Cybersecurity Tools and Testing

🚨 Mastering IT Risk Assessment: A Strategic Framework for Information Security In cybersecurity, guesswork is not strategy. Effective risk management begins with a structured, evidence-based risk assessment process that connects technical threats to business impact. This framework — adapted from leading standards such as NIST SP 800-30 and ISO/IEC 27005 — breaks down how to transform raw threat data into actionable risk intelligence: 1️⃣ System Characterization – Establish clear system boundaries. Define the hardware, software, data, interfaces, people, and mission-critical functions within scope. 🔹 Output: System boundaries, criticality, and sensitivity profile. 2️⃣ Threat Identification – Identify credible threat sources — from external adversaries to insider risks and environmental hazards. 🔹 Output: Comprehensive threat statement. 3️⃣ Vulnerability Identification – Pinpoint systemic weaknesses that can be exploited by these threats. 🔹 Output: Catalog of potential vulnerabilities. 4️⃣ Control Analysis – Evaluate the design and operational effectiveness of current and planned controls. 🔹 Output: Control inventory with performance assessment. 5️⃣ Likelihood Determination – Assess the probability that a given threat will exploit a specific vulnerability, considering existing mitigations. 🔹 Output: Likelihood rating. 6️⃣ Impact Analysis – Quantify potential losses in terms of confidentiality, integrity, and availability of information assets. 🔹 Output: Impact rating. 7️⃣ Risk Determination – Integrate likelihood and impact to determine inherent and residual risk levels. 🔹 Output: Ranked risk register. 8️⃣ Control Recommendations – Prioritize security enhancements to reduce risk to acceptable levels. 🔹 Output: Targeted control recommendations. 9️⃣ Results Documentation – Compile the process, findings, and mitigation actions in a formal risk assessment report for governance and audit traceability. 🔹 Output: Comprehensive risk assessment report. When executed properly, this process transforms IT threat data into strategic business intelligence, enabling leaders to make informed, risk-based decisions that safeguard the organization’s assets and reputation. 👉 Bottom line: An organization’s resilience isn’t built on tools — it’s built on a disciplined, repeatable approach to understanding and managing risk. #CyberSecurity #RiskManagement #GRC #InformationSecurity #ISO27001 #NIST #Infosec #RiskAssessment #Governance

API Security: 16 Critical Practices You Need to Know Drawing from OWASP guidelines, industry standards, and enterprise security frameworks, here are 16 critical API security practices that every development team should implement: 1. Authentication Your first line of defense. Implement OAuth 2.0, JWT, and enforce MFA where possible. 2. Authorization RBAC and ABAC aren't buzzwords - they're essential. Implement granular access controls. 3. Rate Limiting Had an API taken down by a simple script? Rate limiting isn't optional anymore. 4. Input Validation Every parameter is a potential attack vector. Validate, sanitize, and verify - always. 5. Encryption TLS is just the beginning. Think end-to-end encryption and robust key management. 6. Error Handling Generic errors for users, detailed logs for systems. Never expose internals. 7. Logging & Monitoring You can't protect what you can't see. Implement comprehensive audit trails. 8. Security Headers CORS, CSP, HSTS - these headers are your API's immune system. 9. Token Expiry Long-lived tokens are ticking time bombs. Implement proper rotation and expiry. 10. IP Whitelisting Know who's knocking. Implement IP-based access controls where appropriate. 11. Web Application Firewall Your shield against common attack patterns. Configure and monitor actively. 12. API Versioning Security evolves. Your API versioning strategy should account for security patches. 13. Secure Dependencies Your API is only as secure as its weakest dependency. Audit regularly. 14. Intrusion Detection Real-time threat detection isn't luxury - it's necessity. 15. Security Standards Don't reinvent security. Follow established standards and frameworks. 16. Data Redaction Not all data should be visible. Implement robust redaction policies. The key lesson? These aren't independent practices - they form an interconnected security mesh. Miss one, and you might compromise the entire system. What's your experience with these practices? Which ones have you found most challenging to implement?

Case Study. Must read. Fixing Gmail deliverability isn’t as simple as changing your IP or switching platforms. In one real case: A brand moved to a dedicated IP on their ESP’s advice, hoping it would fix domain reputation issues. Warm-up was done correctly. SPF, DKIM, and DMARC were all passing. But Gmail Postmaster reputation dropped to "bad" and stayed there Gmail inbox placement went to 0%. CTRs were around 0.2%, and nothing improved. The core issue wasn't technical. It was behavioral. Their student emails were opt-in. But corporate emails came from purchased ZoomInfo lists. Gmail picked up on this and punished the entire domain. Changing IPs just exposed the issue faster. Their suppression logic also made things worse: 1. Users were suppressed only after 10 sends with no clicks 2. That means 10 chances to hurt domain reputation 3. Engagement-based filtering is strict 4. If people don’t interact, Gmail assumes your content is unwanted Technical setup wasn't perfect either: 1. Their signup API lacked rate limits 2. Bots were likely abusing the form 3. This led to emails being sent to fake or unverified addresses More bad signals sent to Gmail A "0% spam complaint rate" looked good on paper, but it was misleading. If no one sees your email in the inbox, they can’t complain. That’s a sign your emails are already deep in spam. Should you ever change IPs? Yes, if recommended by an experienced deliverability expert because the IPs are burnt and beyond recovery anytime soon. But only after identifying and fixing the root cause. Changing IPs without fixing your behavior is just a temporary patch What can actually help? Along with all other best practices, 1. Stop mailing Gmail users for a while. 2. Start fresh with small, high-quality segments. 3. Promote your email content on your website or social media to drive awareness. Good deliverability doesn’t come from tools or IPs. It comes from permission, relevance, and engagement. I have seen a lot of marketers with no optin lists but with content relevance and positive engagement they are doing great. If Gmail doesn’t see real interest in your emails, nothing else will matter. Happy to chat if you're navigating a similar situation. #email #emailmarketing

At PwC, we've learned that the biggest barrier to scaling enterprise AI isn't model capability: it's trust. Here's how we think about that problem. Every new technology faces the same deadlock: you don't use it because you don't trust it, and you don't trust it because you don't use it. The way out is usually a trust proxy, a visible marker that tells people it's safe to change their behavior. The SSL padlock is the classic example. Ecommerce was technically possible in the 1990s, but adoption stalled because typing a credit card into a browser felt reckless. The padlock didn't create security, the encryption was already there. It made security visible. Enterprise AI faces the same issue. The models work. Real solutions exist. But capability is compounding faster than confidence. You see it in cautious adoption: professionals double-checking outputs the system got right. Not because the models aren't good enough, but because there's no structured way to show they've been rigorously evaluated by people who know what good looks like. These aren't capability problems. They're trust infrastructure problems. That's what we built Evaluation Navigator and the Human Alignment Center to address. 📊 Evaluation Navigator gives AI teams a consistent, repeatable way to evaluate solutions across the development lifecycle, with shared guidance and standardized reporting. By embedding evaluation directly into developer workflows through an SDK, trust markers are built into the solution as it's constructed, not stapled on before deployment. 🧐 The Human Alignment Center adds structured expert review at scale. Automated metrics can assess technical correctness, but in professional services the real question is whether the output reflects experienced professional judgment. The Human Alignment Center translates that judgment into dashboards and audit trails that governance leaders can actually act on. The padlock made invisible security visible. Evaluation infrastructure does the same for AI. Adoption is a trailing indicator of trust, so as evaluation becomes visible and accessible, adoption follows.

There’s one feature I’ll never build at ChannelCrawler. Not because I don’t think it’s important. But because it is, and getting it wrong causes serious damage. I’m talking about sending mass cold emails from within the tool. Let users email hundreds of creators directly, save lots of time. It sounds useful in theory. But in practice? It’s not a good idea: - Microsoft/Google mark it as spam - Your emails start going to spam, even with your connections/customers - Deliverability gets weaker - Domain rating drops At smaller companies, the affects are much worse, because you make up a bigger percentage of the total emails going out. One person doing it wrong can ruin deliverability for the entire domain. If you're sending 1–10 highly personalised emails a day, go for it. For mass sending, even just 10+ a day you need a specialist email platform like Instantly, Smartlead, or Salesforge.ai 🔥 to do it right. We use the latter for example. I get asked about this feature often. And the answer is always the same: we’ll never build it. At most, we’ll offer integrations. Because if someone spams using our platform, we’re guilty by association. (A line from my favourite Linkin Park song.) I don't want to encourage poor practice. I only want people to be successful. The tools will help you do that. They will also help you be less spammy in your outreach to an extent too, but thats another issue altogether That’s the hill I’ll die on. 🫠 None of the platforms mentioned sponsored this post. Except ChannelCrawler I guess, since it pays my salary. . . . 👋 Hi, I’m Jake 🎙 I write and speak about growing businesses through YouTube and YouTubers 💡 Follow for more on that, or DM to chat 📈 Co-founder at ChannelCrawler, The worlds largest YouTube database

🔍 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗙𝗼𝗿𝗲𝗻𝘀𝗶𝗰𝘀 𝗧𝗼𝗼𝗹𝘀 𝗘𝘃𝗲𝗿𝘆 𝗦𝗢𝗖 & 𝗗𝗙𝗜𝗥 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 Digital forensics plays a critical role in incident response, threat hunting, and cybercrime investigations. Below is a curated list of essential tools used by DFIR analysts, SOC teams, and cybersecurity investigators. 🧰 𝗙𝘂𝗹𝗹 𝗙𝗼𝗿𝗲𝗻𝘀𝗶𝗰 𝗦𝘂𝗶𝘁𝗲𝘀 Comprehensive platforms for end-to-end investigations. • Autopsy • The Sleuth Kit • Magnet AXIOM • Cellebrite UFED • X-Ways 🧠 Memory Forensics 𝗧𝗼𝗼𝗹𝘀 𝘂𝘀𝗲𝗱 𝘁𝗼 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗥𝗔𝗠 𝗮𝗻𝗱 𝘃𝗼𝗹𝗮𝘁𝗶𝗹𝗲 𝗱𝗮𝘁𝗮. • Volatility • WinPmem • RAM Capturer • Magnet RAM Capture 📊 𝗧𝗶𝗺𝗲𝗹𝗶𝗻𝗲 & 𝗟𝗼𝗴 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗥𝗲𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁 𝗲𝘃𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗹𝗼𝗴𝘀 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗮𝘁𝘁𝗮𝗰𝗸 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲𝘀. • Log2timeline • Timesketch • Hindsight • DFIRTimewolf 💽 𝗗𝗶𝘀𝗸 𝗜𝗺𝗮𝗴𝗶𝗻𝗴 & 𝗔𝗰𝗾𝘂𝗶𝘀𝗶𝘁𝗶𝗼𝗻 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 𝗳𝗼𝗿𝗲𝗻𝘀𝗶𝗰 𝗶𝗺𝗮𝗴𝗲𝘀 𝗼𝗳 𝘀𝘁𝗼𝗿𝗮𝗴𝗲 𝗱𝗲𝘃𝗶𝗰𝗲𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗮𝗹𝘁𝗲𝗿𝗶𝗻𝗴 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲. • FTK Imager • WinFE • Guymager • dc3dd • ewfacquire • Disk-Arbitrator 🌐 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗙𝗼𝗿𝗲𝗻𝘀𝗶𝗰𝘀 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗻𝗲𝘁𝘄𝗼𝗿𝗸 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗮𝗻𝗱 𝗱𝗲𝘁𝗲𝗰𝘁 𝗺𝗮𝗹𝗶𝗰𝗶𝗼𝘂𝘀 𝗮𝗰𝘁𝗶𝘃𝗶𝘁𝘆. • Wireshark • NetworkMiner • Zeek • Snort • Suricata • Arkime 📱 Mobile Forensics 𝗘𝘅𝘁𝗿𝗮𝗰𝘁 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗱𝗮𝘁𝗮 𝗳𝗿𝗼𝗺 𝗺𝗼𝗯𝗶𝗹𝗲 𝗱𝗲𝘃𝗶𝗰𝗲𝘀. • libimobiledevice • ALEAPP • ILEAPP • ArtEx • MSAB XRY ⚡ 𝗟𝗶𝘃𝗲 𝗙𝗼𝗿𝗲𝗻𝘀𝗶𝗰 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗳𝗿𝗼𝗺 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. • KAPE • Velociraptor • GRR Rapid Response • F-Response • Cylance • UAC 🗂️ 𝗙𝗶𝗹𝗲, 𝗠𝗲𝘁𝗮𝗱𝗮𝘁𝗮 & 𝗗𝗮𝘁𝗮 𝗖𝗮𝗿𝘃𝗶𝗻𝗴 𝗥𝗲𝗰𝗼𝘃𝗲𝗿 𝗳𝗶𝗹𝗲𝘀, 𝗲𝘅𝘁𝗿𝗮𝗰𝘁 𝗺𝗲𝘁𝗮𝗱𝗮𝘁𝗮, 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗮𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀. • bulk-extractor • X-Ways • Bulk • ExifTool • Foremost • Scalpel • FLOSS 🪟 𝗪𝗶𝗻𝗱𝗼𝘄𝘀 𝗔𝗿𝘁𝗶𝗳𝗮𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗴𝗮𝘁𝗲 𝗪𝗶𝗻𝗱𝗼𝘄𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗮𝗿𝘁𝗶𝗳𝗮𝗰𝘁𝘀. • Hayabusa • Login Tracer • RegRipper • RecuperaBit • NTFS-Tool 💡 DFIR is not about one tool — it's about combining multiple tools to uncover the full attack story. Which Digital Forensics tools do you use most in investigations? #DigitalForensics #DFIR #CyberSecurity #IncidentResponse #ThreatHunting #ForensicsTools #SOC #CyberDefense For More Daily Security Updates, Follow: Kaaviya Balaji

I use ChatGPT to verify suspicious emails before getting phished. Here is the workflow: When you get an email that feels sketchy, before clicking anything, copy the full original message and paste it into ChatGPT. ChatGPT can quickly tell you whether the authentication checks pass, whether the redirect chains are legitimate, and whether the sender actually owns the domain. Here is how to get the full original message in the two email systems most people use. Use the prompt "Is this email from legit?" with any of these options. - Outlook Web (Outlook.com or Office 365 online) - Open the email. - Click the three dots. - Choose View, then “View message source.” - Copy the full message into ChatGPT. Outlook Desktop on Mac - Open the email. - In the top menu choose Message, then “View Source.” - Copy the entire raw message. Outlook Desktop on Windows - Open the email. - File, then Properties. - Copy the “Internet headers” field. - This version shows authentication details but not the full raw body, so the web version is better when possible. Gmail - Open the email. - Click the three dots in the upper right. - Choose “Show original.” - Copy everything into ChatGPT Of course this doesn't replace good security hygiene, but it gives you a fast way to validate the technical evidence behind an email without guessing. Have you tried using AI as your cybersecurity partner in this or other ways?

We see this often : a sleek new #cybersecurity tool or a cutting-edge platform and now anything #AI hits the market, and suddenly, it becomes like a "Gold Standard" must-have for the industry. In the world of procurement of #technology and #security solutions, there's a dangerous psychological phenomenon at play. Popularity is often mistaken for suitability. When one sees peers, competitors and industry influencers adopting a specific technology, it seems to give a sense of comfort. But as the old adage goes, familiarity breeds contempt, and in the context of security, tech, infrastructure solutions, that contempt can lead to expensive, insecure ecosystems that fall short of their performance expectations and business justification. The contempt doesn't usually start with the tool, it starts with the misalignment between the tool and actual business needs : 🔸 The Shelfware Syndrome: Where the buy decision was more on industry hype rather than careful assessment of the specific pain points. Then, the tool is underutilized, teams become resentful of the complex interface they weren't trained for 🔸 The False Sense of Security: Familiarity with a brand name breeds a dangerous level of comfort. Often an EDR solution or a DLP is assumed to keep performing as implemented, but teams forget the routine monitoring, upgrades, rule resets etc, and such complacency is what attackers exploit. 🔸 Integration Friction: Just because a tool works for a large institution with a more mature setup doesn't mean it will play well with say, legacy manufacturing systems. In the absence of skill and integrators, it may feel like forced adoption, which create friction and workarounds, becoming dangerous grounds for security vulnerabilities. To avoid the trap of such "contemptuous familiarity", break the 'hype' cycle and consider procurement by the fundamentals : 💡 Why : Where's the gap in the internal process / control ? What specific risk is to be mitigated? Which process can be automated? Where can efficiency be brought, with detailed calculations, on the existing metrics vs expected ? 💡 How : Will the tool be integrated with our unique architecture, or will require substantial changes in say the APIs, connectors, workflows et all? Are there people and skillset to manage this? 💡 Where : Do the proposed tech match business goals ? ✔️ The hardest part of #digitaltransformation, be it large scale #AI #automation or a significant security tool, is to decide the start. ✔️ Make solid groundwork, so as to deliver the expected ROIs and long-term technology adaptations, rather than quick, disconnected experiments. ✔️ Begin with small pilots, and expand only when value is proven in controlled rollout ✔️ Engage a qualified, trained team to define, measure, monitor, gather user feedback and keep refining ✔️ Employ appropriate data management and security against every tech integration. Have you faced such challenges? Add in the comments. #cyberrisk #technologyrisk

How to Approach Mobile Penetration Testing: A Real-World Guide In today’s digital age, mobile applications are a cornerstone of many businesses, but they are also a prime target for attackers. Mobile penetration testing ensures these apps are secure, reliable, and resilient to cyber threats. Here’s how to approach it step-by-step: 1️⃣ Pre-engagement Phase • Define the scope: Android, iOS, or both? Native, web, or hybrid apps? • Set up testing tools: Static analysis (e.g., MobSF), dynamic analysis (e.g., Frida, Burp Suite), and reverse engineering (e.g., JADX). 2️⃣ Reconnaissance • Analyze the app store listing for permissions, version history, and potential clues. • Decompile the app to uncover hardcoded secrets, APIs, and other vulnerabilities. 3️⃣ Static Analysis • Review the codebase for: • Hardcoded credentials. • Insecure storage. • Weak cryptographic practices. • Audit permissions and configuration files for security misconfigurations. 4️⃣ Dynamic Analysis • Test the app on an emulator or physical device. • Intercept and analyze network traffic for sensitive data leaks or weak encryption. • Evaluate authentication and session management mechanisms. 5️⃣ Backend Testing • Assess APIs for vulnerabilities like insecure authorization, IDOR, and data exposure. • Check server configurations (e.g., SSL/TLS setup). 6️⃣ Device Testing • Check local storage for sensitive data. • Review secure storage mechanisms like Keychain/Keystore. • Test for clipboard exposure and file tampering vulnerabilities. 7️⃣ Exploitation • Bypass root/jailbreak detection. • Exploit vulnerabilities for privilege escalation or tampering. 8️⃣ Reporting • Document all findings with clear descriptions, proof-of-concept (PoC), and remediation steps. • Provide actionable recommendations to secure the app. 🛠 Key Tools: • Static Analysis: MobSF, Apktool, JADX. • Dynamic Testing: Frida, Burp Suite, mitmproxy. • Network Analysis: Wireshark, Netcat. What I learned this weekend: This weekend, I deep-dived into the fascinating world of mobile penetration testing. Understanding the real-world processes and tools involved has been eye-opening and invaluable for my skillset. What’s next? I’ll be posting a complete demo of me performing a full mobile penetration test on a demo app as a personal project! I’d love for you to watch, provide feedback, and share your thoughts on what I did right and what could be improved. Let’s learn and grow together! 💡 What’s your go-to tool or tip for mobile app security? Let’s discuss in the comments! #CyberSecurity #MobileSecurity #PenetrationTesting #AppSec #InfoSec #LinkedInNetworking

I just tested an outreach campaign that hit a 34% bounce rate. Same offer. Same copy. Same targeting strategy that worked the month before. But deliverability tanked because half the list was outdated. The best cold email in the world doesn't matter if it never reaches the inbox. Poor data quality doesn't just hurt one campaign—it burns sender reputation and tanks future performance too. One agency learned this after three campaigns in a row hit spam folders. Their domain was flagged. Prospects weren't seeing anything. The problem wasn't the messaging—it was the data. Here's what fixed it: They started verifying every single email before hitting send. Used Skrapp.io to pull fresh B2B contacts from LinkedIn and auto-verify deliverability. No more guessing. No more hoping emails would land. The result? Bounce rate dropped to under 2%. Reply rates doubled. And sender score recovered in two weeks. Great outreach starts way before writing the first line. It starts with clean, verified data. If the list is broken, the campaign is already dead. How are teams keeping their contact lists clean right now?