AI Cybersecurity Fundamentals: The Convergence You Need to Master
By Patrick Dasoberi, CISA, CDPSE, MSc IT | Former CTO, CarePoint | Founder, AI Cybersecurity & Compliance Hub
The Problem Nobody's Talking About
I've sat through dozens of AI product demos over the past three years. Brilliant engineers showcasing impressive machine learning models, sophisticated algorithms, and cutting-edge capabilities. Then I ask a simple question: "How are you handling adversarial attacks on your training data?" Silence. Or my personal favourite: "What's your compliance framework for processing personal data through this AI system under Ghana's Data Protection Act 843?"
Here's what I learned as CTO managing healthcare systems across Ghana, Nigeria, Kenya, and Egypt—and now operating AI-powered platforms across Ghana, Nigeria, and South Africa: The AI industry has a dangerous blind spot.
Vendors excel at building sophisticated AI systems but lack depth in cybersecurity and compliance. Security professionals understand threats but don't grasp AI-specific vulnerabilities. Compliance experts know regulations but struggle with AI's technical implications.
Nobody teaches all three together. University courses separate them. Certifications silo them. The market forces you to learn them independently, then somehow figure out how they intersect.
This creates systems with brilliant AI—and gaping security holes.
I've seen it firsthand—both as CTO responsible for multi-country healthcare systems and operating current AI platforms. AI models trained on poisoned data. Patient information exposed through model inversion attacks. Systems that violate GDPR and don't even know it. Healthcare AI deployed without business associate agreements. Brilliant technology. Terrible security. Worse compliance.
This is why I built AI Security Info: to bring these three critical areas together in a way that makes sense for practitioners building real systems.
Why This Convergence Matters Now
The market is heading toward a reality where AI, cybersecurity, and compliance aren't separate disciplines—they're inseparable requirements.
Consider what's happening:
Regulatory Pressure is Intensifying
You can't build compliant AI without understanding both the technology AND the regulatory frameworks.
AI-Specific Threats Are Evolving
Traditional cybersecurity tools don't detect these attacks. You need AI-aware security approaches.
Business Risk is
Escalating
Based on my experience operating healthcare AI platforms, the organisations that thrive will be those that master the convergence of AI capabilities, security resilience, and regulatory compliance. The ones that treat these as separate problems will fail—slowly through erosion of trust, or quickly through regulatory enforcement.
The Three-Circle Framework: Where AI Security Lives
Through my work at the intersection of AI development, information security auditing (CISA), and data privacy engineering (CDPSE), I've developed a framework for thinking about AI cybersecurity:
Circle 1: AI Technology Understanding
You must understand what AI actually IS before you can secure it.
This isn't about becoming a data scientist. It's about understanding:
- How machine learning models learn from data
- What training data actually contains and reveals
- How models make predictions and decisions
- Where AI systems differ fundamentally from traditional software
Why this matters: You can't secure what you don't understand. When a vendor demos an AI system, you need to ask intelligent questions about model architecture, training methodology, data handling, and inference processes.
Real example: I once reviewed an AI diagnostic tool that claimed to be "fully secure." When I asked about their training data provenance, they had no documentation. No chain of custody. No verification that patient data used for training had proper consent. Brilliant AI. Complete compliance disaster.
Circle 2: Cybersecurity Principles Applied to AI
Traditional security approaches don't translate directly to AI systems.
Yes, you still need encryption, access controls, and network security. But AI introduces entirely new attack surfaces:
Data Security (Beyond Storage)
- Training data integrity: Can attackers poison your training data?
- Inference data protection: Are user queries revealing sensitive information?
- Model outputs: Can responses be used to infer private training data?
Model Security (New Territory)
- Adversarial robustness: How resilient is your model to manipulated inputs?
- Model extraction protection: Can attackers steal your model through API queries?
- Intellectual property: How do you protect your model architecture and weights?
Operational Security (Different Dynamics)
- Continuous learning: If your model updates from production data, how do you prevent poisoning?
- A/B testing: Are you inadvertently exposing model vulnerabilities?
- Monitoring: What does "normal" look like for AI system behavior?
From my experience as CTO and current platform operations: We had to completely rethink our security monitoring for AI systems. Traditional SIEM alerts weren't catching adversarial probing. We needed AI-aware security tools that understood model behavior patterns.
Circle 3: Compliance in the AI Era
AI complicates compliance in ways regulations didn't anticipate.
Data Protection Regulations (GDPR, Ghana Act 843, CCPA)
- Right to erasure: How do you delete training data from a learned model?
- Purpose limitation: Is using customer data to improve AI "compatible processing"?
- Transparency: Can you explain AI decision-making to data subjects?
Sector-Specific Regulations (HIPAA, FERPA)
- PHI protection: Does your AI properly de-identify patient data?
- Business Associate Agreements: Do your AI vendors understand their obligations?
- Security Rule compliance: Are your AI systems meeting required safeguards?
AI-Specific Regulations (EU AI Act)
- Risk classification: Is your AI system "high-risk" under regulatory definitions?
- Documentation requirements: Can you demonstrate model provenance and testing?
- Human oversight: Do you have appropriate human-in-the-loop controls?
Operating across three African countries taught me: Compliance isn't just about following rules. It's about understanding regulatory intent and building systems that respect fundamental rights—even when regulations don't specifically mention AI.
The Sweet Spot: Where All Three Intersect
This is where real AI security happens. This is where I operate. This is what makes modern applications actually secure.
At this intersection, you can:
- Build AI systems that are technically sophisticated AND secure
- Meet regulatory requirements without sacrificing functionality
- Implement security controls that align with compliance obligations
- Understand risks from technical, security, AND legal perspectives
- Make informed decisions about AI deployment
Without this convergence: You build impressive systems that can't be deployed safely or legally.
What You'll Find in This Pillar
This pillar brings together everything you need to understand AI cybersecurity from all three critical angles: technical understanding, security implementation, and compliance navigation.
🧠 Foundation: Understanding AI Security
Why it matters: You can't secure AI without understanding how it works.
What you'll learn:
- What makes AI different from traditional software security
- Core concepts: machine learning, deep learning, neural networks
- How AI models learn, predict, and can be attacked
- The unique threat landscape for AI systems
Start here if: You're transitioning from traditional cybersecurity or new to AI security.
My perspective: Too many security professionals dismiss AI as "just another application." It's not. The attack surfaces are fundamentally different. Start by understanding what makes AI special.
→ Introduction to AI in Cybersecurity
→ How AI Works in Cybersecurity
→ Machine Learning for Cybersecurity
🛡️ AI-Powered Defense: Using AI for Security
Why it matters: AI isn't just a target—it's also a powerful security tool.
What you'll learn:
- Threat detection and prevention with AI
- Automated malware analysis and classification
- Behavioral analytics and anomaly detection
- AI-enhanced incident response
From my experience as CTO and current platform operations, AI-powered threat detection has been transformative for our healthcare platforms. We catch anomalies that would slip through traditional rules-based systems. But you need to understand both the capabilities AND the limitations.
The compliance angle: When using AI for security monitoring, you still need to comply with data protection regulations. Log analysis, user behaviour monitoring—these create privacy implications.
→ AI Threat Detection & Prevention
→ AI-Powered Malware Detection
→ AI Behavioral Analytics
🔒 Securing AI Systems: Protecting the Technology Itself
Why it matters: Your AI models are valuable assets and attack targets.
What you'll learn:
- Adversarial attacks and defenses
- Training data security and integrity
- Model extraction and intellectual property protection
- Secure AI development lifecycle
Real talk: This is where most vendors fail. They focus on what their AI does, not on how attackers might exploit it. I've seen models compromised through training data manipulation, extracted through API abuse, and manipulated through adversarial inputs.
The compliance angle: If your AI processes personal data, model security becomes a data protection requirement. Model inversion attacks can expose training data—which might be protected health information or personally identifiable information.
→ Machine Learning Security
→ Securing AI Infrastructure
→ AI Vulnerability Management
📊 Artificial Intelligence Security
Operations
Why it matters: AI transforms how security teams operate.
What you'll learn:
- AI-enhanced Security Operations Centers (SOC)
- Automated threat intelligence analysis
- AI-powered incident response
- Security automation and orchestration (SOAR)
From running healthcare platforms, AI has made our small security team dramatically more effective. We use AI for alert triage, threat correlation, and automated response. But human oversight remains critical—AI augments security teams, it doesn't replace them.
The compliance angle: Automated security decisions can have legal implications. If your AI blocks a legitimate user or quarantines critical data, you need clear policies and human oversight mechanisms.
→ AI Security Operations Center (SOC)
→ AI Threat Intelligence
→ AI Incident Response
🏥 Domain-Specific AI Security
Why it matters: Different industries have unique AI security challenges.
What you'll learn:
- Healthcare AI and HIPAA compliance
- Financial services AI and fraud detection
- IoT and edge AI security
- Cloud and API security for AI systems
My unique perspective: Operating healthcare AI across three countries has taught me that sector-specific regulations dramatically impact AI security architecture. You can't just bolt compliance onto existing systems—it needs to be designed in from day one.
Key considerations:
- Healthcare: PHI protection, Business Associate Agreements, de-identification
- Financial: PCI-DSS compliance, fraud detection, model explainability
- IoT: Edge security, limited resources, device authentication
- Cloud: Multi-tenancy, data residency, regulatory compliance
→ AI Application Security
→ AI IoT Security
→ AI Cloud Security
🌍 Navigating the Regulatory Landscape
Why it matters: AI regulations are complex and rapidly evolving.
What you'll learn:
- GDPR implications for AI systems
- Ghana Data Protection Act 843 and AI
- EU AI Act requirements
- Sector-specific regulations (HIPAA, FERPA)
- African regulatory landscape
Operating across Ghana, Nigeria, and South Africa: I've learned that regulatory compliance isn't just about checking boxes. It's about understanding the spirit of privacy laws and building systems that respect individual rights. Different jurisdictions have different priorities, but the principles converge: transparency, accountability, fairness, security.
This directly connects to my Data Privacy & AI pillar, where I go deeper on regulatory compliance. But understanding the security implications of compliance requirements is critical for AI cybersecurity.
→ Related: Data Privacy & AI Pillar
→ Related: AI Regulatory Compliance Pillar
The Common Mistakes I See
Through reviewing AI systems and talking with vendors, I see the same mistakes repeatedly:
Mistake 1: "We're Using Encryption, So We're Secure"
Encryption protects data at rest and in transit. It does nothing for:
- Adversarial attacks manipulating model behavior
- Training data poisoning
- Model extraction through API abuse
- Inference-time privacy leaks
Better approach: Layer security controls specific to AI threats on top of traditional security.
Mistake 2: "Our Model is Proprietary, So It's Protected"
Obscurity isn't security. Attackers can:
- Extract model functionality through systematic querying
- Reverse engineer model architecture from behavior
- Steal model weights from insecure storage
Better approach: Assume attackers understand your model architecture. Implement actual protection mechanisms.
Mistake 3: "We'll Handle Compliance Later"
By the time you're ready to deploy, "later" means expensive refactoring or abandoning the project.
Real example: I reviewed an AI diagnostic tool that would require complete re-architecture to comply with HIPAA. Millions invested. Can't legally deploy. It could have been designed right from day one.
Better approach: Integrate compliance requirements into AI system design from the start.
Mistake 4: "Traditional Security Tools Work Fine for AI"
Your SIEM won't catch adversarial probing. Your firewall won't stop model extraction. Your vulnerability scanner doesn't understand AI-specific weaknesses.
Better approach: Augment traditional security with AI-aware tools and processes.
Mistake 5: "We Don't Process Sensitive Data"
Your AI model might not process sensitive data directly, but:
- Training data might have contained sensitive information
- Model can memorize and leak training data
- Inference patterns might reveal sensitive insights
- Aggregated outputs might enable re-identification
Better approach: Assume AI processing has privacy implications and implement appropriate controls.
Getting Started: Your Path Forward
For Security Professionals New to AI
Start here: Learn how AI systems work at a fundamental level. You don't need to become a data scientist, but you need to understand:
- 1. Introduction to AI in Cybersecurity—Foundation concepts
- 2. How AI Works in Cybersecurity—Technical understanding
- 3. AI Threat Detection & Prevention—Apply AI for defence
Why this order: Build conceptual understanding before jumping into tools and techniques.
For AI Developers & Data Scientists
Start here: Understand the security and compliance implications of your work. Your brilliant model can't deploy if it violates regulations or creates unacceptable risks.
Recommended path:
Why this order: Secure your core AI assets, then understand broader compliance context.
For Compliance & Risk Professionals
Start here: Learn what makes AI different from a compliance perspective. Traditional frameworks don't directly translate.
Recommended path:
Why this order: Build technical literacy before tackling complex compliance questions.
For African Market Focus
Start here: If you're operating or building AI systems in Ghana, Nigeria, South Africa, or elsewhere in Africa, pay special attention to:
My perspective: The African AI market is growing rapidly. Security and compliance can't be afterthoughts. Build them in from day one, and you'll have a competitive advantage.
Recommended path:
- Introduction to AI in Cybersecurity - Foundation
- Data Privacy & AI - Includes Ghana Act 843 coverage
- AI Regulatory Compliance - Regional focus
For Healthcare AI Specifically
Start here: Healthcare combines strict regulations, sensitive data, and life-or-death consequences. AI security isn't optional.
Recommended path:
From my platforms: Running AI healthcare systems across three countries has taught me that security and compliance aren't barriers to innovation—they're foundations for trustworthy AI that actually helps patients.
Patrick Dasoberi's AI security credentials showing CTO experience, teaching background, certifications, and unique market positioning
Caption: Why My Perspective on AI Security is Different
1. I've Led Healthcare Technology at Executive Level
As CTO of CarePoint (formerly African Health Holding), I was responsible for healthcare systems operating across four countries: Ghana, Nigeria, Kenya, and Egypt. This wasn't theoretical work—I made strategic technology decisions affecting real patient data across multiple regulatory jurisdictions.
I've navigated HIPAA compliance, Ghana's Data Protection Act 843, Nigeria's NDPR, and Kenya's Data Protection Act—not as an academic exercise, but as operational requirements for production systems. I understand what it takes to build AI systems that actually work in complex, multi-jurisdictional environments.
2. I've Taught Complex Technical Concepts for 7 Years
Before moving into healthcare technology leadership, I taught web development and Java programming in Ghana for seven years. I've stood in front of hundreds of students and learned how to break down complex technical concepts into understandable, actionable knowledge.
This teaching experience shapes how I create content. I don't just know AI security—I know how to explain it in ways that make sense to people transitioning from traditional security, developers adding AI to applications, and compliance professionals navigating new regulations.
3. I Operate at the Intersection Daily
I'm not a security person who dabbles in AI, or an AI engineer who took a security course, or a compliance expert reading about technology. I work at the convergence of AI technology, cybersecurity, and regulatory compliance every single day.
My CISA certification gives me the auditor's perspective on security controls. My CDPSE certification grounds me in privacy engineering. My MSc in Information Technology and AI/ML training (including RAG systems) give me technical depth. My CTO experience forced me to make these all work together in production.
4. I Know African Markets From Direct Experience
Most AI security content assumes Western infrastructure, mature regulatory environments, and abundant resources. I've built and operated AI-powered healthcare platforms (DiabetesCare.Today, MyClinicsOnline, BlackSkinAcne.com) across Ghana, Nigeria, and South Africa.
I understand infrastructure constraints, regulatory variations, local threat landscapes, and the unique challenges of emerging markets. When I write about AI security in African contexts, it's from direct operational experience, not theoretical extrapolation.
5. I've Made the Expensive Mistakes
I've dealt with actual security incidents, real compliance audits, failed vendor implementations, and the messy reality of operating AI systems serving real patients with real health data under real regulatory scrutiny.
I've learned from failures. I've had to refactor systems to meet compliance requirements we initially missed. I've seen what vendors get right, what they miss, and what questions separate real AI security from security theater. I share these lessons so you don't have to learn them the expensive way.
The Content You'll Find Here
I'm building a comprehensive knowledge base at this intersection of AI, cybersecurity, and compliance. This takes time to do properly—I'm writing from experience, not just compiling information.
What's Available Now:
- 1. Foundation articles on AI security fundamentals
- 2. Deep dives into specific AI threats and defences
- 3. Regulatory guidance for AI systems
- 4. Healthcare-specific AI security content
- 5. African market compliance insights
What's Coming: I'm systematically building out all major topic areas. Each article is grounded in practical experience and includes real-world examples, implementation guidance, and compliance considerations.
I'm committed to quality over speed. I'd rather publish fewer articles that genuinely help you than flood you with generic content.
How This Content is Different
Not Generic AI Security Content:
Most AI security content is either too theoretical (academic papers) or too shallow (vendor marketing). I aim for the middle: technically sound, practically actionable, and honestly written.
Not Separated Silos:
I don't write about AI security without addressing compliance implications. I don't discuss compliance requirements without explaining technical implementation. Everything connects because, in reality, everything IS connected.
Not Western-Centric:
While I cover global regulations and frameworks, I include specific insights for African markets, emerging economies, and resource-constrained environments.
Not Sales Pitches:
I'm building a knowledge resource, not a lead generation funnel. When I recommend tools or approaches, it's because they work, not because I have affiliate deals.
Beyond This Pillar
AI Cybersecurity Fundamentals is just one piece of the puzzle. The other six pillars provide deeper dives into specific areas:
AI Cybersecurity Fundamentals is just one piece of the puzzle. The other six pillars provide deeper dives into specific areas:
Connect & Learn More
This is an ongoing journey. AI security is evolving rapidly. Regulations are developing. Threats are emerging. I'm learning constantly and sharing what I discover.
Ways to engage:
- Read systematically: Work through topics in order for structured learning
- Jump to interests: Go directly to areas most relevant to your work
- Stay updated: New content published regularly
- Ask questions: Reach out with specific challenges you're facing
About AI Security Info: This platform is my commitment to making AI security knowledge accessible, practical, and grounded in real-world experience. Whether you're building AI systems, securing them, or ensuring compliance, you'll find insights here that you won't find elsewhere.
Ready to dive deeper?
Browse the comprehensive topic areas below, or start with the recommended learning path for your role.
The timer has expired!
Explore AI Cybersecurity Topics
Below you'll find the major topic areas within AI cybersecurity. Each represents a critical domain of knowledge. Topics are organized from foundational to advanced, but feel free to explore based on your needs.
Note: Content is being developed systematically. Articles marked as "Coming Soon" are in development. Existing articles are linked and ready to read.
🧠 Core AI Concepts for Security
Foundation concepts for understanding AI security
Understanding what AI is, how it differs from traditional software, and why it requires different security approaches. Start here if you're new to AI security.
Key topics: AI definitions, machine learning basics, why AI security matters, market overview, career paths
Skill level: Beginner
Content status: Building out comprehensive coverage
Technical deep dive into AI mechanisms
Understanding the technical processes that enable AI-powered security: pattern recognition, anomaly detection, automated response, and threat prediction.
Key topics: AI algorithms in security, threat detection mechanisms, automated response systems, and pattern recognition.
Skill level: Intermediate
Content status: Coming soon
Machine Learning for Cybersecurity
ML techniques applied to security challenges
Supervised, unsupervised, and reinforcement learning techniques specifically for security use cases. Understanding false positives, model training, and threat prediction.
Key topics: ML algorithms, model training for security, handling false positives, prediction accuracy
Skill level: Intermediate
Content status: Coming soon
🛡️ AI-Powered Defence Systems
AI Threat Detection & Prevention
Using AI to identify and stop threats
- Real-time threat detection, predictive threat intelligence, and proactive security strategies powered by AI systems.
Key topics: Threat hunting, risk assessment, early warning systems, insider threat detection
Skill level: Intermediate
Content status: Coming soon
AI-Powered Malware Detection
Behavioural analysis for malware identification
Detecting viruses, trojans, ransomware, and polymorphic malware using AI-driven behaviouural analysis instead of signature-based approaches.
Key topics: Ransomware detection, polymorphic malware, sandbox analysis, behavioral signatures
Skill level: Intermediate
Content status: Coming soon
AI Phishing Detection & Prevention
Stopping social engineering attacks
- Combating email phishing, SMS phishing, and voice phishing using AI-powered content analysis, sender verification, and behavioural patterns.
Key topics: Spear phishing, URL analysis, BEC detection, social engineering patterns
Skill level: Intermediate
Content status: Coming soon
🔒 Securing AI Infrastructure
AI Network Security
Protecting network infrastructure with AI
- AI-powered network monitoring, traffic analysis, intrusion detection, and DDoS prevention for modern network environments.
Key topics: NIDS/NIPS with AI, DDoS detection, Zero Trust architecture, traffic analysis
Skill level: Intermediate
Content status: Coming soon
AI Endpoint Security
Device protection and EDR
- Protecting endpoints with AI-driven EDR, behavioral analysis, and automated threat response across diverse device types.
Key topics: EDR platforms, device management, BYOD security, IoT endpoint protection
Skill level: Intermediate
Content status: Coming soon
AI Cloud Security
Securing multi-cloud environments
- AI-powered CASB, CSPM, and workload protection for complex multi-cloud and hybrid environments.
Key topics: CASB, CSPM, container security, serverless security
Skill level: Intermediate
Content status: Coming soon
AI Identity & Access Management
Authentication and authorization
- Strengthening authentication with AI-powered biometrics, behavioral analysis, privileged access management, and identity governance.
Key topics: Biometric authentication, MFA, PAM, RBAC, UEBA
Skill level: Intermediate
Content status: Coming soon
📊 Advanced Analytics & Detection
AI Behavioral Analytics
Detecting anomalous behavior
- Analyzing user and entity behavior to detect insider threats, compromised accounts, and anomalous patterns indicating security incidents.
Key topics: UEBA platforms, baseline modeling, risk scoring, peer group analysis
Skill level: Advanced
Content status: Coming soon
AI Anomaly Detection
statistical and ML-based detection
- Identifying unusual patterns across systems, networks, and applications using statistical methods, clustering, and time-series analysis.
Key topics: Statistical methods, clustering algorithms, time-series analysis, outlier detection
Skill level: Advanced
Content status: Coming soon
⚙️ Security Operations & Automation
AI Security Automation
SOAR and orchestration
- Automating security operations with SOAR platforms, playbooks, intelligent orchestration, and automated remediation.
Key topics: SOAR platforms, security playbooks, automated remediation, alert triage
Skill level: Intermediate
Content status: Coming soon
AI Threat Intelligence
Intelligence gathering and analysis
- AI-powered threat intelligence correlation, enrichment, and operationalisation from diverse sources, including OSINT and the dark web.
Key topics: TTP analysis, IoC detection, OSINT automation, dark web monitoring
Skill level: Intermediate
Content status: Coming soon
AI Incident Response
Automated investigation and response
- Streamlining incident detection, investigation, and response with AI-enhanced SIEM, forensics, and automated workflows.
Key topics: SIEM with AI, digital forensics, root cause analysis, response playbooks
Skill level: Intermediate
Content status: Coming soon
AI Vulnerability Management
Risk-based vulnerability prioritisation
- Discovering, assessing, and prioritizing vulnerabilities using AI-driven scanning, risk scoring, and predictive analytics.
Key topics: Vulnerability scanning, CVSS scoring, patch management, zero-day prediction
Skill level: Intermediate
Content status: Coming soon
AI Penetration Testing
Automated testing and red teaming
- Using AI to automate reconnaissance, exploitation, and red team operations while maintaining human oversight.
Key topics: Red team automation, attack simulation, exploitation techniques, purple teaming
Skill level: Advanced
Content status: Coming soon
AI Security Operations Center (SOC)
AI-enhanced SOC operations
- Transforming SOC operations with AI analyst augmentation, alert correlation, workflow automation, and 24/7 capabilities.
Key topics: SOC automation, alert triage, MDR services, continuous operations
Skill level: Intermediate
Content status: Coming soon
💼 Specialised Applications
AI Fraud Detection
Financial fraud prevention
- Preventing payment fraud, identity fraud, and transaction fraud using AI-powered behavioral analytics and risk scoring.
Key topics: Payment fraud, AML, KYC processes, account takeover detection
Skill level: Intermediate
Content status: Coming soon
Part 2
AI Data Loss Prevention
Protecting sensitive data
AI-driven data classification, monitoring, and exfiltration prevention to protect sensitive and regulated information.
Key topics: Data classification, exfiltration detection, content inspection, policy management
Skill level: Intermediate
Content status: Coming soon
AI Application Security
Securing applications and code
- AI-powered SAST, DAST, IAST, and automated vulnerability detection in application code and runtime environments.
Key topics: SAST/DAST, code review automation, DevSecOps, WAF
Skill level: Intermediate
Content status: Coming soon
AI API Security
Protecting APIs
Securing APIs from threats with AI-driven authentication, rate limiting, anomaly detection, and bot protection.
Key topics: API gateways, injection attacks, bot detection, rate limiting
Skill level: Intermediate
Content status: Coming soon
AI IoT Security
Device and edge security
- Securing IoT devices and industrial systems with AI-powered threat detection, device management, and edge protection.
Key topics: IIoT security, smart home protection, botnet detection, edge computing security
Skill level: Intermediate
Content status: Coming soon
AI Mobile Security
Mobile device protection
- Protecting mobile devices and applications with AI-driven malware detection, MDM, and behavioral analytics.
Key topics: MDM/MAM, Android/iOS security, mobile phishing, app security
Skill level: Intermediate
Content status: Coming soon
AI Email Security
Email threat protection
- Defending against email threats with AI-powered spam filtering, BEC detection, content analysis, and impersonation detection.
Key topics: BEC detection, spam filtering, SPF/DKIM/DMARC, impersonation attacks
Skill level: Intermediate
Content status: Coming soon
AI Database Security
Database protection
- Protecting databases with AI-powered activity monitoring, anomaly detection, access control, and audit logging.
Key topics: Database activity monitoring, SQL injection prevention, encryption, audit logging
Skill level: Intermediate
Content status: Coming soon
AI Security Monitoring & Logging
Event analysis and correlation
- Monitoring security events with AI-enhanced SIEM, log analysis, real-time correlation, and intelligent dashboards.
Key topics: SIEM platforms, log analysis, event correlation, security dashboards
Skill level: Intermediate
Content status: Coming soon
🎓 Building Security Culture
AI Security Training & Awareness
Human element of security
- Building security culture with AI-personalized training, phishing simulations, adaptive learning, and gamification.
Key topics: Awareness programs, phishing simulation, gamification, security certifications
Skill level: All levels
Content status: Coming soon
Start Your AI Security Journey
You're at the beginning of understanding one of the most important technological convergences of our time: where artificial intelligence meets cybersecurity and regulatory compliance.
This isn't just about adding another skill. It's about positioning yourself at the intersection of three critical domains that are reshaping how we build, secure, and deploy technology.
The market is heading this way. Organisations are realising they can't separate AI capabilities from security requirements and compliance obligations. The professionals who master this convergence will be the ones building the future.
I'm building this resource systematically, grounded in real experience. Every article reflects lessons learned from operating AI systems in production, navigating complex regulations, and dealing with actual security challenges.
Start exploring. Pick a topic that interests you. Read with a critical eye. Apply what makes sense for your context. And remember: the goal isn't to memorise everything—it's to develop the mental models that let you think clearly about AI security challenges.
Welcome to the intersection. Let's build secure AI systems together.
Patrick D. Dasoberi
CISA, CDPSE, MSc IT, BA Admin, AI/ML Engineer Former CTO, CarePoint | Founder, AI Cybersecurity & Compliance Hub
Patrick Dasoberi brings a rare combination of executive healthcare technology leadership, technical depth, and hands-on teaching experience to AI security education.
Executive Healthcare Technology Leadership
Until recently, Patrick served as Chief Technology Officer of CarePoint (formerly African Health Holding), where he was responsible for healthcare systems operating across four countries: Ghana, Nigeria, Kenya, and Egypt. In this role, he navigated complex multi-jurisdictional regulatory requirements including HIPAA, Ghana's Data Protection Act 843, Nigeria's NDPR, and Kenya's Data Protection Act—making strategic technology decisions that affected real patient data across diverse regulatory environments.
Technical Education Background
Before moving into healthcare technology leadership, Patrick taught web development and Java programming in Ghana for seven years. This extensive teaching experience shapes his approach to content creation—he doesn't just understand AI security deeply, he knows how to explain complex technical concepts in ways that make them accessible and actionable for practitioners.
Current Operations & Focus
Patrick currently operates AI-powered healthcare platforms including DiabetesCare.Today, MyClinicsOnline, and BlackSkinAcne.com across Ghana, Nigeria, and South Africa. Through AI Security Info and the AI Cybersecurity & Compliance Hub, he shares practical insights from building, securing, and operating real-world AI systems under complex regulatory environments.
His unique expertise sits at the intersection of AI technology, cybersecurity, and regulatory compliance—three areas typically taught separately but inseparable in practice. His work focuses on making AI security knowledge accessible to practitioners, with particular attention to African markets and healthcare applications where he has direct operational experience.
Professional Certifications & Education:
- CISA (Certified Information Systems Auditor)
- CDPSE (Certified Data Privacy Solutions Engineer)
- MSc Information Technology, University of the West of England
- BA Administration
- Postgraduate AI/ML Training (RAG Systems)
Executive & Operational Experience:
- Former CTO: CarePoint (healthcare systems across Ghana, Nigeria, Kenya, Egypt)
- Teaching: 7 years teaching web development and Java programming in Ghana
- Current Founder: AI Cybersecurity & Compliance Hub
- Current Operator: AI healthcare platforms across Ghana, Nigeria, South Africa
- Focus Areas: Healthcare AI, African markets, Security + Compliance convergence
