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When a $3K “cybersecurity gap assessment” reveals you don’t actually have cybersecurity to assess…
A prospect just reached out wanting to pay me $3,000 to assess their ISO 27001 readiness.
Here’s how that conversation went:
Me: “Can you share your security policies and procedures?” Them: “We don’t have any.”
Me: “How about your latest penetration test, vulnerability scans, or cloud security assessments?” Them: “Nothing.”
Me: “What about your asset inventory, vendor register, or risk assessments?” Them: “We haven’t done those.”
Me: “Have you conducted any vendor security due diligence or data privacy reviews?” Them: “No.”
Me: “Let’s try HR—employee contracts, job descriptions, onboarding/offboarding procedures?” Them: “It’s all ad hoc. Nothing formal.”
Here’s the problem: You can’t assess what doesn’t exist.
It’s like subscribing to a maintenance plan for an appliance you don’t own yet
The reality? Many organizations confuse “having IT systems” with “having cybersecurity.” They’re running business-critical operations with zero security foundation—no documentation, no testing, no governance.
What they actually need isn’t an assessment. It’s a security program built from the ground up.
ISO 27001 compliance isn’t a checkbox exercise. It requires: âś“ Documented policies and risk management processes âś“ Regular security testing and validation âś“ Asset and vendor management frameworks âś“ HR security controls and awareness training
If you’re in this situation, here’s my advice: Don’t waste money on assessments. Invest in building foundational security controls first. Then assess.
What’s your take? Have you encountered organizations confusing security assessment with security implementation?
Here are some of the main benefits of using Burp Suite Professional — specifically from the perspective of a professional services consultant doing security assessments, penetration testing, or audits for clients. I highlight where Burp Pro gives real value in a professional consulting context.
✅ Why consultants often prefer Burp Suite Professional
Comprehensive, all-in-one toolkit for web-app testing Burp Pro bundles proxying, crawling/spidering, vulnerability scanning, request replay/manipulation, fuzzing/brute forcing, token/sequence analysis, and more — all in a single product. This lets a consultant perform full-scope web application assessments without needing to stitch together many standalone tools.
Automated scanning + manual testing — balanced for real-world audits As a consultant you often need to combine speed (to scan large or complex applications) and depth (to manually investigate subtle issues or business-logic flaws). Burp Pro’s automated scanner quickly highlights many common flaws (e.g. SQLi, XSS, insecure configs), while its manual tools (proxy, repeater, intruder, etc.) allow fine-grained verification and advanced exploitation.
Discovery of “hidden” or non-obvious issues / attack surfaces The crawler/spider + discovery features help map out a target application’s entire attack surface — including hidden endpoints, unlinked pages or API endpoints — which consultants need to find when doing thorough security reviews.
Flexibility for complex or modern web apps (APIs, SPAs, WebSockets, etc.) Many modern applications use single-page frameworks, APIs, WebSockets, token-based auth, etc. Burp Pro supports testing these complex setups (e.g. handling HTTPS, WebSockets, JSON APIs), enabling consultants to operate effectively even on modern, dynamic web applications.
Extensibility and custom workflows tailored to client needs Through the built-in extension store (the “BApp Store”), and via scripting/custom plugins, consultants can customize Burp Pro to fit the unique architecture or threat model of a client’s environment — which is crucial in professional consulting where every client is different.
Professional-grade reporting & audit deliverables Consultants often need to deliver clear, structured, prioritized vulnerability reports to clients or stakeholders. Burp Pro supports detailed reporting, with evidence, severity, context — making it easier to communicate findings and remediation steps.
Efficiency and productivity: saves time and resources By automating large parts of scanning and combining multiple tools in one, Burp Pro helps consultants complete engagements faster — freeing time for deeper manual analysis, more clients, or more thorough work.
Up-to-date detection logic and community / vendor support As new web-app vulnerabilities and attack vectors emerge, Burp Pro (supported by its vendor and community) gets updates and new detection logic — which helps consultants stay current and offer reliable security assessments.
🚨 React2Shell detection is now available in Burp Suite Professional & Burp Suite DAST.
🎯 What this enables in a Consulting / Professional Services Context
Using Burp Suite Professional allows a consultant to:
Provide comprehensive security audits covering a broad attack surface — from standard web pages to APIs, dynamic front-ends, and even modern client-side logic.
Combine fast automated scanning with deep manual review, giving confidence that both common and subtle or business-logic vulnerabilities are identified.
Deliver clear, actionable reports and remediation guidance — a must when working with clients or stakeholders who need to understand risk and prioritize fixes.
Adapt quickly to different client environments — thanks to extensions, custom workflows, and configurability.
Scale testing work: for example, map and scan large applications efficiently, then focus consultant time on validating and exploiting deeper issues rather than chasing basic ones.
Maintain a professional standard of work — many clients expect usage of recognized tools, reproducible evidence, and thorough testing, all of which Burp Pro supports.
✅ Summary — Pro version pays off in consulting work
For a security consultant, Burp Suite Professional isn’t just a “nice to have” — it often becomes a core piece of the toolset. Its mix of automation, manual flexibility, extensibility, and reporting makes it highly suitable for professional-grade penetration testing, audits, and security assessments. While there are other tools out there, the breadth and polish of Burp Pro tends to make it “default standard” in many consulting engagements.
At DISC InfoSec, we provide comprehensive security audits that cover your entire digital attack surface — from standard web pages to APIs, dynamic front-ends, and even modern client-side logic. Our expert team not only identifies vulnerabilities but also delivers a tailored mitigation plan designed to reduce risks and provide assurance against potential security incidents. With DISC InfoSec, you gain the confidence that your applications and data are protected, while staying ahead of emerging threats.
ISO 42001 (published December 2023) is the first international standard dedicated to how organizations should govern and manage AI systems — whether they build AI, use it, or deploy it in services.
It lays out what the authors call an Artificial Intelligence Management System (AIMS) — a structured governance and management framework that helps companies reduce AI-related risks, build trust, and ensure responsible AI use.
Who can use it — and is it mandatory
Any organization — profit or non-profit, large or small, in any industry — that develops or uses AI can implement ISO 42001.
For now, ISO 42001 is not legally required. No country currently mandates it.
But adopting it proactively can make future compliance with emerging AI laws and regulations easier.
What ISO 42001 requires / how it works
The standard uses a “high-level structure” similar to other well-known frameworks (like ISO 27001), covering organizational context, leadership, planning, support, operations, performance evaluation, and continual improvement.
Organizations need to: define their AI-policy and scope; identify stakeholders and expectations; perform risk and impact assessments (on company level, user level, and societal level); implement controls to mitigate risks; maintain documentation and records; monitor, audit, and review the AI system regularly; and continuously improve.
As part of these requirements, there are 38 example controls (in the standard’s Annex A) that organizations can use to reduce various AI-related risks.
Why it matters
Because AI is powerful but also risky (wrong outputs, bias, privacy leaks, system failures, etc.), having a formal governance framework helps companies be more responsible and transparent when deploying AI.
For organizations that want to build trust with customers, regulators, or partners — or anticipate future AI-related regulations — ISO 42001 can serve as a credible, standardized foundation for AI governance.
My opinion
I think ISO 42001 is a valuable and timely step toward bringing some order and accountability into the rapidly evolving world of AI. Because AI is so flexible and can be used in many different contexts — some of them high-stakes — having a standard framework helps organizations think proactively about risk, ethics, transparency, and responsibility rather than scrambling reactively.
That said — because it’s new and not yet mandatory — its real-world impact depends heavily on how widely it’s adopted. For it to become meaningful beyond “nice to have,” regulators, governments, or large enterprises should encourage or require it (or similar frameworks). Until then, it will likely be adopted mostly by forward-thinking companies or those dealing with high-impact AI systems.
🔎 My view: ISO 42001 is a meaningful first step — but (for now) best seen as a foundation, not a silver bullet
I believe ISO 42001 represents a valuable starting point for bringing structure, accountability, and risk awareness to AI development and deployment. Its emphasis on governance, impact assessment, documentation, and continuous oversight is much needed in a world where AI adoption often runs faster than regulation or best practices.
That said — given its newness, generality, and the typical resource demands — I see it as necessary but not sufficient. It should be viewed as the base layer: useful for building internal discipline, preparing for regulatory demands, and signaling commitment. But to address real-world ethical, social, and technical challenges, organizations likely need additional safeguards — e.g. context-specific controls, ongoing audits, stakeholder engagement, domain-specific reviews, and perhaps even bespoke governance frameworks tailored to the type of AI system and its use cases.
In short: ISO 42001 is a strong first step — but real responsible AI requires going beyond standards to culture, context, and continuous vigilance.
✅ Real-world adopters of ISO 42001
IBM (Granite models)
IBM became “the first major open-source AI model developer to earn ISO 42001 certification,” for its “Granite” family of open-source language models.
The certification covers the management system for development, deployment, and maintenance of Granite — meaning IBM formalized policies, governance, data practices, documentation, and risk controls under AIMS (AI Management System).
According to IBM, the certification provides external assurance of transparency, security, and governance — helping enterprises confidently adopt Granite in sensitive contexts (e.g. regulated industries).
Infosys
Infosys — a global IT services and consulting company — announced in May 2024 that it had received ISO 42001:2023 certification for its AI Management System.
Their certified “AIMS framework” is part of a broader set of offerings (the “Topaz Responsible AI Suite”), which supports clients in building and deploying AI responsibly, with structured risk mitigations and accountability.
This demonstrates that even big consulting companies, not just pure-AI labs, see value in adopting ISO 42001 to manage AI at scale within enterprise services.
JAGGAER (Source-to-Pay / procurement software)
JAGGAER — a global player in procurement / “source-to-pay” software — announced that it achieved ISO 42001 certification for its AI Management System in June 2025.
For JAGGAER, the certification reflects a commitment to ethical, transparent, secure deployment of AI within its procurement platform.
This shows how ISO 42001 can be used not only by AI labs or consultancy firms, but by business-software companies integrating AI into domain-specific applications.
🧠 My take — promising first signals, but still early days
These early adopters make a strong case that ISO 42001 can work in practice across very different kinds of organizations — not just AI-native labs, but enterprises, service providers, even consulting firms. The variety and speed of adoption (multiple firms in 2024–2025) demonstrate real momentum.
At the same time — adoption appears selective, and for many companies, the process may involve minimal compliance effort rather than deep, ongoing governance. Because the standard and the ecosystem (auditors, best-practice references, peer case studies) are both still nascent, there’s a real risk that ISO 42001 becomes more of a “badge” than a strong guardrail.
In short: I see current adoptions as proof-of-concepts — promising early examples showing how ISO 42001 could become an industry baseline. But for it to truly deliver on safe, ethical, responsible AI at scale, we’ll need: more widespread adoption across sectors; shared transparency about governance practices; public reporting on outcomes; and maybe supplementary audits or domain-specific guidelines (especially for high-risk AI uses).
Most organizations think they’re ready for AI governance — until ISO/IEC 42001 shines a light on the gaps. With 47 new AI-specific controls, this standard is quickly becoming the global expectation for responsible and compliant AI deployment. To help teams get ahead, we built a free ISO 42001 Compliance Checklist that gives you a readiness score in under 10 minutes, plus a downloadable gap report you can share internally. It’s a fast way to validate where you stand today and what you’ll need to align with upcoming regulatory and customer requirements. If improving AI trust, risk posture, and audit readiness is on your roadmap, this tool will save your team hours.
In a recent report, researchers at Cato Networks revealed that the “Skills” plug‑in feature of Claude — the AI system developed by Anthropic — can be trivially abused to deploy ransomware.
The exploit involved taking a legitimate, open‑source plug‑in (a “GIF Creator” skill) and subtly modifying it: by inserting a seemingly harmless function that downloads and executes external code, the modified plug‑in can pull in a malicious script (in this case, ransomware) without triggering warnings.
When a user installs and approves such a skill, the plug‑in gains persistent permissions: it can read/write files, download further code, and open outbound connections, all without any additional prompts. That “single‑consent” permission model creates a dangerous consent gap.
In the demonstration by Cato Networks researcher Inga Cherny, they didn’t need deep technical skill — they simply edited the plug‑in, re-uploaded it, and once a single employee approved it, ransomware (specifically MedusaLocker) was deployed. Cherny emphasized that “anyone can do it — you don’t even have to write the code.”
Microsoft and other security watchers have observed that MedusaLocker belongs to a broader, active family of ransomware that has targeted numerous organizations globally, often via exploited vulnerabilities or weaponized tools.
This event marks a disturbing evolution in AI‑related cyber‑threats: attackers are moving beyond simple prompt‑based “jailbreaks” or phishing using generative AI — now they’re hijacking AI platforms themselves as delivery mechanisms for malware, turning automation tools into attack vectors.
It’s also a wake-up call for corporate IT and security teams. As more development teams adopt AI plug‑ins and automation workflows, there’s a growing risk that something as innocuous as a “productivity tool” could conceal a backdoor — and once installed, bypass all typical detection mechanisms under the guise of “trusted” software.
Finally, while the concept of AI‑driven attacks has been discussed for some time, this proof‑of-concept exploit shifts the threat from theoretical to real. It demonstrates how easily AI systems — even those with safety guardrails — can be subverted to perform malicious operations when trust is misplaced or oversight is lacking.
🧠 My Take
This incident highlights a fundamental challenge: as we embrace AI for convenience and automation, we must not forget that the same features enabling productivity can be twisted into attack vectors. The “single‑consent” permission model underlying many AI plug‑ins seems especially risky — once that trust is granted, there’s little transparency about what happens behind the scenes.
In my view, organizations using AI–enabled tools should treat them like any other critical piece of infrastructure: enforce code review, restrict who can approve plug‑ins, and maintain strict operational oversight. For people like you working in InfoSec and compliance — especially in small/medium businesses like wineries — this is a timely reminder: AI adoption must be accompanied by updated governance and threat models, not just productivity gains.
Below is a checklist of security‑best practices (for companies and vCISOs) to guard against misuse of AI plug‑ins — could be a useful to assess your current controls.
Meet Your Virtual Chief AI Officer: Enterprise AI Governance Without the Enterprise Price Tag
The question isn’t whether your organization needs AI governance—it’s whether you can afford to wait until you have budget for a full-time Chief AI Officer to get started.
Most mid-sized companies find themselves in an impossible position: they’re deploying AI tools across their operations, facing increasing regulatory scrutiny from frameworks like the EU AI Act and ISO 42001, yet they lack the specialized leadership needed to manage AI risks effectively. A full-time Chief AI Officer commands $250,000-$400,000 annually, putting enterprise-grade AI governance out of reach for organizations that need it most.
The Virtual Chief AI Officer Solution
DeuraInfoSec pioneered a different approach. Our Virtual Chief AI Officer (vCAIO) model delivers the same strategic AI governance leadership that Fortune 500 companies deploy—on a fractional basis that fits your organization’s actual needs and budget.
Think of it like the virtual CISO (vCISO) model that revolutionized cybersecurity for mid-market companies. Instead of choosing between no governance and an unaffordable executive, you get experienced AI governance leadership, proven implementation frameworks, and ongoing strategic guidance—all delivered remotely through a structured engagement model.
How the vCAIO Model Works
Our vCAIO services are built around three core tiers, each designed to meet organizations at different stages of AI maturity:
Tier 1: AI Governance Assessment & Roadmap
What you get: A comprehensive evaluation of your current AI landscape, risk profile, and compliance gaps—delivered in 4-6 weeks.
We start by understanding what AI systems you’re actually running, where they touch sensitive data or critical decisions, and what regulatory requirements apply to your industry. Our assessment covers:
Complete AI system inventory and risk classification
Gap analysis against ISO 42001, EU AI Act, and industry-specific requirements
Vendor AI risk evaluation for third-party tools
Executive-ready governance roadmap with prioritized recommendations
Delivered through: Virtual workshops with key stakeholders, automated assessment tools, document review, and a detailed written report with implementation timeline.
Ideal for: Organizations just beginning their AI governance journey or those needing to understand their compliance position before major AI deployments.
Tier 2: AI Policy Design & Implementation
What you get: Custom AI governance framework designed for your organization’s specific risks, operations, and regulatory environment—implemented over 8-12 weeks.
We don’t hand you generic templates. Our team develops comprehensive, practical governance documentation that your organization can actually use:
AI Management System (AIMS) framework aligned with ISO 42001
AI acceptable use policies and control procedures
Risk assessment and impact analysis processes
Model development, testing, and deployment standards
Incident response and monitoring protocols
Training materials for developers, users, and leadership
Ideal for: Organizations with mature AI deployments needing ongoing governance oversight, or those in regulated industries requiring continuous compliance demonstration.
Why Organizations Choose the vCAIO Model
Immediate Expertise: Our team includes practitioners who are actively implementing ISO 42001 at ShareVault while consulting for clients across financial services, healthcare, and B2B SaaS. You get real-world experience, not theoretical frameworks.
Scalable Investment: Start with an assessment, expand to policy implementation, then scale up to ongoing advisory as your AI maturity grows. No need to commit to full-time headcount before you understand your governance requirements.
Faster Time to Compliance: We’ve already built the frameworks, templates, and processes. What would take an internal hire 12-18 months to develop, we deliver in weeks—because we’re deploying proven methodologies refined across multiple implementations.
Flexibility: Need more support during a major AI deployment or regulatory audit? Scale up engagement. Hit a slower period? Scale back. The vCAIO model adapts to your actual needs rather than fixed headcount.
Delivered Entirely Online
Every aspect of our vCAIO services is designed for remote delivery. We conduct governance assessments through secure virtual workshops and automated tools. Policy development happens through collaborative online sessions with your stakeholders. Ongoing monitoring uses cloud-based dashboards and scheduled video check-ins.
This approach isn’t just convenient—it’s how modern AI governance should work. Your AI systems operate across distributed environments. Your governance should too.
Who Benefits from vCAIO Services
Our vCAIO model serves organizations facing AI governance challenges without the resources for full-time leadership:
Mid-sized B2B SaaS companies deploying AI features while preparing for enterprise customer security reviews
Financial services firms using AI for fraud detection, underwriting, or advisory services under increasing regulatory scrutiny
Healthcare organizations implementing AI diagnostic or operational tools subject to FDA or HIPAA requirements
Private equity portfolio companies needing to demonstrate AI governance for exits or due diligence
Professional services firms adopting generative AI tools while maintaining client confidentiality obligations
Getting Started
The first step is understanding where you stand. We offer a complimentary 30-minute AI governance consultation to review your current position, identify immediate risks, and recommend the appropriate engagement tier for your organization.
From there, most clients begin with our Tier 1 Assessment to establish a baseline and roadmap. Organizations with urgent compliance deadlines or active AI deployments sometimes start directly with Tier 2 policy implementation.
The goal isn’t to sell you the highest tier—it’s to give you exactly the AI governance leadership your organization needs right now, with a clear path to scale as your AI maturity grows.
The Alternative to Doing Nothing
Many organizations tell themselves they’ll address AI governance “once things slow down” or “when we have more budget.” Meanwhile, they continue deploying AI tools, creating risk exposure and compliance gaps that become more expensive to fix with each passing quarter.
The Virtual Chief AI Officer model exists because AI governance can’t wait for perfect conditions. Your competitors are using AI. Your regulators are watching AI. Your customers are asking about AI.
You need governance leadership now. You just don’t need to hire someone full-time to get it.
Ready to discuss how Virtual Chief AI Officer services could work for your organization?
Contact us at hd@deurainfosec.com or visit DeuraInfoSec.com to schedule your complimentary AI governance consultation.
DeuraInfoSec specializes in AI governance consulting and ISO 42001 implementation. As pioneer-practitioners actively implementing these frameworks at ShareVault while consulting for clients across industries, we deliver proven methodologies refined through real-world deployment—not theoretical advice.
How to Assess Your Current Compliance Framework Against ISO 42001
Published by DISCInfoSec | AI Governance & Information Security Consulting
The AI Governance Challenge Nobody Talks About
Your organization has invested years building robust information security controls. You’re ISO 27001 certified, SOC 2 compliant, or aligned with NIST Cybersecurity Framework. Your security posture is solid.
Then your engineering team deploys an AI-powered feature.
Suddenly, you’re facing questions your existing framework never anticipated: How do we detect model drift? What about algorithmic bias? Who reviews AI decisions? How do we explain what the model is doing?
Here’s the uncomfortable truth: Traditional compliance frameworks weren’t designed for AI systems. ISO 27001 gives you 93 controls—but only 51 of them apply to AI governance. That leaves 47 critical gaps.
This isn’t a theoretical problem. It’s affecting organizations right now as they race to deploy AI while regulators sharpen their focus on algorithmic accountability, fairness, and transparency.
At DISCInfoSec, we’ve built a free assessment tool that does something most organizations struggle with manually: it maps your existing compliance framework against ISO 42001 (the international standard for AI management systems) and shows you exactly which AI governance controls you’re missing.
Not vague recommendations. Not generic best practices. Specific, actionable control gaps with remediation guidance.
What Makes This Tool Different
1. Framework-Specific Analysis
Select your current framework:
ISO 27001: Identifies 47 missing AI controls across 5 categories
SOC 2: Identifies 26 missing AI controls across 6 categories
NIST CSF: Identifies 23 missing AI controls across 7 categories
Each framework has different strengths and blindspots when it comes to AI governance. The tool accounts for these differences.
2. Risk-Prioritized Results
Not all gaps are created equal. The tool categorizes each missing control by risk level:
Critical Priority: Controls that address fundamental AI safety, fairness, or accountability issues
High Priority: Important controls that should be implemented within 90 days
Medium Priority: Controls that enhance AI governance maturity
This lets you focus resources where they matter most.
3. Comprehensive Gap Categories
The analysis covers the complete AI governance lifecycle:
AI System Lifecycle Management
Planning and requirements specification
Design and development controls
Verification and validation procedures
Deployment and change management
AI-Specific Risk Management
Impact assessments for algorithmic fairness
Risk treatment for AI-specific threats
Continuous risk monitoring as models evolve
Data Governance for AI
Training data quality and bias detection
Data provenance and lineage tracking
Synthetic data management
Labeling quality assurance
AI Transparency & Explainability
System transparency requirements
Explainability mechanisms
Stakeholder communication protocols
Human Oversight & Control
Human-in-the-loop requirements
Override mechanisms
Emergency stop capabilities
AI Monitoring & Performance
Model performance tracking
Drift detection and response
Bias and fairness monitoring
4. Actionable Remediation Guidance
For every missing control, you get:
Specific implementation steps: Not “implement monitoring” but “deploy MLOps platform with drift detection algorithms and configurable alert thresholds”
Realistic timelines: Implementation windows ranging from 15-90 days based on complexity
ISO 42001 control references: Direct mapping to the international standard
5. Downloadable Comprehensive Report
After completing your assessment, download a detailed PDF report (12-15 pages) that includes:
Executive summary with key metrics
Phased implementation roadmap
Detailed gap analysis with remediation steps
Recommended next steps
Resource allocation guidance
How Organizations Are Using This Tool
Scenario 1: Pre-Deployment Risk Assessment
A fintech company planning to deploy an AI-powered credit decisioning system used the tool to identify gaps before going live. The assessment revealed they were missing:
Algorithmic impact assessment procedures
Bias monitoring capabilities
Explainability mechanisms for loan denials
Human review workflows for edge cases
Result: They addressed critical gaps before deployment, avoiding regulatory scrutiny and reputational risk.
Scenario 2: Board-Level AI Governance
A healthcare SaaS provider’s board asked, “Are we compliant with AI regulations?” Their CISO used the gap analysis to provide a data-driven answer:
62% AI governance coverage from their existing SOC 2 program
18 critical gaps requiring immediate attention
$450K estimated remediation budget
6-month implementation timeline
Result: Board approved AI governance investment with clear ROI and risk mitigation story.
Scenario 3: M&A Due Diligence
A private equity firm evaluating an AI-first acquisition used the tool to assess the target company’s governance maturity:
Target claimed “enterprise-grade AI governance”
Gap analysis revealed 31 missing controls
Due diligence team identified $2M+ in post-acquisition remediation costs
Result: PE firm negotiated purchase price adjustment and built remediation into first 100 days.
Scenario 4: Vendor Risk Assessment
An enterprise buyer evaluating AI vendor solutions used the gap analysis to inform their vendor questionnaire:
Identified which AI governance controls were non-negotiable
Created tiered vendor assessment based on AI risk level
Built contract language requiring specific ISO 42001 controls
Result: More rigorous vendor selection process and better contractual protections.
The Strategic Value Beyond Compliance
While the tool helps you identify compliance gaps, the real value runs deeper:
1. Resource Allocation Intelligence
Instead of guessing where to invest in AI governance, you get a prioritized roadmap. This helps you:
Justify budget requests with specific control gaps
Allocate engineering resources to highest-risk areas
The EU AI Act, proposed US AI regulations, and industry-specific requirements all reference concepts like impact assessments, transparency, and human oversight. ISO 42001 anticipates these requirements. By mapping your gaps now, you’re building proactive regulatory readiness.
3. Competitive Differentiation
As AI becomes table stakes, how you govern AI becomes the differentiator. Organizations that can demonstrate:
Systematic bias monitoring
Explainable AI decisions
Human oversight mechanisms
Continuous model validation
…win in regulated industries and enterprise sales.
4. Risk-Informed AI Strategy
The gap analysis forces conversations between technical teams, risk functions, and business leaders. These conversations often reveal:
AI use cases that are higher risk than initially understood
Opportunities to start with lower-risk AI applications
Need for governance infrastructure before scaling AI deployment
What the Assessment Reveals About Different Frameworks
ISO 27001 Organizations (51% AI Coverage)
Strengths: Strong foundation in information security, risk management, and change control.
Critical Gaps:
AI-specific risk assessment methodologies
Training data governance
Model drift monitoring
Explainability requirements
Human oversight mechanisms
Key Insight: ISO 27001 gives you the governance structure but lacks AI-specific technical controls. You need to augment with MLOps capabilities and AI risk assessment procedures.
SOC 2 Organizations (59% AI Coverage)
Strengths: Solid monitoring and logging, change management, vendor management.
Critical Gaps:
AI impact assessments
Bias and fairness monitoring
Model validation processes
Explainability mechanisms
Human-in-the-loop requirements
Key Insight: SOC 2’s focus on availability and processing integrity partially translates to AI systems, but you’re missing the ethical AI and fairness components entirely.
Key Insight: NIST CSF provides the risk management philosophy but lacks prescriptive AI controls. You need to operationalize AI governance with specific procedures and technical capabilities.
The ISO 42001 Advantage
Why use ISO 42001 as the benchmark? Three reasons:
1. International Consensus: ISO 42001 represents global agreement on AI governance requirements, making it a safer bet than region-specific regulations that may change.
2. Comprehensive Coverage: It addresses technical controls (model validation, monitoring), process controls (lifecycle management), and governance controls (oversight, transparency).
3. Audit-Ready Structure: Like ISO 27001, it’s designed for third-party certification, meaning the controls are specific enough to be auditable.
Getting Started: A Practical Approach
Here’s how to use the AI Control Gap Analysis tool strategically:
Determine build vs. buy decisions (e.g., MLOps platforms)
Create phased implementation plan
Step 4: Governance Foundation (Months 1-2)
Establish AI governance committee
Create AI risk assessment procedures
Define AI system lifecycle requirements
Implement impact assessment process
Step 5: Technical Controls (Months 2-4)
Deploy monitoring and drift detection
Implement bias detection in ML pipelines
Create model validation procedures
Build explainability capabilities
Step 6: Operationalization (Months 4-6)
Train teams on new procedures
Integrate AI governance into existing workflows
Conduct internal audits
Measure and report on AI governance metrics
Common Pitfalls to Avoid
1. Treating AI Governance as a Compliance Checkbox
AI governance isn’t about checking boxes—it’s about building systematic capabilities to develop and deploy AI responsibly. The gap analysis is a starting point, not the destination.
2. Underestimating Timeline
Organizations consistently underestimate how long it takes to implement AI governance controls. Training data governance alone can take 60-90 days to implement properly. Plan accordingly.
3. Ignoring Cultural Change
Technical controls without cultural buy-in fail. Your engineering team needs to understand why these controls matter, not just what they need to do.
4. Siloed Implementation
AI governance requires collaboration between data science, engineering, security, legal, and risk functions. Siloed implementations create gaps and inconsistencies.
5. Over-Engineering
Not every AI system needs the same level of governance. Risk-based approach is critical. A recommendation engine needs different controls than a loan approval system.
The Bottom Line
Here’s what we’re seeing across industries: AI adoption is outpacing AI governance by 18-24 months. Organizations deploy AI systems, then scramble to retrofit governance when regulators, customers, or internal stakeholders raise concerns.
The AI Control Gap Analysis tool helps you flip this dynamic. By identifying gaps early, you can:
Deploy AI with appropriate governance from day one
Avoid costly rework and technical debt
Build stakeholder confidence in your AI systems
Position your organization ahead of regulatory requirements
The question isn’t whether you’ll need comprehensive AI governance—it’s whether you’ll build it proactively or reactively.
Take the Assessment
Ready to see where your compliance framework falls short on AI governance?
DISCInfoSec specializes in AI governance and information security consulting for B2B SaaS and financial services organizations. We help companies bridge the gap between traditional compliance frameworks and emerging AI governance requirements.
We’re not just consultants telling you what to do—we’re pioneer-practitioners implementing ISO 42001 at ShareVault while helping other organizations navigate AI governance.
🚨 If you’re ISO 27001 certified and using AI, you have 47 control gaps.
And auditors are starting to notice.
Here’s what’s happening right now:
→ SOC 2 auditors asking “How do you manage AI model risk?” (no documented answer = finding)
→ Enterprise customers adding AI governance sections to vendor questionnaires
→ EU AI Act enforcement starting in 2025 → Cyber insurance excluding AI incidents without documented controls
ISO 27001 covers information security. But if you’re using:
Customer-facing chatbots
Predictive analytics
Automated decision-making
Even GitHub Copilot
You need 47 additional AI-specific controls that ISO 27001 doesn’t address.
I’ve mapped all 47 controls across 7 critical areas: âś“ AI System Lifecycle Management âś“ Data Governance for AI âś“ Model Risk & Testing âś“ Transparency & Explainability âś“ Human Oversight & Accountability âś“ Third-Party AI Management âś“ AI Incident Response
🔥 Truth bomb from a experience: You can’t make companies care about security.
Most don’t—until they get burned.
Security isn’t important… until it suddenly is. And by then, it’s often too late. Just ask the businesses that disappeared after a cyberattack.
Trying to convince someone it matters? Like telling your friend to eat healthy—they won’t care until a personal wake-up call hits.
Here’s the smarter play: focus on the people who already value security. Show them why you’re the one who can solve their problems. That’s where your time actually pays off.
Your energy shouldn’t go into preaching; it should go into actionable impact for those ready to act.
⏳ Remember: people only take security seriously when they decide it’s worth it. Your job is to be ready when that moment comes.
Opinion: This perspective is spot-on. Security adoption isn’t about persuasion; it’s about timing and alignment. The most effective consultants succeed not by preaching to the uninterested, but by identifying those who already recognize risk and helping them act decisively.
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The Help Net Security video titled “The CISO’s guide to stronger board communication” features Alisdair Faulkner, CEO of Darwinium, who discusses how the role of the Chief Information Security Officer (CISO) has evolved significantly in recent years. The piece frames the challenge: CISOs now must bridge the gap between deep technical knowledge and strategic business conversations.
Faulkner argues that many CISOs fall into the trap of using overly technical language when speaking with board members. This can lead to misunderstanding, disengagement, or even resistance. He highlights that clarity and relevance are vital: CISOs should aim to translate complex security concepts into business-oriented terms.
One key shift he advocates is positioning cybersecurity not as a cost center, but as a business enabler. In other words, security initiatives should be tied to business value—supporting goals like growth, innovation, resilience, and risk mitigation—rather than being framed purely as expense or compliance.
Faulkner also delves into the effects of artificial intelligence on board-level discussions. He points out that AI is both a tool and a threat: it can enhance security operations, but it also introduces new vulnerabilities and risk vectors. As such, it shifts the nature of what boards must understand about cybersecurity.
To build trust and alignment with executives, the video offers practical strategies. These include focusing on metrics that matter to business leaders, storytelling to make risks tangible, and avoiding the temptation to “drown” stakeholders in technical detail. The goal is to foster informed decision-making, not just to show knowledge.
Faulkner emphasizes resilience and innovation as hallmarks of modern security leadership. Rather than passively reacting to threats, the CISO should help the organization anticipate, adapt, and evolve. This helps ensure that security is integrated into the business’s strategic journey.
Another insight is that board communications should be ongoing and evolving, not limited to annual reviews or audits. As risks, technologies, and business priorities shift, the CISO needs to keep the board apprised, engaged, and confident in the security posture.
In sum, Faulkner’s guidance reframes the CISO’s role—from a highly technical operator to a strategic bridge to the board. He urges CISOs to communicate in business terms, emphasize value and resilience, and adapt to emerging challenges like AI. The video is a call for security leaders to become fluent in “the language of the board.”
My opinion I think this is a very timely and valuable perspective. In many organizations, there’s still a disconnect between cybersecurity teams and executive governance. Framing security in business value rather than technical jargon is essential to elevate the conversation and gain real support. The emphasis on AI is also apt—boards increasingly need to understand both the opportunities and risks it brings. Overall, Faulkner’s approach is pragmatic and strategic, and I believe CISOs who adopt these practices will be more effective and influential.
Here’s a concise cheat sheet based on the article and video:
📝 CISO–Board Communication Cheat Sheet
1. Speak the Board’s Language
Avoid deep technical jargon.
Translate risks into business impact (financial, reputational, operational).
2. Frame Security as a Business Enabler
Position cybersecurity as value-adding, not just a cost or compliance checkbox.
Show how security supports growth, innovation, and resilience.
3. Use Metrics That Matter
Present KPIs that executives care about (risk reduction, downtime avoided, compliance readiness).
Keep dashboards simple and aligned to strategic goals.
4. Leverage Storytelling
Use real scenarios, case studies, or analogies to make risks tangible.
1. Framing a Risk-Aware AI Strategy The book begins by laying out the need for organizations to approach AI not just as a source of opportunity (innovation, efficiency, etc.) but also as a domain rife with risk: ethical risks (bias, fairness), safety, transparency, privacy, regulatory exposure, reputational risk, and so on. It argues that a risk-aware strategy must be integrated into the whole AI lifecycle—from design to deployment and maintenance. Key in its framing is that risk management shouldn’t be an afterthought or a compliance exercise; it should be embedded in strategy, culture, governance structures. The idea is to shift from reactive to proactive: anticipating what could go wrong, and building in mitigations early.
2. How the book leverages ISO 42001 and related standards A core feature of the book is that it aligns its framework heavily with ISO IEC 42001:2023, which is the first international standard to define requirements for establishing, implementing, maintaining, and continuously improving an Artificial Intelligence Management System (AIMS). The book draws connections between 42001 and adjacent or overlapping standards—such as ISO 27001 (information security), ISO 31000 (risk management in general), as well as NIST’s AI Risk Management Framework (AI RMF 1.0). The treatment helps the reader see how these standards can interoperate—where one handles confidentiality, security, access controls (ISO 27001), another handles overall risk governance, etc.—and how 42001 fills gaps specific to AI: lifecycle governance, transparency, ethics, stakeholder traceability.
3. The Artificial Intelligence Management System (AIMS) as central tool The concept of an AI Management System (AIMS) is at the heart of the book. An AIMS per ISO 42001 is a set of interrelated or interacting elements of an organization (policies, controls, processes, roles, tools) intended to ensure responsible development and use of AI systems. The author Andrew Pattison walks through what components are essential: leadership commitment; roles and responsibilities; risk identification, impact assessment; operational controls; monitoring, performance evaluation; continual improvement. One strength is the practical guidance: not just “you should do these”, but how to embed them in organizations that don’t have deep AI maturity yet. The book emphasizes that an AIMS is more than a set of policies—it’s a living system that must adapt, learn, and respond as AI systems evolve, as new risks emerge, and as external demands (laws, regulations, public expectations) shift.
4. Comparison and contrasts: ISO 42001, ISO 27001, and NIST In comparing standards, the book does a good job of pointing out both overlaps and distinct value: for example, ISO 27001 is strong on information security, confidentiality, integrity, availability; it has proven structures for risk assessment and for ensuring controls. But AI systems pose additional, unique risks (bias, accountability of decision-making, transparency, possible harms in deployment) that are not fully covered by a pure security standard. NIST’s AI Risk Management Framework provides flexible guidance especially for U.S. organisations or those aligning with U.S. governmental expectations: mapping, measuring, managing risks in a more domain-agnostic way. Meanwhile, ISO 42001 brings in the notion of an AI-specific management system, lifecycle oversight, and explicit ethical / governance obligations. The book argues that a robust strategy often uses multiple standards: e.g. ISO 27001 for information security, ISO 42001 for overall AI governance, NIST AI RMF for risk measurement & tools.
5. Practical tools, governance, and processes The author does more than theory. There are discussions of impact assessments, risk matrices, audit / assurance, third-party oversight, monitoring for model drift / unanticipated behavior, documentation, and transparency. Some of the more compelling content is about how to do risk assessments early (before deployment), how to engage stakeholders, how to map out potential harms (both known risks and emergent/unknown ones), how governance bodies (steering committees, ethics boards) can play a role, how responsibility should be assigned, how controls should be tested. The book does point out real challenges: culture change, resource constraints, measurement difficulties, especially for ethical or fairness concerns. But it provides guidance on how to surmount or mitigate those.
6. What might be less strong / gaps While the book is very useful, there are areas where some readers might want more. For instance, in scaling these practices in organizations with very little AI maturity: the resource costs, how to bootstrap without overengineering. Also, while it references standards and regulations broadly, there may be less depth on certain jurisdictional regulatory regimes (e.g. EU AI Act in detail, or sector-specific requirements). Another area that is always hard—and the book is no exception—is anticipating novel risks: what about very advanced AI systems (e.g. generative models, large language models) or AI in uncontrolled environments? Some of the guidance is still high-level when it comes to edge-cases or worst-case scenarios. But this is a natural trade-off given the speed of AI advancement.
7. Future of AI & risk management: trends and implications Looking ahead, the book suggests that risk management in AI will become increasingly central as both regulatory pressure and societal expectations grow. Standards like ISO 42001 will be adopted more widely, possibly even made mandatory or incorporated into regulation. The idea of “certification” or attestation of compliance will gain traction. Also, the monitoring, auditing, and accountability functions will become more technically and institutionally mature: better tools for algorithmic transparency, bias measurement, model explainability, data provenance, and impact assessments. There’ll also be more demand for cross-organizational cooperation (e.g. supply chains and third-party models), for oversight of external models, for AI governance in ecosystems rather than isolated systems. Finally, there is an implication that organizations that don’t get serious about risk will pay—through regulation, loss of trust, or harm. So the future is of AI risk management moving from “nice-to-have” to “mission-critical.”
Overall, Managing AI Risk is a strong, timely guide. It bridges theory (standards, frameworks) and practice (governance, processes, tools) well. It makes the case that ISO 42001 is a useful centerpiece for any AI risk strategy, especially when combined with other standards. If you are planning or refining an AI strategy, building or implementing an AIMS, or anticipating future regulatory change, this book gives a solid and actionable foundation.
The role of the modern CISO has evolved far beyond technical oversight. While many entered the field expecting to focus solely on firewalls, frameworks, and fighting cyber threats, the reality is that today’s CISOs must operate as business leaders as much as security experts. Increasingly, the role demands skills that look surprisingly similar to sales.
This shift is driven by business dynamics. Buyers and partners are highly sensitive to security posture. A single breach or regulatory fine can derail deals and destroy trust. As a result, security is no longer just a cost center—it directly influences revenue, customer acquisition, and long-term business resilience.
CISOs now face a dual responsibility: maintaining deep technical credibility while also translating security into a business advantage. Boards and executives are asking not only, “Are we protected?” but also, “How does our security posture help us win business?” This requires CISOs to communicate clearly and persuasively about the commercial value of trust and compliance.
At the same time, budgets are tight and CISO compensation is under scrutiny. Justifying investment in security requires framing it in business terms—showing how it prevents losses, enables sales, and differentiates the company in a competitive market. Security is no longer seen as background infrastructure but as a factor that can make or break deals.
Despite this, many security professionals still resist the sales aspect of the job, seeing it as outside their domain. This resistance risks leaving them behind as the role changes. The reality is that security leadership now includes revenue protection and revenue generation, not just technical defense.
The future CISO will be defined by their ability to translate security into customer confidence and measurable business outcomes. Those who embrace this evolution will shape the next generation of leadership, while those who cling only to the technical side risk becoming sidelined.
Advice on AI’s impact on the CISO role: AI will accelerate this transformation. On the technical side, AI tools will automate many detection, response, and compliance tasks that once required hands-on oversight, reducing the weight of purely operational responsibilities. On the business side, AI will raise customer expectations for security, privacy, and ethical use of data. This means CISOs must increasingly act as “trust architects,” communicating how AI is governed and secured. The CISO who can blend technical authority with persuasive storytelling about AI risk and trust will not only safeguard the enterprise but also directly influence growth. In short, AI will make the CISO less of a firewall operator and more of a business strategist who sells trust.
This book positions itself not just as a technical guide but as a strategic roadmap for the future of cybersecurity leadership. It emphasizes that in today’s complex threat environment, CISOs must evolve beyond technical mastery and step into the role of business leaders who weave cybersecurity into the very fabric of organizational strategy.
The core message challenges the outdated view of CISOs as purely technical experts. Instead, it calls for a strategic shift toward business alignment, measurable risk management, and adoption of emerging technologies like AI and machine learning. This evolution reflects growing expectations from boards, executives, and regulators—expectations that CISOs must now meet with business fluency, not just technical insight.
The book goes further by offering actionable guidance, case studies, and real-world examples drawn from extensive experience across hundreds of security programs. It explores practical topics such as risk quantification, cyber insurance, and defining materiality, filling the gap left by more theory-heavy resources.
For aspiring CISOs, the book provides a clear path to transition from technical expertise to strategic leadership. For current CISOs, it delivers fresh insight into strengthening business acumen and boardroom credibility, enabling them to better drive value while protecting organizational assets.
My thought: This book’s strength lies in recognizing that the modern CISO role is no longer just about defending networks but about enabling business resilience and trust. By blending strategy with technical depth, it seems to prepare security leaders for the boardroom-level influence they now require. In an era where cybersecurity is a business risk, not just an IT issue, this perspective feels both timely and necessary.
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AI isn’t just another tool—it’s a paradigm shift. CISOs must now integrate AI-driven analytics into real-time threat detection and incident response. These systems analyze massive volumes of data faster and surface patterns humans might miss.
2. New vulnerabilities from AI use
Deploying AI creates unique risks: biased outputs, prompt injection, data leakage, and compliance challenges across global jurisdictions. CISOs must treat models themselves as attack surfaces, ensuring robust governance.
3. AI amplifies offensive threats
Adversaries now weaponize AI to automate reconnaissance, craft tailored phishing lures or deepfakes, generate malicious code, and launch fast-moving credential‑stuffing campaigns.
4. Building an AI‑enabled cyber team
Moving beyond tool adoption, CISOs need to develop core data capabilities: quality pipelines, labeled datasets, and AI‑savvy talent. This includes threat‑hunting teams that grasp both AI defense and AI‑driven offense.
5. Core capabilities & controls
The playbook highlights foundational strategies:
Data governance (automated discovery and metadata tagging).
Zero trust and adaptive access controls down to file-system and AI pipelines.
AI-powered XDR and automated IR workflows to reduce dwell time.
6. Continuous testing & offensive security
CISOs must adopt offensive measures—AI pen testing, red‑teaming models, adversarial input testing, and ongoing bias audits. This mirrors traditional vulnerability management, now adapted for AI-specific threats.
7. Human + machine synergy
Ultimately, AI acts as a force multiplier—not a surrogate. Humans must oversee, interpret, understand model limitations, and apply context. A successful cyber‑AI strategy relies on continuous training and board engagement .
🧩 Feedback
Comprehensive: Excellent balance of offense, defense, data governance, and human oversight.
Actionable: Strong emphasis on building capabilities—not just buying tools—is a key differentiator.
Enhance with priorities: Highlighting fast-moving threats like prompt‑injection or autonomous AI agents could sharpen urgency.
Communications matter: Reminding CISOs to engage leadership with justifiable ROI and scenario planning ensures support and budget.
AI transforms the cybersecurity role—especially for CISOs—in several fundamental ways:
1. From Reactive to Predictive
Traditionally, security teams react to alerts and known threats. AI shifts this model by enabling predictive analytics. AI can detect anomalies, forecast potential attacks, and recommend actions before damage is done.
2. Augmented Decision-Making
AI enhances the CISO’s ability to make high-stakes decisions under pressure. With tools that summarize incidents, prioritize risks, and assess business impact, CISOs move from gut instinct to data-informed leadership.
3. Automation of Repetitive Tasks
AI automates tasks like log analysis, malware triage, alert correlation, and even generating incident reports. This allows security teams to focus on strategic, higher-value work, such as threat modeling or security architecture.
4. Expansion of Threat Surface Oversight
With AI deployed in business functions (e.g., chatbots, LLMs, automation platforms), the CISO must now secure AI models and pipelines themselves—treating them as critical assets subject to attack and misuse.
5. Offensive AI Readiness
Adversaries are using AI too—to craft phishing campaigns, generate polymorphic malware, or automate social engineering. The CISO’s role expands to understanding offensive AI tactics and defending against them in real time.
6. AI Governance Leadership
CISOs are being pulled into AI governance: setting policies around responsible AI use, bias detection, explainability, and model auditing. Security leadership now intersects with ethical AI oversight and compliance.
7. Cross-Functional Influence
Because AI touches every function—HR, legal, marketing, product—the CISO must collaborate across departments, ensuring security is baked into AI initiatives from the ground up.
Summary: AI transforms the CISO from a control enforcer into a strategic enabler who drives predictive defense, leads governance, secures machine intelligence, and shapes enterprise-wide digital resilience. It’s a shift from gatekeeping to guiding responsible, secure innovation.
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Wineries may not seem like obvious cyber targets, but they hold valuable data—customer and employee details like social security numbers, payment info, and birthdates—that cybercriminals can exploit for identity theft and sell on the dark web. Even business financials are at risk.
Target Clients:
We care for the planet and your data
Wineries invest in luxury branding
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Wineries with 50–1000 employees
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Limited or no in-house IT/security expertise
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🛡️ Why Wineries Are at Risk
Wineries today handle more sensitive data than ever—credit cards, wine club memberships, ecommerce sales, shipping details, and supplier records. Yet many rely on legacy systems, lack dedicated IT teams, and operate in a complex regulatory environment.
Cybercriminals know this. Wineries have become easy, high-value targets.
✅ Our Services
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🎯 Who This Is For
Family-run or boutique wineries with direct-to-consumer operations
Wineries investing in digital growth, but unsure how secure it is
Teams managing POS, ecommerce, club CRMs, M&A and vendor integrations
💡 Why It Matters
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1. Evolving Role of Cybersecurity Services Traditional cybersecurity engagements—such as vulnerability patching, audits, or one-off assessments—tend to be short-term and reactive, addressing immediate concerns without long-term risk reduction. In contrast, end-to-end cybersecurity programs offer sustained value by embedding security into an organization’s core operations and strategic planning. This shift transforms cybersecurity from a technical task into a vital business enabler.
2. Strategic Provider-Client Relationship Delivering lasting cybersecurity outcomes requires service providers to move beyond technical support and establish strong partnerships with organizational leadership. Providers that engage at the executive level evolve from being IT vendors to trusted advisors. This elevated role allows them to align security with business objectives, providing continuous support rather than piecemeal fixes.
3. Core Components of a Strategic Cybersecurity Program A comprehensive end-to-end program must address several key domains: risk assessment and management, strategic planning, compliance and governance, business continuity, security awareness, incident response, third-party risk management, and executive reporting. Each area works in concert to strengthen the organization’s overall security posture and resilience.
4. Risk Assessment & Management A strategic cybersecurity initiative begins with a thorough risk assessment, providing visibility into vulnerabilities and their business impact. A complete asset inventory is essential, and follow-up includes risk prioritization, mitigation planning, and adapting defenses to evolving threats like ransomware. Ongoing risk management ensures that controls remain effective as business conditions change.
5. Strategic Planning & Roadmaps Once risks are understood, the next step is strategic planning. Providers collaborate with clients to create a cybersecurity roadmap that aligns with business goals and compliance obligations. This roadmap includes near-, mid-, and long-term goals, backed by security policies and metrics that guide decision-making and keep efforts aligned with the company’s direction.
6. Compliance & Governance With rising regulatory scrutiny, organizations must align with standards such as NIST, ISO 27001, HIPAA, SOC 2, PCI-DSS, and GDPR. Security providers help identify which regulations apply, assess current compliance gaps, and implement sustainable practices to meet ongoing obligations. This area remains underserved and represents an opportunity for significant impact.
7. Business Continuity & Disaster Recovery Effective security programs not only prevent breaches but also ensure operational continuity. Business Continuity Planning (BCP) and Disaster Recovery (DR) encompass infrastructure backups, alternate operations, and crisis communication strategies. Providers play a key role in building and testing these capabilities, reinforcing their value as strategic advisors.
8. Human-Centric Security & Response Preparedness People remain a major risk vector, so training and awareness are critical. Providers offer education programs, phishing simulations, and workshops to cultivate a security-aware culture. Incident response readiness is also essential—providers develop playbooks, assign roles, and simulate breaches to ensure rapid and coordinated responses to real threats.
9. Executive-Level Communication & Reporting A hallmark of high-value cybersecurity services is the ability to translate technical risks into business language. Clear executive reporting connects cybersecurity activities to business outcomes, supporting board-level decision-making and budget justification. This capability is key for client retention and helps providers secure long-term engagements.
Feedback
This clearly outlines how cybersecurity must evolve from reactive technical support into a strategic business function. The focus on continuous oversight, executive engagement, and alignment with organizational priorities is especially relevant in today’s complex threat landscape. The structure is logical and well-grounded in vCISO best practices. However, it could benefit from sharper differentiation between foundational services (like asset inventories) and advanced advisory (like executive communication). Emphasizing measurable outcomes—such as reduced incidents, improved audit results, or enhanced resilience—would also strengthen the business case. Overall, it’s a strong framework for any provider building or refining an end-to-end security program.
Aaron McCray, Field CISO at CDW, discusses the evolving role of the Chief Information Security Officer (CISO) in the age of artificial intelligence (AI). He emphasizes that CISOs are transitioning from traditional cybersecurity roles to strategic advisors who guide enterprise-wide AI governance and risk management. This shift, termed “CISO 3.0,” involves aligning AI initiatives with business objectives and compliance requirements.
McCray highlights the challenges of integrating AI-driven security tools, particularly regarding visibility, explainability, and false positives. He notes that while AI can enhance security operations, it also introduces complexities, such as the need for transparency in AI decision-making processes and the risk of overwhelming security teams with irrelevant alerts. Ensuring that AI tools integrate seamlessly with existing infrastructure is also a significant concern.
The article underscores the necessity for CISOs and their teams to develop new skill sets, including proficiency in data science and machine learning. McCray points out that understanding how AI models are trained and the data they rely on is crucial for managing associated risks. Adaptive learning platforms that simulate real-world scenarios are mentioned as effective tools for closing the skills gap.
When evaluating third-party AI tools, McCray advises CISOs to prioritize accountability and transparency. He warns against tools that lack clear documentation or fail to provide insights into their decision-making processes. Red flags include opaque algorithms and vendors unwilling to disclose their AI models’ inner workings.
In conclusion, McCray emphasizes that as AI becomes increasingly embedded across business functions, CISOs must lead the charge in establishing robust governance frameworks. This involves not only implementing effective security measures but also fostering a culture of continuous learning and adaptability within their organizations.
Feedback
The article effectively captures the transformative impact of AI on the CISO role, highlighting the shift from technical oversight to strategic leadership. This perspective aligns with the broader industry trend of integrating cybersecurity considerations into overall business strategy.
By addressing the practical challenges of AI integration, such as explainability and infrastructure compatibility, the article provides valuable insights for organizations navigating the complexities of modern cybersecurity landscapes. These considerations are critical for maintaining trust in AI systems and ensuring their effective deployment.
The emphasis on developing new skill sets underscores the dynamic nature of cybersecurity roles in the AI era. Encouraging continuous learning and adaptability is essential for organizations to stay ahead of evolving threats and technological advancements.
The cautionary advice regarding third-party AI tools serves as a timely reminder of the importance of due diligence in vendor selection. Transparency and accountability are paramount in building secure and trustworthy AI systems.
The article could further benefit from exploring specific case studies or examples of organizations successfully implementing AI governance frameworks. Such insights would provide practical guidance and illustrate the real-world application of the concepts discussed.
Overall, the article offers a comprehensive overview of the evolving responsibilities of CISOs in the context of AI integration. It serves as a valuable resource for cybersecurity professionals seeking to navigate the challenges and opportunities presented by AI technologies.
AI is rapidly transforming systems, workflows, and even adversary tactics, regardless of whether our frameworks are ready. It isn’t bound by tradition and won’t wait for governance to catch up…When AI evaluates risks, it may enhance the speed and depth of risk management but only when combined with human oversight, governance frameworks, and ethical safeguards.
A new ISO standard, ISO 42005 provides organizations a structured, actionable pathway to assess and document AI risks, benefits, and alignment with global compliance frameworks.
Increased Regulatory Complexity: With GDPR, CCPA, HIPAA, and emerging regulations like DORA (EU), EU AI Act businesses are seeking specialized compliance partners.
SME Cybersecurity Prioritization: Mid-sized businesses are investing in vCISO services to bridge expertise gaps without hiring full-time CISOs.
Rise of Cyber Insurance: Insurers are demanding evidence of strong compliance postures, increasing demand for third-party audits and vCISO engagements.
Growth Projections
vCISO market is expected to grow at 17–20% CAGR through 2028.
Compliance automation tools, Process orchestration (AI) and advisory services are growing due to demand for cost-effective solutions.
2. Competitor Landscape
Direct Competitors
Virtual CISO Services by Cynomi, Fractional CISO, and SideChannel
Offer standardized packages, onboarding frameworks, and clear SLA-based services.
Differentiate through cost, specialization (e.g., healthcare, fintech), and automation integration.
Indirect Competitors
MSSPs and GRC Platforms like Arctic Wolf, Drata, Vanta
Provide automated compliance dashboards, sometimes bundled with consulting.
Threat: Position as “compliance-as-a-service,” reducing perceived need for vCISO.
3. Differentiation Levers
What Works in the Market
Vertical Specialization: Deep focus on industries like legal, SaaS, fintech, or healthcare adds credibility.
Thought Leadership: Regular LinkedIn posts, webinars, and compliance guides elevate visibility and trust.
Compliance-as-a-Path-to-Growth: Reframing compliance as a revenue enabler (e.g., “SOC 2 = more enterprise clients”) resonates well.
Emerging Niches
vDPO (Virtual Data Protection Officer) in the EU market.
Posture Maturity Consulting for startups seeking Series A or B funding.
Third-Party Risk Management-as-a-Service as vendor scrutiny rises.
4. SWOT Analysis
Strengths
Weaknesses
Deep expertise in InfoSec & compliance
May lack scalability without automation
Custom vCISO engagements
High-touch model limits price elasticity
Opportunities
Threats
Demand surge in SMBs & startups
Commoditization by automated GRC tools
Cross-border compliance needs (e.g., UK GDPR + US laws)
As cyber threats become more frequent and complex, many small and medium-sized businesses (SMBs) find themselves unable to afford a full-time Chief Information Security Officer (CISO). Enter the Virtual CISO (vCISO)—a flexible, cost-effective solution that’s rapidly gaining traction. For Managed Service Providers (MSPs) and Managed Security Service Providers (MSSPs), offering vCISO services isn’t just a smart move—it’s a major business opportunity.
Why vCISO Services Are Gaining Ground
With cybersecurity becoming a top priority across industries, demand for expert guidance is soaring. Many MSPs have started offering partial vCISO services—helping with compliance or risk assessments. But those who provide comprehensive vCISO offerings, including security strategy, policy development, board-level reporting, and incident management, are reaping higher revenues and deeper client trust.
The CISO’s Critical Role
A traditional CISO wears many hats: managing cyber risk, setting security strategies, ensuring compliance, and overseeing incident response and vendor risk. They also liaise with leadership, align IT with business goals, and handle regulatory requirements like GDPR and HIPAA. With experienced CISOs in short supply and expensive to hire, vCISOs are filling the gap—especially for SMBs.
Why MSPs Are Perfectly Positioned
Most SMBs don’t have a dedicated internal cybersecurity leader. That’s where MSPs and MSSPs come in. Offering vCISO services allows them to tap into recurring revenue streams, enter new markets, and deepen client relationships. By going beyond reactive services and offering proactive, executive-level security guidance, MSPs can differentiate themselves in a crowded field.
Delivering Full vCISO Services: What It Takes
To truly deliver on the vCISO promise, providers must cover end-to-end services—from risk assessments and strategy setting to business continuity planning and compliance. A solid starting point is a thorough risk assessment that informs a strategic cybersecurity roadmap aligned with business priorities and budget constraints.
It’s About Action, Not Just Advice
A vCISO isn’t just a strategist—they’re also responsible for guiding implementation. This includes deploying controls like MFA and EDR tools, conducting vulnerability scans, and ensuring backups and disaster recovery plans are robust. Data protection, archiving, and secure disposal are also critical to safeguarding digital assets.
Educating and Enabling Everyone
Cybersecurity is a team sport. That’s why training and awareness programs are key vCISO responsibilities. From employee phishing simulations to executive-level briefings, vCISOs ensure everyone understands their role in protecting the business. Meanwhile, increasing compliance demands—from clients and regulators alike—make vCISO support in this area invaluable.
Planning for the Worst: Incident & Vendor Risk Management
Every business will face a cyber incident eventually. A strong incident response plan is essential, as is regular practice via tabletop exercises. Additionally, third-party vendors represent growing attack vectors. vCISOs are tasked with managing this risk, ensuring vendors follow strict access and authentication protocols.
Scale Smart with Automation
With the rise of automation and the widespread emergence of agentic AI, are you prepared to navigate this disruption responsibly? Providing all these services can be daunting—especially for smaller providers. That’s where platforms like Cynomi come in. By automating time-consuming tasks like assessments, policy creation, and compliance mapping, Cynomi enables MSPs and MSSPs to scale their vCISO services without hiring more staff. It’s a game-changer for those ready to go all-in on vCISO.
Conclusion: Delivering full vCISO services isn’t easy—but the payoff is big. With the right approach and tools, MSPs and MSSPs can offer high-value, scalable cybersecurity leadership to clients who desperately need it. For those ready to lead the charge, the time to act is now.
​The RSA Conference Executive Security Action Forum (ESAF) report, How Top CISOs Are Transforming Third-Party Risk Management, presents insights from Fortune 1000 Chief Information Security Officers (CISOs) on evolving strategies to manage third-party cyber risks. The report underscores the inadequacy of traditional risk management approaches and highlights innovative practices adopted by leading organizations.​
1. Escalating Third-Party Risks
The report begins by emphasizing the increasing threat posed by third-party relationships. A survey revealed that 87% of Fortune 1000 companies experienced significant cyber incidents originating from third parties within a year. This statistic underscores the urgency for organizations to reassess their third-party risk management strategies.​
2. Limitations of Traditional Approaches
Traditional methods, such as self-assessment questionnaires and cybersecurity ratings, are criticized for their ineffectiveness. These approaches often lack context, fail to reduce actual risk, and do not foster resilience against cyber threats. The report advocates for a shift towards more proactive and context-aware strategies.​
3. Innovative Strategies by Leading CISOs
In response to these challenges, top CISOs are implementing bold new approaches. These include establishing prioritized security requirements, setting clear deadlines for control implementations, incorporating enforcement clauses in contracts, and assisting third parties in acquiring necessary security technologies and services. Such measures aim to enhance the overall security posture of both the organization and its partners.​
4. Emphasizing Business Leadership and Resilience
The report highlights the importance of involving business leaders in managing cyber risks. By integrating cybersecurity considerations into business decisions and fostering a culture of resilience, organizations can better prepare for and respond to third-party incidents. This holistic approach ensures that cybersecurity is not siloed but is a shared responsibility across the enterprise.​
5. Case Studies Demonstrating Effective Practices
Six cross-sector case studies are presented, showcasing how organizations in industries like defense, healthcare, insurance, manufacturing, and technology are successfully transforming their third-party risk management. These real-world examples provide valuable insights into the practical application of the recommended strategies and their positive outcomes.​
6. The Role of Technology and Security Vendors
The report calls upon technology and security vendors to play a pivotal role in minimizing complexities and reducing costs associated with third-party risk management. By collaborating with organizations, vendors can develop solutions that are more aligned with the evolving cybersecurity landscape and the specific needs of businesses.​
7. Industry Collaboration for Systemic Change
Recognizing that third-party risk is a widespread issue, the report advocates for industry-wide collaboration. Establishing common standards, sharing best practices, and engaging in joint initiatives can lead to systemic changes that enhance the security of the broader ecosystem. Such collective efforts are essential for addressing the complexities of modern cyber threats.​
8. Moving Forward with Proactive Measures
The ESAF report concludes by encouraging organizations to adopt proactive measures in managing third-party risks. By moving beyond traditional methods and embracing innovative, collaborative, and resilient strategies, businesses can better safeguard themselves against the evolving threat landscape. The insights provided serve as a roadmap for organizations aiming to strengthen their cybersecurity frameworks in partnership with their third parties.​
