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!
The fourteen vulnerability domains outlined in the OWASP Secure Coding Practices checklist collectively address the most common and dangerous weaknesses found in modern applications. They begin with Input Validation, which emphasizes rejecting malformed, unexpected, or malicious data before it enters the system by enforcing strict type, length, range, encoding, and whitelist controls. Closely related is Output Encoding, is a security technique that converts untrusted user input into a safe format before it is rendered by a browser, preventing malicious scripts from executing, which ensures that any data leaving the system—especially untrusted input—is properly encoded and sanitized based on context (HTML, SQL, OS commands, etc.) to prevent injection and cross-site scripting attacks. Authentication and Password Management focuses on enforcing strong identity verification, secure credential storage using salted hashes, robust password policies, secure reset mechanisms, protection against brute-force attacks, and the use of multi-factor authentication for sensitive accounts. Session Management strengthens how authenticated sessions are created, maintained, rotated, and terminated, ensuring secure cookie attributes, timeout controls, CSRF protections, and prevention of session hijacking or fixation.
Access Control ensures that authorization checks are consistently enforced across all requests, applying least privilege, segregating privileged logic, restricting direct object references, and documenting access policies to prevent horizontal and vertical privilege escalation. Cryptographic Practices govern how encryption and key management are implemented, requiring trusted execution environments, secure random number generation, protection of master secrets, compliance with standards, and defined key lifecycle processes. Error Handling and Logging prevents sensitive information leakage through verbose errors while ensuring centralized, tamper-resistant logging of security-relevant events such as authentication failures, access violations, and cryptographic errors to enable monitoring and incident response. Data Protection enforces encryption of sensitive data at rest, safeguards cached and temporary files, removes sensitive artifacts from production code, prevents insecure client-side storage, and supports secure data disposal when no longer required.
Communication Security protects data in transit by mandating TLS for all sensitive communications, validating certificates, preventing insecure fallback, enforcing consistent TLS configurations, and filtering sensitive data from headers. System Configuration reduces the attack surface by keeping components patched, disabling unnecessary services and HTTP methods, minimizing privileges, suppressing server information leakage, and ensuring secure default behavior. Database Security focuses on protecting data stores through secure queries, restricted privileges, parameterized statements, and protection against injection and unauthorized access. File Management addresses safe file uploads, storage, naming, permissions, and validation to prevent path traversal, malicious file execution, and unauthorized access. Memory Management emphasizes preventing buffer overflows, memory leaks, and improper memory handling that could lead to exploitation, especially in lower-level languages. Finally, General Coding Practices reinforce secure design principles such as defensive programming, code reviews, adherence to standards, minimizing complexity, and integrating security throughout the software development lifecycle.
My perspective: What stands out is that these fourteen areas are not isolated technical controls—they form an interconnected security architecture. Most major breaches trace back to failures in just a few of these domains: weak input validation, broken access control, poor credential handling, or misconfiguration. Organizations often overinvest in perimeter defenses while underinvesting in secure coding discipline. In reality, secure coding is risk management at the source. If development teams operationalize these fourteen domains as mandatory engineering guardrails—not optional best practices—they dramatically reduce exploitability, compliance exposure, and incident response costs. Secure coding is no longer a developer concern alone; it is a governance and leadership responsibility.
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.
From Encryption to Evolution: Leading with Cryptographic Agility
Relying on an simple “encrypt and forget” approach is no longer a sustainable long-term security strategy. Modern organizations, especially in highly regulated sectors, must recognize that encryption is not a one-time control but an ongoing lifecycle commitment. As threat landscapes evolve and computing power increases, encryption methods that are strong today may become vulnerable tomorrow, requiring continuous reassessment and adaptation.
Financial institutions, in particular, are required to retain highly sensitive customer and transaction data for decades due to regulatory, legal, and operational obligations. This extended data lifespan creates a mismatch with the effective lifespan of many cryptographic algorithms. What is considered secure at the time of encryption may not remain secure over the full retention period, exposing long-stored data to future decryption risks.
For this reason, designing systems with cryptographic agility — the ability to quickly replace or upgrade cryptographic algorithms and keys — has become a strategic leadership responsibility. It is no longer a distant technical concern reserved for specialists. Executives and security leaders must prioritize architectures that support seamless cryptographic transitions, ensuring long-term resilience and regulatory readiness.
My perspective: Organizations that treat cryptography as a dynamic capability rather than a static control will be better positioned to manage emerging risks, including advances in quantum computing and new attack techniques. Cryptographic agility should be embedded into governance, architecture, and investment decisions today. Leaders who proactively plan for algorithm evolution are not just improving security — they are protecting long-term trust, compliance, and business continuity.
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.
Summary of the key points from the Joint Statement on AI-Generated Imagery and the Protection of Privacy published on 23 February 2026 by the Global Privacy Assembly’s International Enforcement Cooperation Working Group (IEWG) — coordinated by data protection authorities including the UK’s Information Commissioner’s Office (ICO):
📌 What the Statement is: Data protection regulators from 61 jurisdictions around the world issued a coordinated statement raising serious concerns about AI systems that generate realistic images and videos of identifiable individuals without their consent. This includes content that can be intimate, defamatory, or otherwise harmful.
📌 Core Concerns: The authorities emphasize that while AI can bring benefits, current developments — especially image and video generation integrated into widely accessible platforms — have enabled misuse that poses significant risks to privacy, dignity, safety, and especially the welfare of children and other vulnerable groups.
📌 Expectations and Principles for Organisations: Signatories outlined a set of fundamental principles that must guide the development and use of AI content generation systems:
Implement robust safeguards to prevent misuse of personal information and avoid creation of harmful, non-consensual content.
Ensure meaningful transparency about system capabilities, safeguards, appropriate use, and risks.
Provide mechanisms for individuals to request removal of harmful content and respond swiftly.
Address specific risks to children and vulnerable people with enhanced protections and clear communication.
📌 Why It Matters: By coordinating a global position, regulators are signaling that companies developing or deploying generative AI imagery tools must proactively meet privacy and data protection laws — and that creating identifiable harmful content without consent can already constitute criminal offences in many jurisdictions.
How the Feb 23, 2026 Joint Statement by data protection regulators on AI-generated imagery — including the one from the UK Information Commissioner’s Office — will affect the future of AI governance globally:
🔎 What the Statement Says (Summary)
The joint statement — coordinated by the Global Privacy Assembly’s International Enforcement Cooperation Working Group (IEWG) and signed by 61 data protection and privacy authorities worldwide — focuses on serious concerns about AI systems that can generate realistic images/videos of real people without their knowledge or consent.
Key principles for organisations developing or deploying AI content-generation systems include:
Implement robust safeguards to prevent misuse of personal data and harmful image creation.
Ensure transparency about system capabilities, risks, and guardrails.
Provide effective removal mechanisms for harmful content involving identifiable individuals.
Address specific risks to children and vulnerable groups with enhanced protections.
The statement also emphasizes legal compliance with existing privacy and data protection laws and notes that generating non-consensual intimate imagery can be a criminal offence in many places.
🧭 How This Will Shape AI Governance
1. 📈 Raising the Bar on Responsible AI Development
This statement signals a shift from voluntary guidelines to expectations that privacy and human-rights protections must be embedded early in development lifecycles.
Privacy-by-design will no longer be just a GDPR buzzword – regulators expect demonstrable safeguards from the outset.
Systems must be transparent about their risks and limitations.
Organisations failing to do so are more likely to attract enforcement attention, especially where harms affect children or vulnerable groups. (EDPB)
This creates a global baseline of expectations even where laws differ — a powerful signal to tech companies and AI developers.
2. 🛡️ Stronger Enforcement and Coordination Between Regulators
Because 61 authorities co-signed the statement and pledged to share information on enforcement approaches, we should expect:
More coordinated investigations and inquiries, particularly against major platforms that host or enable AI image generation.
Cross-border enforcement actions, especially where harmful content is widely distributed.
Regulators referencing each other’s decisions when assessing compliance with privacy and data protection law. (EDPB)
This cooperation could make compliance more uniform globally, reducing “regulatory arbitrage” where companies try to escape strict rules by operating in lax jurisdictions.
3. ⚖️ Clarifying Legal Risks for Harmful AI Outputs
Two implications for AI governance and compliance:
Non-consensual image creation may be treated as criminal or civil harm in many places — not just a policy issue. Regulators explicitly said it can already be a crime in many jurisdictions.
Organisations may face tougher liability and accountability obligations when identifiable individuals are involved — particularly where children are depicted.
This adds legal pressure on AI developers and platforms to ensure their systems don’t facilitate defamation, harassment, or exploitation.
4. 🤝 Encouraging Proactive Engagement Between Industry and Regulators
The statement encourages organisations to engage proactively with regulators, not reactively:
Early risk assessments
Regular compliance outreach
Open dialogue on mitigations
This marks a shift from regulators policing after harm to requiring proactive risk governance — a trend increasingly reflected in broader AI regulation such as the EU AI Act. (mlex.com)
5. 🌐 Contributing to Emerging Global Norms
Even without a single binding law or treaty, this statement helps build international norms for AI governance:
Shared principles help align diverse legal frameworks (e.g., GDPR, local privacy laws, soon the EU AI Act).
Sets the stage for future binding rules or standards in areas like content provenance, watermarking, and transparency.
Helps civil society and industry advocate for consistent global risk standards for AI content generation.
📌 Bottom Line
This joint statement is more than a warning — it’s a governance pivot point. It signals that:
✅ Privacy and data protection are now core governance criteria for generative AI — not nice-to-have. ✅ Regulators globally are ready to coordinate enforcement. ✅ Companies that build or deploy AI systems will increasingly be held accountable for the real-world harms their outputs can cause.
In short, the statement helps shift AI governance from frameworks and principles toward operational compliance and enforceable expectations.
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.
AI-Enhanced Methodology That Scales Human Expertise
For DISC InfoSec, Burp AI hasn’t redefined what excellent penetration testing looks like — it has accelerated the path to achieving it. The objective was never to replace skilled professionals, but to eliminate repetitive, time-consuming tasks that slow them down. By reducing friction, testers can dedicate more time to solving complex, high-impact security challenges.
Instead of positioning AI as a substitute for human judgment, DISC InfoSec leverages Burp AI as an intelligent assistant — a “thinking partner” that augments expertise. This approach enables junior consultants to ramp up faster, supports senior testers with deeper analysis, and maintains the craftsmanship that defines high-quality pentesting engagements.
The result is a scalable, expertise-driven model: stronger collaboration, improved efficiency, and greater value delivered to clients. AI expands capacity without compromising rigor, allowing teams to focus on meaningful vulnerabilities rather than administrative overhead.
My perspective on Burp AI: When used responsibly, tools like Burp AI can significantly elevate penetration testing programs. The key is governance and methodology. AI should enhance structured testing processes — not shortcut them. If organizations treat AI as augmentation rather than automation, they gain speed and analytical depth while preserving accountability. In the right hands, Burp AI isn’t a replacement for skill — it’s a force multiplier.
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.
Major ISO/IEC Standards in AI Compliance — Summary & Significance
1. ISO/IEC 42001:2023 — AI Management System (AIMS) This standard defines the requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System. It focuses on organizational governance, accountability, and structured oversight of AI lifecycle activities. Its significance lies in providing a formal management framework that embeds responsible AI practices into daily operations, enabling organizations to systematically manage risks, document decisions, and demonstrate compliance to regulators and stakeholders.
2. ISO/IEC 23894:2023 — AI Risk Management This standard offers guidance for identifying, assessing, and monitoring risks associated with AI systems across their lifecycle. It promotes a risk-based approach aligned with enterprise risk management. Its importance in AI compliance is that it helps organizations proactively detect technical, operational, and ethical risks, ensuring structured mitigation strategies that reduce unexpected failures and compliance gaps.
3. ISO/IEC 38507:2022 — Governance of AI This framework provides principles for boards and executive leadership to oversee AI responsibly. It emphasizes strategic alignment, accountability, and ethical decision-making. Its compliance value comes from strengthening executive oversight, ensuring AI initiatives align with organizational values, regulatory expectations, and long-term strategy.
4. ISO/IEC 22989:2022 — AI Concepts & Architecture This standard establishes shared terminology and reference architectures for AI systems. It ensures stakeholders use consistent language and system classifications. Its significance lies in reducing ambiguity in policy, governance, and compliance discussions, which improves collaboration between legal, technical, and business teams.
5. ISO/IEC 23053:2022 — Machine Learning System Framework This framework describes the structure and lifecycle of ML-based AI systems, including system components and data-model interactions. It is significant because it guides organizations in designing AI systems with traceability and control, supporting auditability and lifecycle governance required for compliance.
6. ISO/IEC 5259 — Data Quality for AI This series focuses on dataset governance, quality metrics, and bias-aware controls. It emphasizes the integrity and reliability of training and operational data. Its compliance relevance is critical, as poor data quality directly affects fairness, performance, and legal defensibility of AI outcomes.
7. ISO/IEC TR 24027:2021 — Bias in AI This technical report explains sources of bias in AI systems and outlines mitigation and measurement techniques. It is significant for compliance because it supports fairness and non-discrimination objectives, helping organizations implement defensible controls against biased outcomes.
8. ISO/IEC TR 24028:2020 — Trustworthiness in AI This report defines key attributes of trustworthy AI, including robustness, transparency, and reliability. Its role in compliance is to provide practical benchmarks for evaluating system dependability and stakeholder trust.
9. ISO/IEC TR 24368:2022 — Ethical & Societal Concerns This guidance examines the broader human and societal impacts of AI deployment. It encourages responsible implementation that considers social risk and ethical implications. Its significance is in aligning AI programs with public expectations and emerging regulatory ethics requirements.
Overview: How ISO Standards Build AIMS and Reduce AI Risk
Major ISO/IEC standards form an integrated ecosystem that supports organizations in building a robust Artificial Intelligence Management System (AIMS) and achieving effective AI compliance. ISO/IEC 42001 serves as the structural backbone by defining management system requirements that embed governance, accountability, and continuous improvement into AI operations. ISO/IEC 23894 complements this by providing a structured risk management methodology tailored to AI, ensuring risks are systematically identified and mitigated.
Supporting standards strengthen specific pillars of AI governance. ISO/IEC 27001 and ISO/IEC 27701 reinforce data security and privacy protection, safeguarding sensitive information used in AI systems. ISO/IEC 22989 establishes shared terminology that reduces ambiguity across teams, while ISO/IEC 23053 and the ISO/IEC 5259 series enhance lifecycle management and data quality controls. Technical reports addressing bias, trustworthiness, and ethical concerns further ensure that AI systems operate responsibly and transparently.
Together, these standards create a comprehensive compliance architecture that improves accountability, supports regulatory readiness, and minimizes operational and ethical risks. By integrating governance, risk management, security, and quality assurance into a unified framework, organizations can deploy AI with greater confidence and resilience.
My Perspective
ISO’s AI standards represent a shift from ad-hoc AI experimentation toward disciplined, auditable AI governance. What makes this ecosystem powerful is not any single standard, but how they interlock: management systems provide structure, risk frameworks guide decision-making, and ethical and technical standards shape implementation. Organizations that adopt this integrated approach are better positioned to scale AI responsibly while maintaining stakeholder trust. In practice, the biggest value comes when these standards are operationalized — embedded into workflows, metrics, and leadership oversight — rather than treated as checkbox compliance.
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.
ISO certification is a structured process organizations follow to demonstrate that their management systems meet internationally recognized standards such as International Organization for Standardization frameworks like ISO 27001 or ISO 27701. The journey typically begins with understanding the standard’s requirements, defining the scope of certification, and aligning internal practices with those requirements. Organizations document their controls, implement processes, train staff, and conduct internal reviews before engaging an certification body for an external audit. The goal is not just to pass an audit, but to build a repeatable, risk-driven management system that improves security, privacy, and operational discipline over time.
Gap assessment & scoring is the diagnostic phase where the organization’s current practices are compared against the selected ISO standard. Each requirement of the standard is reviewed to identify missing controls, weak processes, or incomplete documentation. The “scoring” aspect prioritizes gaps by severity and business impact, helping leadership understand where the biggest risks and compliance shortfalls exist. This structured baseline gives a clear roadmap, timeline, and resource estimate for achieving certification, turning a complex standard into an actionable improvement plan.
Risk assessment & control selection focuses on identifying threats to the organization’s information assets and evaluating their likelihood and impact. Based on this analysis, appropriate security and privacy controls are selected to reduce risks to acceptable levels. Rather than blindly implementing every possible control, the organization applies a risk-based approach to choose measures that are proportional, cost-effective, and aligned with business objectives. This ensures the certification effort strengthens real security posture instead of becoming a checkbox exercise.
Policy and process definition translates ISO requirements and chosen controls into formal governance documents and operational workflows. Policies set management intent and direction, while processes define how daily activities are performed, monitored, and improved. Clear documentation creates consistency, accountability, and auditability across teams. It also ensures that responsibilities are well defined and that employees understand how their roles contribute to compliance and risk management.
Implementation support and internal audit is the execution and validation stage. Organizations deploy the defined controls, integrate them into everyday operations, and provide training to staff. Internal audits are then conducted to independently verify that processes are being followed and that controls are effective. Findings from these audits drive corrective actions and continuous improvement, helping the organization resolve issues before the external certification audit.
Pre-certification readiness review is a final mock audit that simulates the certification body’s assessment. It checks documentation completeness, evidence of control operation, and overall system maturity. Any remaining weaknesses are addressed quickly, reducing the risk of surprises during the official audit. This step increases confidence that the organization is fully prepared to demonstrate compliance.
Perspective: The ISO certification process is most valuable when treated as a long-term governance framework rather than a one-time project. Organizations that focus on embedding risk management, accountability, and continuous improvement into their culture gain far more than a certificate—they build resilient systems that scale with the business. When done properly, certification becomes a catalyst for operational maturity, customer trust, and measurable risk reduction.
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.
“Balancing the Scales: What AI Teaches Us About the Future of Cyber Risk Governance”
1. The AI Opportunity and Challenge Artificial intelligence is rapidly transforming how organizations function and innovate, offering immense opportunity while also introducing significant uncertainty. Leaders increasingly face a central question: How can AI risks be governed without stifling innovation? This issue is a recurring theme in boardrooms and risk committees, especially as enterprises prepare for major industry events like the ISACA Conference North America 2026.
2. Rethinking AI Risk Through Established Lenses Instead of treating AI as an entirely unprecedented threat, the author suggests applying quantitative governance—a disciplined, measurement-focused approach previously used in other domains—to AI. Grounding our understanding of AI risks in familiar frameworks allows organizations to manage them as they would other complex, uncertain risk profiles.
3. Familiar Risk Categories in New Forms Though AI may seem novel, the harms it creates—like data poisoning, misleading outputs (hallucinations), and deepfakes—map onto traditional operational risk categories defined decades ago, such as fraud, disruptions to business operations, regulatory penalties, and damage to trust and reputation. This connection is important because it suggests existing governance doctrines can still serve us.
4. New Causes, Familiar Consequences Where AI differs is in why the risks happen. The article mentions a taxonomy of 13 AI-specific triggers—including things like model drift, lack of explainability, or robustness failures—that drive those familiar risk outcomes. By breaking down these root causes, risk leaders can shift from broad fear of AI to measurable scenarios that can be prioritized and governed.
5. Governance Structures Are Lagging AI is evolving faster than many governance systems can respond, meaning organizations risk falling behind if their oversight practices remain static. But the author argues that this lag isn’t an inevitability. By combining the discipline of operational risk management, rigorous model validation, and quantitative analysis, governance can be scalable and effective for AI systems.
6. Continuity Over Reinvention A key theme is continuity: AI doesn’t require entirely new governance frameworks but rather an extension of what already exists, adapted to account for AI’s unique behaviors. This reduces the need to reinvent the wheel and gives risk practitioners concrete starting points rooted in established practice.
7. Reinforcing the Role of Governance Ultimately, the article emphasizes that AI doesn’t diminish the need for strong governance—it amplifies it. Organizations that integrate traditional risk management methods with AI-specific insights can oversee AI responsibly without overly restricting its potential to drive innovation.
My Opinion
This article strikes a sensible balance between AI optimism and risk realism. Too often, AI is treated as either a magical solution that solves every problem or an existential threat requiring entirely new paradigms. Grounding AI risk in established governance frameworks is pragmatic and empowers most organizations to act now rather than wait for perfect AI-specific standards. The suggestion to incorporate quantitative risk approaches is especially useful—if done well, it makes AI oversight measurable and actionable rather than vague.
However, the reality is that AI’s rapid evolution may still outpace some traditional controls, especially in areas like explainability, bias, and autonomous decision-making. So while extending existing governance frameworks is a solid starting point, organizations should also invest in developing deeper AI fluency internally, including cross-functional teams that merge risk, data science, and ethical perspectives.
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.
“You can’t have a risk register without an approved GRC charter.”
I hear this statement often — and it’s a myth worth clarifying.
A risk register is an operational tool. Many organizations create and use one long before they formalize governance structures. Major frameworks from the National Institute of Standards and Technology (NIST) and the International Organization for Standardization (ISO) don’t require a GRC charter as a prerequisite to building a risk register.
However, here’s the nuance: a GRC charter gives the risk register authority. It defines ownership, executive sponsorship, and decision rights. Without governance backing, a risk register can exist — but it risks becoming a passive checklist instead of a strategic decision tool.
The practical takeaway: you can start managing risks immediately, but for long-term effectiveness, formal governance should follow quickly. Mature organizations align their risk registers with a clear charter to ensure accountability and impact.
Risk management is not about paperwork — it’s about enabling better decisions.
Here’s a policy-style version you can use for internal documentation:
Risk Register Governance Policy Statement
The organization maintains a risk register as a formal mechanism to identify, assess, document, and monitor enterprise risks. The existence of a risk register does not depend on the prior approval of a formal Governance, Risk, and Compliance (GRC) charter; however, effective risk management requires clear governance authority and executive sponsorship.
Consistent with guidance from the National Institute of Standards and Technology and standards published by the International Organization for Standardization, the organization recognizes that a risk register is an operational tool that supports decision-making at all levels. A formal GRC charter strengthens the effectiveness of the risk register by defining roles, responsibilities, and accountability for risk ownership and acceptance.
Where a GRC charter is not yet established, management may initiate and maintain a risk register to support ongoing risk management activities. The organization will work toward formalizing governance structures to ensure that risks documented in the register are reviewed, prioritized, and acted upon with appropriate authority.
The objective of this policy is to ensure that the risk register functions as an living management instrument that informs strategic and operational decisions, rather than as a static compliance artifact.
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.
Most people mix up LLMs, RAG, AI Agents, and Agentic AI because they all build on similar foundations, but they serve very different purposes. Choosing the wrong one can lead to overspending, unnecessary complexity, and solutions that don’t match real business needs. Here’s a clear, practical breakdown of how they differ in what they are, what they do best, and what they typically cost.
LLM (Large Language Model) An LLM is essentially a smart text engine — a raw AI “brain” that generates and interprets language based on patterns learned during training. It doesn’t have built-in long-term memory or native tool use. Its primary functionality is predicting and generating text, which makes it strong at drafting emails, writing stories, summarizing information, and answering quick questions. LLMs are best suited for one-off Q&A and content creation tasks. From a cost perspective, they are the cheapest option because you mainly pay per interaction. They’re lightweight, fast, and ideal when you just need intelligent text generation without external data integration.
RAG (Retrieval-Augmented Generation) RAG combines an LLM with a retrieval system that searches your own documents or databases before answering. Instead of guessing from training alone, it pulls relevant information from real files and uses that to produce factual responses. Its primary functionality is grounding answers in up-to-date, organization-specific knowledge, reducing hallucinations. RAG is commonly used for customer support bots, internal knowledge bases, and research assistance. The cost is typically medium: you pay for the AI model plus storage and retrieval infrastructure. It’s a practical step up from a plain LLM when accuracy and company-specific context matter.
AI Agent An AI Agent extends an LLM with the ability to plan actions and use tools. It can break down goals, call APIs, run code, search the web, and complete multi-step tasks with some autonomy. Its primary functionality is task execution and workflow automation rather than just conversation. AI Agents are useful for research projects, organizing data, and automating repetitive processes. They tend to be higher cost because they use multiple tools, take longer to run, and require more compute and orchestration. You’re paying for capability and autonomy, not just text generation.
Agentic AI Agentic AI represents coordinated systems of multiple AI agents working together like a team. These agents collaborate, delegate responsibilities, and manage complex objectives across large workflows. Its primary functionality is orchestrating end-to-end processes where different specialized agents share information and coordinate actions. This approach is best suited for enterprise-level automation, large marketing or operational campaigns, and complex business processes. It carries the highest cost because it runs multiple models simultaneously and requires significant infrastructure. It’s powerful but often overkill for simpler needs.
The key takeaway is to start simple and scale only when complexity is justified. Many organizations benefit most from RAG — a focused, cost-effective way to make AI useful with their own data. Jumping straight to agentic systems can add expense and engineering overhead without proportional value. Matching the technology to the problem ensures faster delivery, lower cost, and solutions that actually serve business goals.
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.
1. Translate business priorities into security outcomes
A CISO’s first responsibility is to convert business goals into concrete security protections. This means understanding what assets are mission-critical and identifying scenarios that could seriously damage revenue, operations, safety, or regulatory standing. Security becomes a business enabler rather than a technical afterthought.
Priority tasks include identifying crown-jewel assets, mapping them to business processes, and modeling high-impact loss scenarios. The CISO should then align controls and investments directly with business objectives—protecting uptime, customer trust, and compliance exposure. Regular executive discussions ensure security strategy evolves with business priorities.
2. Establish governance and clear risk ownership
Effective governance ensures that cybersecurity risk is shared and owned across the organization, not isolated within IT. The CISO builds a structure where executives understand and accept accountability for risks tied to their domains.
Key priorities are defining risk ownership across departments, creating formal decision forums where risk and investment are reviewed, and embedding cybersecurity into enterprise governance processes. Clear escalation paths and accountability frameworks help transform security from advisory guidance into organizational action.
3. Build an actionable risk register
An actionable risk register turns abstract threats into prioritized, manageable work. It allows leadership to see which risks matter most and what actions will reduce them.
The CISO should prioritize evaluating risks based on likelihood and business impact, ranking them transparently, and linking each item to a funded remediation roadmap. The focus is on measurable risk reduction rather than isolated projects, ensuring investments produce visible resilience gains.
4. Own identity and access as the control plane
Identity and access management acts as the organization’s primary defensive layer. By controlling who can access what, the CISO limits the damage of inevitable breaches.
Priority actions include enforcing multi-factor authentication, implementing least-privilege access, and maintaining disciplined joiner-mover-leaver processes. Continuous access reviews and lifecycle automation reduce attack surfaces and shrink the blast radius of compromised accounts.
5. Operationalize third-party risk
Third-party relationships extend the organization’s attack surface. The CISO must treat vendor risk as an ongoing operational function, not a one-time assessment.
Critical tasks include tiering vendors by risk level, embedding security requirements into contracts, and establishing onboarding and offboarding controls. Continuous monitoring and reassessment ensure vendor security posture keeps pace with changing threats and business dependencies.
6. Run incident response like a business capability
Incident response should function as a rehearsed organizational capability rather than an ad hoc reaction. It protects operational continuity and reputation.
The CISO prioritizes defining clear roles, developing tested playbooks, and conducting tabletop exercises with executive leadership. Structured escalation and communication processes enable faster containment, minimize business disruption, and accelerate recovery.
7. Report metrics that leadership can act on
Security metrics must inform decisions, not just decorate dashboards. The CISO translates operational data into insights leadership can use.
Priority work includes tracking actionable indicators such as detection and containment times, patch cycles, control coverage, and vendor exposure. Reporting should demonstrate trends and measurable improvements in security posture, supporting informed investment and governance decisions.
8. Build a team and partner ecosystem that executes
A strong execution engine requires skilled people and effective partnerships. The CISO creates an operating model that turns strategy into results.
Key priorities are defining clear roles and responsibilities, strengthening engineering and operational capabilities, and selecting tools that demonstrably improve detection and response. External partners and platforms should complement internal strengths and scale execution.
Perspective: A modern CISO’s value lies in building a system where security is embedded in business decision-making. When the role is reduced to technical firefighting, organizations lose strategic leverage. A high-impact CISO establishes governance, accountability, and measurable outcomes—transforming security from reactive theater into proactive business resilience.
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.
Artificial intelligence is reshaping cybersecurity by shifting defenses from reactive protection to proactive and adaptive resilience. Instead of only responding after an breach occurs, AI enables organizations to continuously monitor systems, detect emerging threats, and respond in real time. By combining advanced analytics with machine learning, AI strengthens every layer of cybersecurity—from threat detection to fraud prevention—creating a more intelligent and responsive security posture.
AI-Based Threat Detection
AI-powered threat detection focuses on real-time monitoring and early identification of suspicious behavior. Using predictive analytics, pattern recognition, and behavioral anomaly detection, AI systems learn what “normal” activity looks like and quickly flag deviations. This allows security teams to catch threats that traditional rule-based tools might miss. In my view, AI significantly improves this category by reducing detection time and helping organizations move from reactive incident response to continuous, intelligent threat hunting.
Malware Analysis
In malware analysis, AI uses deep learning and automated sandboxing to examine suspicious files and behaviors without relying solely on known signatures. This enables the identification of previously unseen or zero-day threats. By analyzing how software behaves rather than just matching patterns, AI can uncover sophisticated attacks faster. I see AI as a force multiplier here—it accelerates analysis, reduces manual workload, and improves the ability to defend against rapidly evolving malware.
Intrusion Detection Systems (IDS) and Fraud Detection
AI enhances intrusion detection systems by applying machine learning to network security monitoring. These systems identify unusual traffic patterns and suspicious activities that may indicate an intrusion. Similarly, in fraud detection—especially in financial transactions—AI evaluates transaction behavior, risk scores, and user authentication signals to detect anomalies. From my perspective, AI’s strength in this area lies in its ability to process massive volumes of data and uncover subtle patterns, making defenses more scalable and precise.
Machine Learning Models and Core Concepts
At the core of AI in cybersecurity are machine learning approaches such as supervised learning, unsupervised learning, and reinforcement learning. Supervised learning uses labeled data for classification and prediction tasks, unsupervised learning discovers hidden structures and clusters in unlabeled data, and reinforcement learning improves decisions through trial and feedback. Together, these methods form the technical backbone that enables adaptive and intelligent security systems. I believe understanding these models is essential, as they drive the innovation that allows cybersecurity tools to evolve alongside emerging threats.
Overall, AI acts as a proactive and adaptive shield for modern cybersecurity. By improving detection accuracy, accelerating response times, and enabling continuous learning, AI helps organizations stay ahead of increasingly complex threats and maintain a stronger security posture.
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.
When AI systems are connected to internal databases or proprietary intellectual property, they effectively become another privileged user in your environment. If this access is not tightly scoped and continuously monitored, sensitive information can be unintentionally exposed, copied, or misused. A proper diagnostic question is: Do we clearly know what data each AI system can see, and is that access minimized to only what is necessary? Data exposure through AI is often silent and cumulative, making early control essential.
AI systems that can execute actions
AI-driven workflows that trigger operational or financial actions—such as approving transactions, modifying configurations, or initiating automated processes—introduce execution risk. Errors, prompt manipulation, or unexpected model behavior can directly impact business operations. Organizations should treat these systems like automated decision engines and require guardrails, approval thresholds, and rollback mechanisms. The key issue is not just what AI recommends, but what it is allowed to do autonomously.
Overprivileged service accounts
Service accounts connected to AI platforms frequently inherit broad permissions for convenience. Over time, these accounts accumulate access that exceeds their intended purpose. This creates a high-value attack surface: if compromised, they can be used to pivot across systems. A mature posture requires least-privilege design, periodic permission reviews, and segmentation of AI-related credentials from core infrastructure.
Insufficiently isolated AI logging
When AI logs are mixed with general system logging, it becomes difficult to trace model behavior, investigate incidents, or audit decisions. AI systems generate unique telemetry—inputs, prompts, outputs, and decision paths—that require dedicated visibility. Without separated and structured logging, organizations lose the ability to reconstruct events and detect misuse patterns. Clear audit trails are foundational for both security and accountability.
Lack of centralized AI inventory
If there is no centralized inventory of AI tools, integrations, and models in use, governance becomes reactive instead of intentional. Shadow AI adoption spreads quickly across departments, creating blind spots in risk management. A centralized registry helps organizations understand where AI exists, what it does, who owns it, and how it connects to critical systems. You cannot manage or secure what you cannot see.
Weak third-party AI vendor assessment
AI vendors often process sensitive data or embed deeply into workflows, yet many organizations evaluate them using standard vendor checklists that miss AI-specific risks. Enhanced third-party reviews should examine model transparency, data handling practices, security controls, and long-term dependency risks. Without this scrutiny, external AI services can quietly expand your attack surface and compliance exposure.
Missing human oversight for high-impact outputs
When high-impact AI outputs—such as legal decisions, financial approvals, or customer-facing actions—are not subject to human validation, the organization assumes algorithmic risk without a safety net. Human-in-the-loop controls act as a checkpoint against model errors, bias, or unexpected behavior. The diagnostic question is simple: Where do we deliberately require human judgment before consequences become irreversible?
Perspective
This readiness assessment highlights a central truth: AI exposure is less about exotic threats and more about governance discipline. Most risks arise from familiar issues—access control, visibility, vendor management, and accountability—amplified by the speed and scale of AI adoption. Visibility is indeed the first layer of control. When organizations lack a clear architectural view of how AI interacts with their systems, decisions are driven by assumptions and convenience rather than intentional design.
In my view, the organizations that succeed with AI will treat it as a core infrastructure layer, not an experimental add-on. They will build inventories, enforce least privilege, require auditable logging, and embed human oversight where impact is high. This doesn’t slow innovation; it stabilizes it. Strong governance creates the confidence to scale AI responsibly, turning potential exposure into managed capability rather than unmanaged risk.
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.
Overview of the Top 10 AI Governance Best Practices from the Lumenova AI article:
1. Build Cross-Functional AI Governance Committees
AI risk isn’t isolated to one department — it spans legal, security, data science, and business operations. Establishing a multi-disciplinary governance body ensures that decisions consider diverse perspectives and risks, rather than leaving oversight to only technology or compliance teams. This committee should have authority to review and, if needed, block AI deployments that don’t meet governance standards.
2. Standardize AI Use Case Approval and Risk Classification
Shadow AI — unvetted tools and projects — is one of the biggest governance threats. A structured intake and approval workflow helps organizations classify each AI use case by risk level (e.g., low, high) and routes them through appropriate oversight processes. This keeps innovation moving while preventing uncontrolled deployments.
3. Align Governance with Global Regulatory Standards
AI governance is no longer just internal policy; it must align with evolving laws like the EU AI Act and various U.S. state regulations. Mapping controls to the strictest standards creates a single compliance approach that covers multiple jurisdictions rather than maintaining separate regional frameworks.
4. Maintain a Centralized AI Inventory and Policy Repository
You can’t govern what you don’t see. A unified registry that tracks AI models, their datasets, lineage, versions, and associated policies becomes the “source of truth” for compliance and audit readiness. It also enables rapid impact analysis when governance needs change.
5. Embed Governance into Daily Workflows
Governance today isn’t about policies filed away in a binder — it must be integrated into how AI is developed, deployed, and monitored. Embedding controls into everyday workflows ensures oversight is continuous, not periodic, and matches the pace of how modern AI systems evolve.
6. Automate Compliance and Controls Where Possible
Relying on manual checks doesn’t scale. Automating policy enforcement, compliance validation, and risk monitoring helps organizations stay ahead of drift, bias, and other governance gaps — reducing both human error and operational bottlenecks.
7. Continuously Document Models and Decisions
Transparent documentation — covering training data sources, intended use cases, performance limits, and governance decisions — is key for audits, regulatory scrutiny, and internal accountability. It also supports explainability and trust with stakeholders.
8. Monitor AI Systems Post-Deployment
AI systems change over time — as input data shifts and usage patterns evolve — meaning ongoing monitoring is essential. This includes watching for bias, performance decay, security vulnerabilities, and other risks. Continuous oversight ensures systems stay aligned with standards and expectations.
9. Enforce Human Oversight Where Needed
For high-impact or high-risk AI, human oversight (e.g., human-in-the-loop checkpoints) ensures that critical decisions aren’t fully automated and that ethical judgment or context is retained. This practice balances automation with accountability.
10. Foster a Responsible AI Culture Through Training
Governance isn’t just about tools and policies — it’s also about people. Ongoing education and role-specific training help teams understand why governance matters, what their responsibilities are, and how to implement best practices effectively.
My Perspective
As AI adoption accelerates, governance is no longer optional — it’s foundational. Organizations that treat governance as a compliance checkbox inevitably fall behind; those that operationalize it — embedding controls into workflows, automating compliance, and building cross-functional oversight — gain real strategic advantage. Strong AI governance doesn’t slow innovation; it reduces risk, builds stakeholder trust, and enables AI to scale responsibly across the enterprise. By shifting from static policies to living governance practices, leaders protect their organizations while unlocking AI’s full value.
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.
Governance, Risk, and Compliance (GRC) — A Practical Summary
What GRC Means in Practice
A Governance, Risk, and Compliance (GRC) framework is a structured way to bring order, accountability, and consistency to how an organization manages decisions, risks, and regulatory obligations. Governance sets the direction by defining goals, leadership responsibilities, and policies so everyone understands their role and the company’s priorities. Risk management focuses on identifying threats—such as cyber incidents, operational failures, or legal exposure—and reducing their likelihood or impact. Compliance ensures the organization follows laws, standards, and internal rules. Together, these elements create an integrated system that improves oversight, reduces surprises, and builds trust with stakeholders.
GRC Success Metrics and Value
A mature GRC program improves risk visibility and decision-making by linking priorities to measurable value and protection outcomes. Organizations with effective GRC frameworks typically see stronger alignment between business goals and risk controls, better resource prioritization, and improved protection against operational and regulatory failures. By tracking key performance indicators (KPIs) and formulas—such as risk scoring (likelihood × impact) and control effectiveness—leaders can quantify how well the organization is managing uncertainty and compliance. This data-driven approach helps convert abstract risk into actionable insights.
Step-by-Step GRC Framework
Building a GRC framework follows a logical progression. It starts with establishing a governance structure and charter that defines authority and accountability. Next comes defining risk appetite—how much risk the organization is willing to accept. A policy framework is then developed to translate strategy into practical rules. Regulatory mapping ensures all legal and industry requirements are addressed. Risk identification and assessment help prioritize threats, followed by implementing appropriate controls. Continuous monitoring through key risk indicators (KRIs), reporting dashboards, and feedback loops supports ongoing improvement. The process is cyclical: document, monitor, and refine regularly to keep the framework relevant.
Core GRC Components
At the center of a GRC framework is an integrated system that connects governance, risk management, and compliance activities. Core components include strategy and governance oversight, risk assessment and management processes, compliance tracking, internal controls, and audit and assurance functions. Supporting artifacts—such as a GRC charter, risk register, policy library, control matrix, compliance tracker, and audit reports—provide the documentation backbone. Together, these components ensure that risks are systematically identified, controls are enforced, and compliance is continuously validated.
Essential Formulas, KPIs, and Documentation
Effective GRC relies on measurable indicators and structured documentation. Key formulas and KPIs evaluate performance, risk exposure, and control effectiveness, allowing leaders to monitor progress objectively. Essential document outputs—such as risk registers, policy libraries, and control matrices—create transparency and consistency. A clear approval workflow (draft → review → approval → implementation → monitoring → improvement) ensures accountability and continuous oversight. These mechanisms transform GRC from a theoretical model into an operational discipline.
Common GRC Mistakes
Many organizations struggle with GRC because of cultural and structural gaps. Weak leadership commitment, unclear risk appetite, inconsistent policy enforcement, and lack of continuous monitoring are common pitfalls. Without executive support and regular review, frameworks become paperwork exercises rather than living systems. Avoiding these mistakes requires strong tone at the top, simple and well-documented processes, and frequent reassessment.
Final Perspective
A well-designed GRC framework acts as a stabilizing force for an organization. It clarifies governance, reduces risk exposure, strengthens compliance posture, and supports sustainable performance. By keeping the framework simple, documented, and continuously reviewed, companies can transform GRC into a practical operating system that guides everyday decisions rather than a one-time compliance project.
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.
Security frameworks exist to reduce chaos in how organizations manage risk. Without a shared structure, every company invents its own way of “doing security,” which leads to inconsistent controls, unclear responsibilities, and hidden blind spots. This post illustrates how two major frameworks — National Institute of Standards and Technology’s Cybersecurity Framework (NIST CSF) and International Organization for Standardization’s ISO/IEC 27001 — approach this challenge from complementary angles. Together, they bring order to everyday security operations by defining both what to protect and how to manage protection over time.
The NIST CSF acts like a master technical architect. It provides a practical blueprint for implementing safeguards: identifying assets, protecting systems, detecting threats, responding to incidents, and recovering from disruptions. Its strength lies in being implementation-focused and highly actionable. Organizations use NIST to harden their environment, close technical gaps, and standardize best practices. By offering a common language and structured set of controls, NIST reduces operational confusion, aligns teams around clear priorities, and makes day-to-day risk management more predictable and measurable.
ISO/IEC 27001, on the other hand, focuses on governance and sustainability. Rather than concentrating on specific technical controls, it builds a management system — an Information Security Management System (ISMS) — that ensures security processes are repeatable, accountable, and continuously improved. It defines roles, policies, oversight mechanisms, and audit structures that keep security running as a disciplined business function. Certification under ISO 27001 signals assurance and trust to customers and stakeholders. In practical terms, ISO reduces chaos by embedding security into organizational routines, clarifying ownership, and ensuring that protections don’t fade over time.
When layered together, these frameworks create a powerful system. NIST provides the technical depth to design and operationalize safeguards, while ISO 27001 supplies the governance engine that sustains them. Mature organizations rarely treat this as an either-or decision. They use NIST to shape their technical security architecture and ISO 27001 to institutionalize it through management processes and external assurance. This layered approach addresses both technical risk and trust risk — the need to protect systems and the need to prove that protection is consistently maintained.
From my perspective, asking whether we need both frameworks is really a question about organizational maturity and goals. If a company is struggling with technical implementation, NIST offers immediate practical guidance. If it needs to demonstrate credibility and long-term governance, ISO 27001 becomes essential. In reality, most organizations benefit from combining them: NIST drives effective execution, and ISO ensures durability and trust. Together, they transform security from a reactive set of tasks into a structured, sustainable discipline that meaningfully reduces everyday operational chaos.
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.
Cybersecurity and cyber risk are closely related, but they operate with different priorities and lenses. Cybersecurity is primarily concerned with defending systems, networks, and data from threats. It focuses on identifying vulnerabilities, applying controls, and fixing technical weaknesses. The central question in cybersecurity is often, “How do we remediate this issue to make the system more secure?” It is action-oriented and technical, aiming to reduce exposure through engineering and operational safeguards.
Cyber risk, in contrast, shifts the conversation from technical fixes to business consequences. It asks, “If this system fails or is compromised, what does that mean for the organization?” This perspective evaluates the likelihood of an event and its potential impact on finances, operations, compliance, and reputation. Not every vulnerability translates into significant business risk, and some of the most serious risks may stem from strategic or process gaps rather than isolated technical flaws. Cyber risk management therefore emphasizes context, prioritization, and tradeoffs, helping leaders decide where to invest resources and which risks are acceptable.
From my perspective, the distinction between cyber risk and cybersecurity represents a maturation of the field. Cybersecurity is essential as the execution arm — it provides the tools and controls that protect assets. Cyber risk is the decision framework that ensures those efforts align with business objectives. Organizations that focus only on cybersecurity can become trapped in an cycle of chasing vulnerabilities without clear prioritization. Conversely, a cyber risk approach connects technical findings to measurable business outcomes, enabling informed decisions at the executive level. The strongest programs integrate both: cybersecurity delivers protection, while cyber risk guides strategy, investment, and governance so the organization can operate confidently amid uncertainty.
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.
Blockchain 101: Understanding the Basics Through a Visual
Think of cryptocurrency as a new kind of digital money that exists only on the internet and doesn’t rely on banks or governments to run it.
A good way to understand it is by starting with the most famous example: Bitcoin.
What is cryptocurrency?
Cryptocurrency is digital money secured by cryptography (advanced math used to protect information). Instead of a bank keeping track of who owns what, transactions are recorded on a public digital ledger called a blockchain.
You can imagine blockchain as a shared Google Sheet that thousands of computers around the world constantly verify and update. No single company controls it.
Key features:
💻 Digital only – no physical coins or bills
🌍 Decentralized – not controlled by one government or bank
🔒 Secure – protected by cryptography
📜 Transparent – transactions are recorded publicly
How does cryptocurrency work?
Most cryptocurrencies run on a blockchain network.
Here’s a simplified flow:
You create a wallet A crypto wallet is like a digital bank account. It has:
a public address (like your email you can share)
a private key (like your password — keep it secret)
You send a transaction When you send crypto, your wallet signs the transaction with your private key.
The network verifies it Thousands of computers (called nodes or miners/validators) check that:
you actually own the funds
you aren’t spending the same money twice
The transaction is added to the blockchain Once verified, it’s grouped with others into a “block” and permanently recorded.
After that, the transaction can’t easily be changed.
Benefits of cryptocurrency
1. Faster global payments
You can send money anywhere in the world in minutes, often cheaper than banks.
2. No middleman required
You don’t need a bank or payment company to approve transactions.
3. Financial access
Anyone with internet access can use crypto — helpful in places with weak banking systems.
4. Transparency and security
Transactions are public and hard to tamper with.
5. Programmable money
Some cryptocurrencies (like Ethereum) allow smart contracts — programs that automatically execute agreements.
Example: A simple crypto transaction
Let’s walk through a real-world style example.
Scenario: Alice wants to send $20 worth of Bitcoin to Bob for helping with a project.
Step-by-step:
Alice opens her wallet app and enters Bob’s public address.
She types in the amount and presses Send.
Her wallet signs the transaction with her private key.
The Bitcoin network checks that Alice has enough funds.
The transaction is added to the blockchain.
Bob sees the payment appear in his wallet.
Time: ~10 minutes (depending on network traffic) No bank involved.
It’s similar to handing someone cash — but done digitally and verified by a global network.
Simple analogy
Think of cryptocurrency like:
Email for money
Before email, sending letters took days and required postal systems. Crypto lets you send money across the internet as easily as sending an email.
Important things to know (balanced view)
While crypto has benefits, it also has challenges:
⚠️ Prices can be very volatile
🔐 If you lose your private key, you may lose your funds
🧾 Regulations are still evolving
🧠 It has a learning curve
let’s walk through the diagram step by step in plain language, like you would in a classroom.
This diagram is showing how a blockchain records a transaction (like sending money using Bitcoin).
Step 1: New transactions are created
On the left side, you see a list of new transactions (for example: Alice sends money to Bob).
Think of this as:
👉 People requesting to send digital money to each other.
At this stage, the transactions are waiting to be verified.
Step 2: Transactions are grouped into a block
In the next section, those transactions are packed into a block.
A block is like a container or page in a notebook that stores:
A list of transactions
A timestamp (when it happened)
A unique security code (called a hash)
This security code links the block to the previous block — like a chain link.
Step 3: The network of computers verifies the block
In the middle of the diagram, you see many connected computers.
These computers form a global network that checks:
Are the transactions valid?
Does the sender actually have the funds?
Is anyone trying to cheat?
If most computers agree the transactions are valid, the block is approved.
Think of it like a group of students checking each other’s math homework to make sure it’s correct.
Step 4: The block is added to the chain
Once approved, the block is attached to previous blocks, forming a chain of blocks — this is the blockchain.
Each new block connects to the one before it using cryptographic links.
This makes it very hard to change past records, because you would have to change every block after it.
Step 5: Permanent record stored everywhere
On the far right, the diagram shows a secure folder.
This represents the permanent record:
The transaction is now finalized
It’s copied and stored across thousands of computers
It cannot easily be altered
This is what makes blockchain secure and transparent.
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.
The OWASP Smart Contract Top 10 is an industry-standard awareness and guidance document for Web3 developers and security teams detailing the most critical classes of vulnerabilities in smart contracts. It’s based on real attacks and expert analysis and serves as both a checklist for secure design and an audit reference to help reduce risk before deployment.
🔍 The 2026 Smart Contract Top 10 (Rephrased & Explained)
SC01 – Access Control Vulnerabilities
What it is: Happens when a contract fails to restrict who can call sensitive functions (like minting, admin changes, pausing, or upgrades). Why it matters: Without proper permission checks, attackers can take over critical actions, change ownership, steal funds, or manipulate state. Mitigation: Use well-tested access control libraries (e.g., Ownable, RBAC), apply permissions modifiers, and ensure admin/initialization functions are restricted to trusted roles. 👉 Ensures only authorized actors can invoke critical logic.
SC02 – Business Logic Vulnerabilities
What it is: Flaws in how contract logic is designed, not just coded (e.g., incorrect accounting, faulty rewards, broken lending logic). Why it matters: Even if code is syntactically correct, logic errors can be exploited to drain funds or warp protocol economics. Mitigation: Thoroughly define intended behavior, write comprehensive tests, and undergo peer reviews and professional audits. 👉 Helps verify that the contract does what it should, not just compiles.
SC03 – Price Oracle Manipulation
What it is: Contracts often rely on external price feeds (“oracles”). If those feeds can be tampered with or spoofed, protocol logic behaves incorrectly. Why it matters: Manipulated price data can trigger unfair liquidations, bad trades, or exploit chains that profit the attacker. Mitigation: Use decentralized or robust oracle networks with slippage limits, price aggregation, and sanity checks. 👉 Prevents external data from being a weak link in internal calculations.
SC04 – Flash Loan–Facilitated Attacks
What it is: Flash loans let attackers borrow large amounts with no collateral within one transaction and manipulate a protocol. Why it matters: Small vulnerabilities in pricing or logic can be leveraged with borrowed capital to cause big economic damage. Mitigation: Include checks that prevent manipulations during a single transaction (e.g., TWAP pricing, re-pricing guards, invariants). 👉 Stops attackers from using borrowed capital as an offensive weapon.
SC05 – Lack of Input Validation
What it is: A contract accepts values (addresses, amounts, parameters) without checking they are valid or within expected ranges. Why it matters: Bad input can lead to malformed state, unexpected behavior, or exploitable conditions. Mitigation: Validate and sanitize all inputs — reject zero addresses, negative amounts, out-of-range values, and unexpected data shapes. 👉 Reduces the risk of attackers “feeding” bad data into sensitive functions.
SC06 – Unchecked External Calls
What it is: The contract calls external code but doesn’t check if those calls succeed or how they influence its state. Why it matters: A failing external call can leave a contract in an inconsistent state and expose it to exploits. Mitigation: Always check return values or use Solidity patterns that handle call failures explicitly (e.g., require). 👉 Ensures your logic doesn’t blindly trust other contracts or addresses.
SC07 – Arithmetic Errors (Rounding & Precision)
What it is: Mistakes in math operations — rounding, scaling, and precision errors — especially around decimals or shares. Why it matters: In DeFi, small arithmetic mistakes can be exploited repeatedly or magnified with flash loans. Mitigation: Use safe math libraries and clearly define how rounding/truncation should work. Consider fixed-point libraries with clear precision rules. 👉 Avoids subtle calculation bugs that can siphon value over time.
SC08 – Reentrancy Attacks
What it is: A contract calls an external contract before updating its own state. A malicious callee re-enters and manipulates state repeatedly. Why it matters: This classic attack can drain funds, corrupt internal accounting, or turn single actions into repeated ones. Mitigation: Update state before external calls, use reentrancy guards, and follow established secure patterns. 👉 Prevents an external party from interrupting your logic in a harmful order.
SC09 – Integer Overflow and Underflow
What it is: Arithmetic exceeds the maximum or minimum representable integer value, causing wrap-around behavior. Why it matters: Attackers can exploit wrapped values to inflate balances or break invariants. Mitigation: Use Solidity’s built-in checked arithmetic (since 0.8.x) or libraries that revert on overflow/underflow. 👉 Stops attackers from exploiting unexpected number behavior.
SC10 – Proxy & Upgradeability Vulnerabilities
What it is: Misconfigured upgrade mechanisms or proxy patterns let attackers take over contract logic or state. Why it matters: Many modern protocols support upgrades; an insecure path can allow malicious re-deployments, unauthorized initialization, or bypass of intended permissions. Mitigation: Secure admin keys, guard initializer functions, and use time-locked governance for upgrades. 👉 Ensures upgrade patterns do not become new attack surfaces.
💡 How the Top 10 Helps Build Better Smart Contracts
Security baseline: Provides a structured checklist for teams to review and assess risk throughout development and before deployment.
Risk prioritization: Highlights the most exploited or impactful vulnerabilities seen in real attacks, not just academic theory.
Design guidance: Encourages developers to bake security into requirements, design, testing, and deployment — not just fix bugs reactively.
Audit support: Auditors and reviewers can use the Top 10 as a framework to validate coverage and threat modeling.
🧠 Feedback Summary
The OWASP Smart Contract Top 10 is valuable because it combines empirical data and expert consensus to pinpoint where real smart contract breaches occur. It moves beyond generic lists to specific classes tailored for blockchain platforms. As a result:
It helps developers avoid repeat mistakes made by others.
It provides practical remediations rather than abstract guidance.
It supports continuous improvement in smart contract practices as the threat landscape evolves.
Using this list early in design (not just before audits) can elevate security hygiene and reduce costly exploits.
Below are practical Solidity defense patterns and code snippets mapped to each item in the OWASP Smart Contract Top 10 (2026). These are simplified examples meant to illustrate secure design patterns, not production-ready contracts.
SC01 — Access Control Vulnerabilities
Defense pattern: Role-based access control + modifiers
Key idea: Prevent re-initialization and tightly control upgrade authority.
Practical Takeaway
These patterns collectively enforce a secure smart contract lifecycle:
Restrict authority (who can act)
Validate assumptions (what is allowed)
Protect math and logic (how it behaves)
Guard external interactions (who you trust)
Secure upgrades (how it evolves)
They translate abstract vulnerability categories into repeatable engineering habits.
Here’s a practical mapping of the OWASP Smart Contract Top 10 (2026) to a real-world smart contract audit workflow — structured the way professional auditors actually run engagements.
I’ll show:
👉 Audit phase → What auditors do → Which Top 10 risks are checked → Tools & techniques
Smart Contract Audit Workflow Mapped to OWASP Top 10
1. Scope Definition & Threat Modeling
Goal: Understand architecture, trust boundaries, and attack surface before touching code.
What auditors do
Review protocol architecture diagrams
Identify privileged roles and external dependencies
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.
— From Reactive Defense to Intelligent Protection
Artificial intelligence is fundamentally changing the way organizations defend against cyber threats. As digital ecosystems expand and attackers become more sophisticated, traditional security tools alone are no longer enough. AI introduces speed, scale, and intelligence into cybersecurity operations, enabling systems to detect and respond to threats in real time. This shift marks a transition from reactive defense to proactive and predictive protection.
One of the most impactful uses of AI is in AI-powered threat hunting. Instead of waiting for alerts, AI continuously scans massive volumes of network data to uncover hidden or emerging threats. By recognizing patterns and anomalies that humans might miss, AI helps security teams identify suspicious behavior early. This proactive capability reduces dwell time and strengthens overall situational awareness.
Another critical capability is dynamic risk assessment. AI systems continuously evaluate vulnerabilities and changing threat landscapes, updating risk profiles in real time. This allows organizations to prioritize defenses and allocate resources where they matter most. Adaptive risk modeling ensures that security strategies evolve alongside emerging threats rather than lag behind them.
AI also strengthens endpoint security by monitoring devices such as laptops, servers, and mobile systems. Through behavioral analysis, AI can detect unusual activities and automatically isolate compromised endpoints. Continuous monitoring helps prevent lateral movement within networks and minimizes the potential impact of breaches.
AI-driven identity protection enhances authentication and access control. By analyzing behavioral patterns and biometric signals, AI can distinguish legitimate users from impostors. This reduces the risk of credential theft and unauthorized access while enabling more seamless and secure user experiences.
Another key advantage is faster incident response. AI accelerates detection, triage, and remediation by automating routine tasks and correlating threat intelligence instantly. Security teams can respond to incidents in minutes rather than hours, limiting damage and downtime. Automation also reduces alert fatigue and improves operational efficiency.
The image also highlights adaptive defense, where AI-driven systems learn from past attacks and continuously refine their protective measures. These systems evolve alongside threat actors, creating a feedback loop that strengthens defenses over time. Adaptive security architectures make organizations more resilient to unknown or zero-day threats.
To counter threats using AI-powered threat hunting, organizations should deploy machine learning models trained on diverse threat intelligence and integrate them with human-led threat analysis. Combining automated discovery with expert validation ensures both speed and accuracy while minimizing false positives.
For dynamic risk assessment, companies should implement AI-driven risk dashboards that integrate vulnerability scanning, asset inventories, and real-time telemetry. In endpoint security, AI-based EDR (Endpoint Detection and Response) tools should be paired with automated isolation policies. For identity protection, behavioral biometrics and zero-trust frameworks should be reinforced by AI anomaly detection. To enable faster incident response, orchestration and automated response playbooks are essential. Finally, adaptive defense requires continuous learning pipelines that retrain models with updated threat data and feedback from security operations.
Overall, AI is becoming a central pillar of modern cybersecurity. It amplifies human expertise, accelerates detection and response, and enables organizations to defend against increasingly complex threats. However, AI is not a standalone solution—it must be combined with governance, skilled professionals, and ethical safeguards. When used responsibly, AI transforms cybersecurity from a defensive necessity into a strategic advantage that prepares organizations for the evolving digital future.
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.
The iceberg captures the reality of AI transformation.
At the very top of the iceberg sits “AI Strategy.” This is the visible, exciting part—the headlines about GenAI, AI agents, copilots, and transformation. On the surface, leaders are saying, “AI will transform us,” and teams are eager to “move fast.” This is where ambition lives.
Just below the waterline, however, are the layers most organizations prefer not to talk about.
First come legacy systems—applications stitched together over decades through acquisitions, quick fixes, and short-term decisions. These systems were never designed to support real-time AI workflows, yet they hold critical business data.
Beneath that are data pipelines—fragile processes moving data between systems. Many break silently, rely on manual intervention, or produce inconsistent outputs. AI models don’t fail dramatically at first; they fail subtly when fed inconsistent or delayed data.
Below that lies integration debt—APIs, batch jobs, and custom connectors built years ago, often without clear ownership. When no one truly understands how systems talk to each other, scaling AI becomes risky and slow.
Even deeper is undocumented code—business logic embedded in scripts and services that only a few long-tenured employees understand. This is the most dangerous layer. When AI systems depend on logic no one can confidently explain, trust erodes quickly.
This is where the real problems live—beneath the surface. Organizations are trying to place advanced AI strategies on top of foundations that are unstable. It’s like installing smart automation in a building with unreliable wiring.
We’ve seen what happens when the foundation isn’t ready:
AI systems trained on “clean” lab data struggle in messy real-world environments.
Models inherit bias from historical datasets and amplify it.
Enterprise AI pilots stall—not because the algorithms are weak, but because data quality, workflows, and integrations can’t support them.
If AI is to work at scale, the invisible layers must become the priority.
Clean Data
Clean data means consistent definitions, deduplicated records, validated inputs, and reconciled sources of truth. It means knowing which dataset is authoritative. AI systems amplify whatever they are given—if the data is flawed, the intelligence will be flawed. Clean data is the difference between automation and chaos.
Strong Pipelines
Strong pipelines ensure data flows reliably, securely, and in near real time. They include monitoring, error handling, lineage tracking, and version control. AI cannot depend on pipelines that break quietly or require manual fixes. Reliability builds trust.
Disciplined Integration
Disciplined integration means structured APIs, documented interfaces, clear ownership, and controlled change management. AI agents must interact with systems in predictable ways. Without integration discipline, AI becomes brittle and risky.
Governance
Governance defines accountability—who owns the data, who approves models, who monitors bias, who audits outcomes. It aligns AI usage with regulatory, ethical, and operational standards. Without governance, AI becomes experimentation without guardrails.
Documentation
Documentation captures business logic, data definitions, workflows, and architectural decisions. It reduces dependency on tribal knowledge. In AI governance, documentation is not bureaucracy—it is institutional memory and operational resilience.
The Bigger Picture
GenAI is powerful. But it is not magic. It does not repair fragmented data landscapes or reconcile conflicting system logic. It accelerates whatever foundation already exists.
The organizations that succeed with AI won’t be the ones that move fastest at the top of the iceberg. They will be the ones willing to strengthen what lies beneath the waterline.
AI is the headline. Data infrastructure is the foundation. AI Governance is the discipline that makes transformation real.
My perspective: AI Governance is not about controlling innovation—it’s about preparing the enterprise so innovation doesn’t collapse under its own ambition. The “boring” work—data quality, integration discipline, documentation, and oversight—is not a delay to transformation. It is the transformation.
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.