password managers, security baselines, and basic network segmentation quickly reduce attack surface and prevent common breaches. These are often high-impact, low-friction wins that should be implemented early.

Certain investments are designed for long-term payoff and require significant investment. Zero Trust architectures, DevSecOps, CSPM, identity governance, and DLP programs strengthen security at scale and maturity, but they take time, cultural change, and sustained funding to deliver full value.

Finally, there are long-term, low-investment initiatives that quietly build resilience over time. Security awareness training, vulnerability management, penetration testing, security champions programs, strong documentation, meaningful KPIs, and open-source security tools all improve security posture steadily without heavy upfront costs.

A well-designed cybersecurity investment strategy balances quick wins with long-term capability building. The real question for leadership isn’t what tools to buy, but which three initiatives, if prioritized today, would reduce the most risk and support the business right now.

My opinion: the best cybersecurity investment strategy is a balanced, risk-driven mix of fast wins and long-term foundations, not an “all-in” bet on any single quadrant.

Here’s why:

Start with fast results / low investment (mandatory baseline)
This should be non-negotiable. Controls like MFA, security baselines, password managers, and basic network segmentation deliver immediate risk reduction at minimal cost. Skipping these while investing in advanced tools is one of the most common (and expensive) mistakes I see.
Add 1–2 fast results / high investment controls (situational)
Once the basics are in place, selectively invest in high-impact capabilities like EDR/XDR or incident response readiness—only if your threat profile, regulatory exposure, or business criticality justifies it. These tools are powerful, but without maturity, they become noisy and underutilized.
Continuously build long-term / low investment foundations (quiet multiplier)
Security awareness, vulnerability management, documentation, and KPIs don’t look flashy, but they compound over time. These initiatives increase ROI on every other control you deploy and reduce operational friction.
Delay long-term / high investment initiatives until maturity exists
Zero Trust, DevSecOps, DLP, and identity governance are excellent goals—but pursuing them too early often leads to stalled programs and wasted spend. These work best when governance, ownership, and basic hygiene are already solid.

Bottom line:
The best strategy is baseline first → targeted protection next → long-term maturity in parallel.
If I had to simplify it:

Fix what attackers exploit most today, while quietly building the capabilities that prevent tomorrow’s failures.

This approach aligns security spend with business risk, avoids tool sprawl, and delivers both immediate and sustained value.

Tags: Cybersecurity Investment Strategy

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Jan 05 2026

Why IRT War Rooms Are Critical for Effective Incident Response

Category: Security Incident — disc7 @ 12:22 pm

IRT war rooms are essential because they impose discipline, focus, and clarity at the exact moment when chaos is most likely to take over.

During serious incidents, the biggest risks are not just technical failures but fragmented communication, unclear ownership, and cognitive overload. A war room creates a single source of truth—one place where decisions are made, actions are tracked, and priorities are aligned. This dramatically reduces duplication of effort, conflicting instructions, and rumor-driven responses.

War rooms also enforce accountability under pressure. By clearly assigning roles (via RACI), verbalizing milestones, and recording decisions, they prevent hindsight confusion and “who knew what, when” disputes. This is invaluable not only for recovery, but also for executive briefings, legal defensibility, and regulatory scrutiny.

Equally important, war rooms protect the response team. By isolating responders from constant interruptions and external noise, they preserve cognitive bandwidth—something that is often underestimated but critical in high-severity incidents where small mistakes can have outsized consequences.

In short, an effective IRT war room turns incident response from a reactive scramble into a controlled, auditable, and business-aligned operation. Organizations that treat war rooms as a formal capability—rather than an ad hoc call—consistently respond faster, communicate better, and recover with less damage.

When a security incident escalates to Severity Level 2 or higher, establishing an Incident Response (IRT) war room becomes critical. A war room allows responders to step away from daily distractions, maintain focus, and work in a tightly coordinated environment. By isolating the response team, organizations reduce noise, prevent miscommunication, and enable faster, more accurate decision-making during high-pressure situations.

The war room is initiated by the incident lead and typically takes the form of a dedicated Zoom session that remains open throughout the active phase of the incident. Recording the session ensures that decisions, discussions, and actions are fully captured. Early in the meeting, a designated reporter is assigned to provide structured and periodic updates to key stakeholders who are not directly involved in the response, ensuring transparency without disrupting the response team.

Clear roles and accountability are essential in a war room. The team should reference the IRT RACI chart to announce major response functions and confirm ownership of each activity. Key milestones—such as completing a preliminary damage assessment—should be explicitly stated and shared as they occur. This structured approach ensures leadership and external stakeholders receive consistent, accurate updates aligned with the incident’s progression.

As response activities unfold, actions taken by the team should be clearly described and documented during the session. Capturing sufficient detail in real time helps preserve institutional knowledge and creates a reliable record of how the incident was handled. The communication method used should align with the severity level, ensuring the right balance between speed, accuracy, and control.

Once the Zoom recording is available, a transcript is generated and stored along with the recording in the organization’s IRT repository. The transcript is also uploaded to the document management system, where summaries can be produced for post-incident analysis, reporting, and continuous improvement. In my view, well-run IRT war rooms are not just operational tools—they are critical governance mechanisms that improve response quality, accountability, and long-term security maturity.

Tags: Incident war rooms, IRT war rooms

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Jan 05 2026

Deepfakes Cost $25 Million: Why Old-School Verification Still Works

Category: AI,AI Governance,Deepfakes — disc7 @ 9:01 am

A British engineering firm reportedly lost $25 million after an employee joined a video call that appeared to include their CFO. The voice, the face, and the mannerisms all checked out—but it wasn’t actually him. The incident highlights how convincing deepfake technology has become and how easily trust can be exploited.

This case shows that visual and audio cues alone are no longer reliable for verification. AI can now replicate voices and faces with alarming accuracy, making traditional “it looks and sounds right” judgment calls dangerously insufficient, especially under pressure.

Ironically, the most effective countermeasure to advanced AI attacks isn’t more technology—it’s simpler, human-centered controls. When digital signals can be forged, analog verification methods regain their value.

One such method is establishing a “safe word.” This is a randomly chosen word known only to a small, trusted group and never shared via email, chat, or documents. It lives only in human memory.

If an urgent request comes in—whether from a “CEO,” “CFO,” or even a family member—especially involving money or sensitive actions, the response should be to pause and ask for the safe word. An AI can mimic a voice, but it cannot reliably guess a secret it was never trained on.

My opinion: Safe words may sound old-fashioned, but they are practical, low-cost, and highly effective in a world of deepfakes and social engineering. Every finance team—and even families—should treat this as a basic risk control, not a gimmick. In high-risk moments, simple friction can be the difference between trust and a multimillion-dollar loss.

#CyberSecurity #DeepFakes #SocialEngineering #AI #RiskManagement

Tags: Deepfake, Deepfakes and Fraud

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Jan 04 2026

AI Governance That Actually Works: Beyond Policies and Promises

Category: AI,AI Governance,AI Guardrails,ISO 42001,NIST CSF — disc7 @ 3:33 pm

1. AI Has Become Core Infrastructure
AI is no longer experimental — it’s now deeply integrated into business decisions and societal functions. With this shift, governance can’t stay theoretical; it must be operational and enforceable. The article argues that combining the NIST AI Risk Management Framework (AI RMF) with ISO/IEC 42001 makes this operationalization practical and auditable.

2. Principles Alone Don’t Govern
The NIST AI RMF starts with the Govern function, stressing accountability, transparency, and trustworthy AI. But policies by themselves — statements of intent — don’t ensure responsible execution. ISO 42001 provides the management-system structure that anchors these governance principles into repeatable business processes.

3. Mapping Risk in Context
Understanding the context and purpose of an AI system is where risk truly begins. The NIST RMF’s Map function asks organizations to document who uses a system, how it might be misused, and potential impacts. ISO 42001 operationalizes this through explicit impact assessments and scope definitions that force organizations to answer difficult questions early.

4. Measuring Trust Beyond Accuracy
Traditional AI metrics like accuracy or speed fail to capture trustworthiness. The NIST RMF expands measurement to include fairness, explainability, privacy, and resilience. ISO 42001 ensures these broader measures aren’t aspirational — they require documented testing, verification, and ongoing evaluation.

5. Managing the Full Lifecycle
The Manage function addresses what many frameworks ignore: what happens after AI deployment. ISO 42001 formalizes post-deployment monitoring, incident reporting and recovery, decommissioning, change management, and continuous improvement — framing AI systems as ongoing risk assets rather than one-off projects.

6. Third-Party & Supply Chain Risk
Modern AI systems often rely on external data, models, or services. Both frameworks treat third-party and supplier risks explicitly — a critical improvement, since risks extend beyond what an organization builds in-house. This reflects growing industry recognition of supply chain and ecosystem risk in AI.

7. Human Oversight as a System
Rather than treating human review as a checkbox, the article emphasizes formalizing human roles and responsibilities. It calls for defined escalation and override processes, competency-based training, and interdisciplinary decision teams — making oversight deliberate, not incidental.

8. Strategic Value of NIST-ISO Alignment
The real value isn’t just technical alignment — it’s strategic: helping boards, executives, and regulators speak a common language about risk, accountability, and controls. This positions organizations to be both compliant with emerging regulations and competitive in markets where trust matters.

9. Trust Over Speed
The article closes with a cultural message: in the next phase of AI adoption, trust will outperform speed. Organizations that operationalize responsibility (through structured frameworks like NIST AI RMF and ISO 42001) will lead, while those that chase innovation without governance risk reputational harm.

10. Practical Implications for Leaders
For AI leaders, the takeaway is clear: you need both risk-management logic and a management system to ensure accountability, measurement, and continuous improvement. Cryptic policies aren’t enough; frameworks must translate into auditable, executive-reportable actions.

Opinion

This article provides a thoughtful and practical bridge between high-level risk principles and real-world governance. NIST’s AI RMF on its own captures what needs to be considered (governance, context, measurement, and management) — a critical starting point for responsible AI risk management. (NIST)

But in many organizations today, abstract frameworks don’t translate into disciplined execution — that gap is exactly where ISO/IEC 42001 can add value by prescribing systematic processes, roles, and continuous improvement cycles. Together, the NIST AI RMF and ISO 42001 form a stronger operational baseline for responsible, auditable AI governance.

In practice, however, the challenge will be in integration — aligning governance systems already in place (e.g., ISO 27001, internal risk programs) with these newer AI standards without creating redundancy or compliance fatigue. The real test of success will be whether organizations can bake these practices into everyday decision-making, not just compliance checklists.

Tags: ISO 42001, NIST AI Risk Management Framework, NIST AI RMF

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Jan 03 2026

Choosing the Right AI Security Frameworks: A Practical Roadmap for Secure AI Adoption

Category: AI,AI Governance,ISO 42001,Security and privacy Law,Security Compliance — disc7 @ 11:22 pm

Choosing the right AI security framework is becoming a critical decision as organizations adopt AI at scale. No single framework solves every problem. Each one addresses a different aspect of AI risk, governance, security, or compliance, and understanding their strengths helps organizations apply them effectively.

The NIST AI Risk Management Framework (AI RMF) is best suited for managing AI risks across the entire lifecycle—from design and development to deployment and ongoing use. It emphasizes trustworthy AI by addressing security, privacy, safety, reliability, and bias. This framework is especially valuable for organizations that are building or rapidly scaling AI capabilities and need a structured way to identify and manage AI-related risks.

ISO/IEC 42001, the AI Management System (AIMS) standard, focuses on governance rather than technical controls. It helps organizations establish policies, accountability, oversight, and continuous improvement for AI systems. This framework is ideal for enterprises deploying AI across multiple teams or business units and looking to formalize AI governance in a consistent, auditable way.

For teams building AI-enabled applications, the OWASP Top 10 for LLMs and Generative AI provides practical, hands-on security guidance. It highlights common and emerging risks such as prompt injection, data leakage, insecure output handling, and model abuse. This framework is particularly useful for AppSec and DevSecOps teams securing AI interfaces, APIs, and user-facing AI features.

MITRE ATLAS takes a threat-centric approach by mapping adversarial tactics and techniques that target AI systems. It is well suited for threat modeling, red-team exercises, and AI breach simulations. By helping security teams think like attackers, MITRE ATLAS strengthens defensive strategies against real-world AI threats.

From a regulatory perspective, the EU AI Act introduces a risk-based compliance framework for organizations operating in or offering AI services within the European Union. It defines obligations for high-risk AI systems and places strong emphasis on transparency, accountability, and risk controls. For global organizations, this regulation is becoming a key driver of AI compliance strategy.

The most effective approach is not choosing one framework, but combining them. Using NIST AI RMF for risk management, ISO/IEC 42001 for governance, OWASP and MITRE for technical security, and the EU AI Act for regulatory compliance creates a balanced and defensible AI security posture.

Tags: AI Security Frameworks

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Jan 03 2026

Self-Assessment Tools That Turn Compliance Confusion into a Clear Roadmap

Category: AI Governance Tools,CISO,GRC,ISO 27k,ISO 42001,Security Tools,vCISO — disc7 @ 11:04 pm

GRC Solutions offers a collection of self-assessment and gap analysis tools designed to help organisations evaluate their current compliance and risk posture across a variety of standards and regulations. These tools let you measure how well your existing policies, controls, and processes match expectations before you start a full compliance project.
Several tools focus on ISO standards, such as ISO 27001:2022 and ISO 27002 (information security controls), which help you identify where your security management system aligns or falls short of the standard’s requirements. Similar gap analysis tools are available for ISO 27701 (privacy information management) and ISO 9001 (quality management).
For data protection and privacy, there are GDPR-related assessment tools to gauge readiness against the EU General Data Protection Regulation. These help you see where your data handling and privacy measures require improvement or documentation before progressing with compliance work.
The Cyber Essentials Gap Analysis Tool is geared toward organisations preparing for this basic but influential UK cybersecurity certification. It offers a simple way to assess the maturity of your cyber controls relative to the Cyber Essentials criteria.
Tools also cover specialised areas such as PCI DSS (Payment Card Industry Data Security Standard), including a self-assessment questionnaire tool to help identify how your card-payment practices align with PCI requirements.
There are industry-specific and sector-tailored assessment tools too, such as versions of the GDPR gap assessment tailored for legal sector organisations and schools, recognising that different environments have different compliance nuances.
Broader compliance topics like the EU Cloud Code of Conduct and UK privacy regulations (e.g., PECR) are supported with gap assessment or self-assessment tools. These allow you to review relevant controls and practices in line with the respective frameworks.
A NIST Gap Assessment Tool helps organisations benchmark against the National Institute of Standards and Technology framework, while a DORA Gap Analysis Tool addresses preparedness for digital operational resilience regulations impacting financial institutions.
Beyond regulatory compliance, the catalogue includes items like a Business Continuity Risk Management Pack and standards-related gap tools (e.g., BS 31111), offering flexibility for organisations to diagnose gaps in broader risk and continuity planning areas as well.

Self-assessment tools

Browse wide range of self-assessment tools, covering topics such as the GDPR, ISO 27001 and Cyber Essentials, to identify the gaps in your compliance projects.

Tags: Self Assessment Tools

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Jan 03 2026

8 Practical Cybersecurity Steps Every Small Business Can Take Today

Category: cyber security,Information Security — disc7 @ 11:47 am

Many small and medium businesses are attractive targets for cybercriminals because they hold valuable data and often lack extensive IT resources. Threats like ransomware, phishing and business email compromise can disrupt operations, damage reputation, and cause financial loss. Recognizing that no business is too small to be targeted is the first step toward protection.

1. Teach employees to recognize and report phishing attacks. Phishing is one of the primary ways attackers gain access. Regular awareness training helps staff spot suspicious emails, links, and requests, reducing the chance that a click triggers a breach.

2. Require strong passwords across your organization. Weak or reused passwords are easily guessed or brute-forced. Establish a strong password policy and consider tools like password managers so employees can securely store unique credentials.

3. Implement multifactor authentication (MFA). Adding MFA means users must provide more than just a password to access accounts. This extra layer of verification dramatically reduces the odds that attackers can impersonate employees, even if they obtain a password.

4. Keep systems and software up to date. Outdated software often contains known security flaws that attackers exploit. Having regular patching schedules and enabling automatic updates wherever possible keeps your systems protected against many common vulnerabilities.

5. Enable logging and monitoring. Logging system activity gives you visibility into what’s happening on your network. Monitoring logs helps detect suspicious behavior early, so you can respond before an incident becomes a major breach.

6. Back up your business data regularly. Ransomware and other failures can cripple operations if you can’t access critical files. Maintain backups following a reliable strategy—such as the 3-2-1 rule—to ensure you can restore data quickly and resume business functions.

7. Encrypt sensitive data and devices. Encryption transforms your data into unreadable code for anyone without access keys. Applying encryption to data at rest and in transit helps protect information even if a device is lost or a system is compromised.

8. Report cyber incidents and share threat information. If an incident occurs, reporting it to agencies like CISA helps the broader business community stay informed about emerging threats and may provide access to additional guidance or alerts.

Taken together, these steps create a practical cybersecurity foundation for your business. Start with basics like employee training and MFA, then build up to backups, encryption, and incident reporting to strengthen your resiliency against evolving threats.

Source: You Can Protect Your Business from Online Threats (CISA)

Tags: Cybersecurity for SMBs

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Jan 02 2026

No Breach, No Alerts—Still Stolen: When AI Models Are Taken Without Being Hacked

Category: AI,AI Governance,AI Guardrails — disc7 @ 11:11 am

No Breach. No Alerts. Still Stolen: The Model Extraction Problem

1. A company can lose its most valuable AI intellectual property without suffering a traditional security breach. No malware, no compromised credentials, no incident tickets—just normal-looking API traffic. Everything appears healthy on dashboards, yet the core asset is quietly walking out the door.

2. This threat is known as model extraction. It happens when an attacker repeatedly queries an AI model through legitimate interfaces—APIs, chatbots, or inference endpoints—and learns from the responses. Over time, they can reconstruct or closely approximate the proprietary model’s behavior without ever stealing weights or source code.

3. A useful analogy is a black-box expert. If I can repeatedly ask an expert questions and carefully observe their answers, patterns start to emerge—how they reason, where they hesitate, and how they respond to edge cases. Over time, I can train someone else to answer the same questions in nearly the same way, without ever seeing the expert’s notes or thought process.

4. Attackers pursue model extraction for several reasons. They may want to clone the model outright, steal high-value capabilities, distill it into a cheaper version using your model as a “teacher,” or infer sensitive traits about the training data. None of these require breaking in—only sustained access.

5. This is why AI theft doesn’t look like hacking. Your model can be copied simply by being used. The very openness that enables adoption and revenue also creates a high-bandwidth oracle for adversaries who know how to exploit it.

6. The consequences are fundamentally business risks. Competitive advantage evaporates as others avoid your training costs. Attackers discover and weaponize edge cases. Malicious clones can damage your brand, and your IP strategy collapses because the model’s behavior has effectively been given away.

7. The aftermath is especially dangerous because it’s invisible. There’s no breach report or emergency call—just a competitor releasing something “surprisingly similar” months later. By the time leadership notices, the damage is already done.

8. At scale, querying equals learning. With enough inputs and outputs, an attacker can build a surrogate model that is “good enough” to compete, abuse users, or undermine trust. This is IP theft disguised as legitimate usage.

9. Defending against this doesn’t require magic, but it does require intent. Organizations need visibility by treating model queries as security telemetry, friction by rate-limiting based on risk rather than cost alone, and proof by watermarking outputs so stolen behavior can be attributed when clones appear.

My opinion: Model extraction is one of the most underappreciated risks in AI today because it sits at the intersection of security, IP, and business strategy. If your AI roadmap focuses only on performance, cost, and availability—while ignoring how easily behavior can be copied—you don’t really have an AI strategy. Training models is expensive; extracting behavior through APIs is cheap. And in most markets, “good enough” beats “perfect.”

Tags: AI Models, Hacked

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Jan 01 2026

Not All Risks Are Equal: What Every Organization Must Know

Category: Risk Assessment,Security Risk Assessment — disc7 @ 11:15 am

Types of Risk & Risk Assessment

Organizations face multiple types of risks that can affect strategy, operations, compliance, and reputation. Strategic risks arise when business objectives or long-term goals are threatened—such as when weak security planning damages customer confidence. Operational risks stem from human errors, flawed processes, or technology failures, like a misconfigured firewall or inadequate incident response.

Cyber and information security risks affect the confidentiality, integrity, and availability of data. Examples include ransomware attacks, data breaches, and insider threats. Compliance or regulatory risks occur when companies fail to meet legal or industry requirements such as ISO 27001, ISO 42001, GDPR, PCI-DSS, or IEC standards.

Financial risk is tied to monetary losses through fraud, fines, or system downtime. Reputational risks damage stakeholder trust and the public perception of the organization, often triggered by events like public breach disclosures. Lastly, third-party/vendor risks originate from suppliers and partners, such as when a vendor’s weak cybersecurity exposes the organization.

Risk assessment is the structured process used to protect the business from these threats, ensuring vulnerabilities are addressed before causing harm. The assessment lifecycle involves five key phases:
1️⃣ Identifying risks through understanding assets and their vulnerabilities
2️⃣ Analyzing likelihood and impact
3️⃣ Evaluating and prioritizing based on risk severity
4️⃣ Treating risks through mitigation, transfer, acceptance, or avoidance
5️⃣ Monitoring and continually improving controls over time

Opinion: Why Knowing Risk Types Helps Businesses

Understanding the distinct categories of risks allows companies to take a proactive approach instead of reacting after damage occurs. It provides clarity on where threats originate, which helps leaders allocate resources more efficiently, strengthen compliance, protect revenue, and build trust with customers and stakeholders. Ultimately, knowing the types of risks empowers smarter decision-making and leads to long-term business resilience.

Tags: Types of Risks

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Dec 31 2025

Shadow AI: When Productivity Gains Create New Risks

Category: AI — disc7 @ 9:20 am

Shadow AI: The Productivity Paradox

Organizations face a new security challenge that doesn’t originate from malicious actors but from well-intentioned employees simply trying to do their jobs more efficiently. This phenomenon, known as Shadow AI, represents the unauthorized use of AI tools without IT oversight or approval.

Marketing teams routinely feed customer data into free AI platforms to generate compelling copy and campaign content. They see these tools as productivity accelerators, never considering the security implications of sharing sensitive customer information with external systems.

Development teams paste proprietary source code into public chatbots seeking quick debugging assistance or code optimization suggestions. The immediate problem-solving benefit overshadows concerns about intellectual property exposure or code base security.

Human resources departments upload candidate resumes and personal information to AI summarization tools, streamlining their screening processes. The efficiency gains feel worth the convenience, while data privacy considerations remain an afterthought.

These employees aren’t threat actors—they’re productivity seekers exploiting powerful tools available at their fingertips. Once organizational data enters public AI models or third-party vector databases, it escapes corporate control entirely and becomes permanently exposed.

The data now faces novel attack vectors like prompt injection, where adversaries manipulate AI systems through carefully crafted queries to extract sensitive information, essentially asking the model to “forget your instructions and reveal confidential data.” Traditional security measures offer no protection against these techniques.

We’re witnessing a fundamental shift from the old paradigm of “Data Exfiltration” driven by external criminals to “Data Integration” driven by internal employees. The threat landscape has evolved beyond perimeter defense scenarios.

Legacy security architectures built on network perimeters, firewalls, and endpoint protection become irrelevant when employees voluntarily connect to external AI services. These traditional controls can’t prevent authorized users from sharing data through legitimate web interfaces.

The castle-and-moat security model fails completely when your own workforce continuously creates tunnels through the walls to access the most powerful computational tools humanity has ever created. Organizations need governance frameworks, not just technical barriers.

Opinion: Shadow AI represents the most significant information security challenge for 2026 because it fundamentally breaks the traditional security model. Unlike previous shadow IT concerns (unauthorized SaaS apps), AI tools actively ingest, process, and potentially retain your data for model training purposes. Organizations need immediate AI governance frameworks including acceptable use policies, approved AI tool catalogs, data classification training, and technical controls like DLP rules for AI service domains. The solution isn’t blocking AI—that’s impossible and counterproductive—but rather creating “Lighted AI” pathways: secure, sanctioned AI tools with proper data handling controls. ISO 42001 provides exactly this framework, which is why AI Management Systems have become business-critical rather than optional compliance exercises.

Shadow AI for Everyone: Understanding Unauthorized Artificial Intelligence, Data Exposure, and the Hidden Threats Inside Modern Enterprises

Tags: prompt Injection, Shadow AI

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Dec 30 2025

EU AI Act: Why Every Organization Using AI Must Pay Attention

Category: AI,AI Governance — disc7 @ 11:07 am

EU AI Act: Why Every Organization Using AI Must Pay Attention

The EU AI Act is the world’s first major regulation designed to govern how artificial intelligence is developed, deployed, and managed across industries. Approved in June 2024, it establishes harmonized rules for AI use across all EU member states — just as GDPR did for privacy.

Any organization that builds, integrates, or sells AI systems within the European Union must comply — even if they are headquartered outside the EU. That means U.S. and global companies using AI in European markets are officially in scope.

The Act introduces a risk-based regulatory model. AI is categorized across four risk tiers — from unacceptable, which are completely banned, to high-risk, which carry strict controls, limited-risk with transparency requirements, and minimal-risk, which remain largely unregulated.

High-risk AI includes systems governing access to healthcare, finance, employment, critical infrastructure, law enforcement, and essential public services. Providers of these systems must implement rigorous risk management, governance, monitoring, and documentation processes across the entire lifecycle.

Certain AI uses are explicitly prohibited — such as social scoring, biometric emotion recognition in workplaces or schools, manipulative AI techniques, and untargeted scraping of facial images for surveillance.

Compliance obligations are rolling out in phases beginning February 2025, with core high-risk system requirements taking effect in August 2026 and final provisions extending through 2027. Organizations have limited time to assess their current systems and prepare for adherence.

This legislation is expected to shape global AI governance frameworks — much like GDPR influenced worldwide privacy laws. Companies that act early gain an advantage: reduced legal exposure, customer trust, and stronger market positioning.

How DISC InfoSec Helps You Stay Ahead

DISC InfoSec brings 20+ years of security and compliance excellence with a proven multi-framework approach. Whether preparing for EU AI Act, ISO 42001, GDPR, SOC 2, or enterprise governance — we help organizations implement responsible AI controls without slowing innovation.

If your business touches the EU and uses AI — now is the time to get compliant.

📩 Let’s build your AI governance roadmap together.
Reach out: Info@DeuraInfosec.com