🗓️ This Week

• Made a little more progress in the SwiftUI tutorial.
• Started researching web technology stacks and creating a roadmap to gradually turn my current blog into a portfolio site using React Router v7. I'm moving forward little by little.
• Completed the AI Forensics room from the AI Security Learning Path on TryHackMe this week.

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💡 Thoughts on the AI Forensics Room

The AI Forensics room was much deeper and more challenging than I initially expected, and each task took a significant amount of time to complete. However, it turned out to be an incredibly valuable learning experience 🔥

Task 5, "Practical - The Digital Trail," was especially impressive. It was a story-driven hands-on investigation where I analyzed a compromised company environment after attackers stole critical proprietary source code 🔎

Instead of simply reading explanations, I had to actively investigate logs, suspicious files, reverse shells, persistence mechanisms, and data exfiltration activity step by step.

Because I was constantly thinking, investigating, and connecting the dots myself, the experience felt far more practical and realistic. It gave me a much deeper understanding of how AI-assisted DFIR investigations work in real-world scenarios.

One of the biggest lessons I learned from this room was that AI is a powerful tool, but human insight is still essential. AI can quickly detect suspicious activity, but investigators still need to analyze the context and validate the findings themselves.

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📱 iOS (SwiftUI)

• Worked through the SwiftUI tutorial and completed Sections 6 and 7: "Create an Algorithm for Badges"

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🌐 Web Development

• Posted my weekly dev log on Dev.to 📝
• Researched how to transform my current blog site into the portfolio site I want to build in the future.
• Created a roadmap with ChatGPT to gradually turn my blog site into a portfolio site.

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🔐 Security (TryHackMe)

• Completed the AI Forensics room (part of the AI Security Learning Path) on TryHackMe.

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💡 Key Takeaways

📱 SwiftUI Review

• Reviewed how SwiftData automatically creates relationships between @Model objects through model properties.
• Understood how bidirectional relationships work between Moment and Badge models.
• Learned that Group is useful when applying modifiers to conditionally displayed views.
• Realized that SwiftUI modifiers can only be attached to actual views, not directly to an if statement.
• Reviewed how @ViewBuilder allows a custom SwiftUI view to accept and display child views passed from outside.
• Understood that .offset(y:) moves a view from its original position to fine-tune layouts.

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🔐 TryHackMe Learning

AI Forensics Task 3 — AI & DFIR

• Learned how AI and machine learning are transforming modern DFIR investigations.
• Studied how CNN models can detect image tampering and manipulated content.
• Learned how machine learning is used in dynamic malware analysis.
• Understood that API call sequences represent program behavior patterns.
• Studied how API sequences can be converted into 2D images for AI analysis.
• Learned why CNN-based image recognition models can classify malware behavior.
• Learned how NLP models help identify phishing emails and suspicious communications.
• Understood how AI can reconstruct incident timelines from fragmented evidence.
• Studied how AI accelerates forensic analysis and improves detection capabilities.
• Understood that AI enhances human investigators rather than replacing them.
• Learned the importance of combining AI-assisted analysis with human expertise in cybersecurity.

AI Forensics Task 4 — AI Legal & Ethical Implications

• Learned that AI in forensics must be explainable and defensible in court.
• Understood how “black box” AI models can weaken the credibility of digital evidence.
• Learned that biased AI systems can lead to real-world injustice and wrongful accusations.
• Studied the importance of maintaining chain of custody and audit trails when using AI in DFIR.
• Understood why undocumented AI processing can make forensic evidence legally challengeable.
• Learned that privacy and legal compliance are critical when handling sensitive evidence with AI tools.
• Studied how Federated Learning and offline AI environments help preserve privacy.
• Learned that AI should enhance human investigators, not replace human judgment and responsibility.

AI Forensics Task 5 — Practical: The Digital Trail

• Learned how AI and machine learning can support DFIR investigations by identifying suspicious logs and files.
• Studied how attackers use reverse shells to gain and maintain remote access to compromised systems.
• Learned how hidden files, /tmp, and /dev/shm are commonly abused for stealth and persistence.
• Understood how attackers disguise malicious tools as legitimate system utilities to evade detection.
• Studied how SSH key abuse and authorized_keys modification can enable stealthy privilege escalation.
• Learned how fake telemetry logs and masquerading techniques help attackers blend into normal environments.
• Understood how compressed and Base64-encoded archives can be staged for data exfiltration.
• Learned how DFIR investigations connect evidence from logs, bash history, suspicious files, and network activity.
• Studied how AI can misclassify legitimate files as suspicious, highlighting the importance of human validation.
• Learned that AI enhances investigations, but human reasoning and contextual analysis remain essential in DFIR.

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🚀 Next Week

• Complete the badge algorithm in the SwiftUI tutorial.
• Continue posting small articles on Dev.to.
• Continue working on the AI Security Learning Path.

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🌈 Goals for This Year

📱 iOS (SwiftUI)

• Build a solid foundation in SwiftUI and create at least one iOS app.

🌐 Web Development

• Continue posting learning logs on Dev.to and eventually turn them into a portfolio site using React Router v7.

🔐 Security (TryHackMe)

• Continue learning cybersecurity on TryHackMe.