QA Methods for Identifying Risks and Dependencies

If you're a software engineer working with AI in your workflow, here's a simple prompt to make sure you're 100% covered from a security point of view, based on my last 6 years in DevSecOps: Paste this into your agent before you ship anything important: You are a senior security engineer performing an adversarial security audit of this codebase, app, or system design. Assume it will run in a hostile environment with motivated attackers. Audit these layers: - frontend - backend - auth and permissions - database and storage - infrastructure and deployment - third-party integrations and dependencies Your job: 1. Find critical, high, medium, and low severity issues 2. Catch logic flaws, not just common patterns 3. Identify multi-step attack paths 4. Flag unusual or non-obvious risks 5. Think like a creative attacker, not a checklist scanner Threat model first: - define attacker types - identify entry points - identify trust boundaries - identify sensitive assets like data, secrets, tokens, and permissions Check for issues in: - auth, sessions, password reset, token misuse - broken authorization, IDOR, privilege escalation - SQL, NoSQL, command, template, and file upload attacks - XSS, CSRF, replay, race conditions, cache poisoning - mass assignment, rate limit gaps, brute force paths - secret leaks, weak crypto, insecure storage, bad logging - CORS, CSP, headers, debug endpoints, env leaks - cloud or deployment misconfigurations - vulnerable or risky dependencies Also try to discover: - feature abuse - impossible-but-possible behavior - state desync issues - weak trust assumptions - attack chains built from smaller issues Output format: 1. Vulnerability summary by severity 2. Detailed findings with: - title - severity - affected component - description - exploitation steps - impact - recommended fix 3. Attack chains 4. Secure design improvements Important: - assume nothing is safe - infer risk where context is missing - be exhaustive - if something looks risky but uncertain, flag it and explain why Most people use AI to write code faster. Very few use it to pressure test what they just built. That second use case will save you a lot more pain. -- 📢 Follow saed if you enjoyed this post 🔖 Be sure to subscribe to the newsletter: https://lnkd.in/eD7hgbnk 📹 Reach me on https://lnkd.in/eZ9mU5Ka for open DM's

Agile: It Depends Sorry, purists, but cross-team dependencies are a reality, even in Agile environments - and especially when scaling (e.g., SAFe). Agile teams are independent, but don't (or shouldn't) work in isolation. Dependencies, whether they're due to shared systems, limited expertise, or interconnected work products, can disrupt flow, cause friction, and delay value delivery. When they can't be eliminated, then managing them effectively should become a core team skill in any complex, interconnected environment. Dependencies Dependencies emerge when one team’s work relies on the completion or input of another team, ART, or external group. Left unmanaged, they create bottlenecks, misalignments, and delays, threatening Agile’s focus on predictability. The ideal scenario minimizes dependencies, but practical constraints like limited expertise or tightly coupled systems mean they can’t all be eliminated. So, the focus must shift to managing dependencies with transparency and collaboration. Visualization Make dependencies visible. Tools like dependency maps, inter-team Kanban boards, or visualizations in platforms like Jira (e.g., BigPicture) help teams see connections and track progress. Effective visualization highlights critical handoffs and potential delays, enables teams to monitor dependency resolution in real time, and provides a shared understanding for better coordination. During PI Planning, teams can use dependency boards to identify risks, align timelines, and agree on milestones. Be Proactive Dependencies must be identified as early as possible to reduce surprises. Teams should surface them during Agile events During PI Planning, teams collaborate to uncover cross-team dependencies and plan solutions. Reviewing stories during Backlog Refinement allows teams to flag and address dependencies before they become urgent. By proactively identifying dependencies, teams can align their schedules, coordinate integration efforts, and mitigate delays before they impact delivery. Accountability Every dependency needs a clear owner. Without ownership, accountability gets lost, and dependencies become a source of frustration. Ownership means assigning a team or person to manage each dependency, setting clear agreements on timelines and expectations, and checking progress regularly to maintain alignment. This reduces ambiguity and fosters trust. Reduce Impact Some dependencies are unavoidable, but teams can reduce their impact through thoughtful technical and architectural choices. Designing modular systems, using feature toggles, and automating shared tests are just some of the practices that can help teams work more independently. It Depends - But It’s Manageable Dependencies may be unavoidable, but they don’t have to be disruptive. By visualizing, identifying, owning, and mitigating dependencies, teams can maintain flow, improve collaboration, and deliver value predictably. Doing so is a skill every Agile team must master.

Dear Risk manager, 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗿𝗶𝘀𝗸 in an organization involves systematically evaluating potential threats that could affect the achievement of objectives, impact operations, or harm stakeholders. Here are key steps to identify risks: 1️⃣ 𝗖𝗼𝗻𝗱𝘂𝗰𝘁 𝗮 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗰𝗲𝘀𝘀: √ Define Risk Criteria √ Identify Key Objectives: Understand the organization's strategic, operational, and financial goals to determine what risks could potentially prevent their achievement. 2️⃣ 𝗥𝗶𝘀𝗸 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: √ Brainstorming Sessions: Involve teams from different departments to generate a list of potential risks. √ SWOT Analysis: Analyze the organization's strengths, weaknesses, opportunities, and threats to uncover both internal and external risks. √ Interviews and Surveys: Engage key stakeholders (executives, managers, employees) to get their perspectives on what risks they foresee. √ Historical Data Review: Examine past incidents or similar organizations’ cases to identify recurring or likely risks. √ Checklists: Use industry-specific risk checklists to ensure that common risks are not overlooked. 3️⃣ 𝗥𝗶𝘀𝗸 𝗠𝗮𝗽𝗽𝗶𝗻𝗴: √ Categorize Risks: Group risks into categories, such as financial, operational, technological, legal, environmental, strategic, or reputational risks. √ Risk Matrix: Assess the likelihood and impact of each identified risk to determine its severity and prioritize mitigation actions. 4️⃣ 𝗨𝘀𝗲 𝗼𝗳 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗧𝗼𝗼𝗹𝘀: √ Risk Registers: Create a central repository to record identified risks, their causes, potential impacts, and the actions taken to address them. √ Risk Management Software: Implement tools to track and analyze risks more effectively. 5️⃣ 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗘𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁: √ Regulatory Changes: Monitor changes in laws, regulations, and industry standards that could introduce new risks. √ Market Trends: Stay updated on shifts in the market or competition that could pose strategic risks. √ Technology Advancements: Assess how new technologies might create cybersecurity risks or operational disruptions. 6️⃣ 𝗥𝗲𝗴𝘂𝗹𝗮𝗿 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗥𝗲𝘃𝗶𝗲𝘄: √ Continuous Monitoring: Keep a regular check on internal and external factors that might change, leading to new or altered risks. √ Audit and Inspections: Regular internal audits, inspections, and compliance checks can uncover risks early. 7️⃣ 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴: √ What-if Analysis: Test various scenarios of risk occurrences (e.g., economic downturn, data breach) and assess their potential impact. √ Stress Testing: Simulate extreme conditions (financial crisis, supply chain failure) to assess organizational resilience. By using these methods and continuously reassessing the environment, organizations can identify and mitigate risks effectively.

𝐐𝐀 𝐭𝐞𝐚𝐦𝐬: are you relying on instinct to decide which tests to prioritize? 😕 That method can quietly drain your time and leave high-risk areas exposed. Many teams treat test coverage like a numbers game. More tests must mean better quality, right? But here’s the reality… 𝘚𝘰𝘮𝘦 𝘵𝘦𝘴𝘵𝘴 𝘯𝘦𝘷𝘦𝘳 𝘧𝘢𝘪𝘭. 𝘚𝘰𝘮𝘦 𝘧𝘦𝘢𝘵𝘶𝘳𝘦𝘴 𝘢𝘭𝘸𝘢𝘺𝘴 𝘣𝘳𝘦𝘢𝘬 𝘢𝘧𝘵𝘦𝘳 𝘶𝘱𝘥𝘢𝘵𝘦𝘴. 𝘈𝘯𝘥 𝘴𝘰𝘮𝘦 𝘢𝘳𝘦𝘢𝘴 𝘤𝘢𝘶𝘴𝘦 𝘪𝘴𝘴𝘶𝘦𝘴 𝘳𝘦𝘱𝘦𝘢𝘵𝘦𝘥𝘭𝘺 𝘺𝘦𝘵 𝘨𝘦𝘵 𝘵𝘩𝘦 𝘴𝘢𝘮𝘦 𝘢𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘢𝘴 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘦𝘭𝘴𝘦. Predictive analytics helps shift that dynamic. By pulling data from failed tests, bug histories, and past releases, you start to see patterns, the features that break more often, the types of changes that introduce risk, and the areas that need closer inspection. You can: ➡️ Focus testing on modules that are statistically more likely to fail ➡️ Surface high-risk code paths earlier in the cycle ➡️ Reduce noise by identifying tests that rarely catch defects When you understand what’s likely to go wrong, you don’t have to treat every test like it’s equal. The data is already telling a story. It’s just a matter of paying attention. 🚀 #QA #SoftwareTesting #PredictiveAnalytics

🔵 𝐑𝐢𝐬𝐤, 𝐀𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧𝐬, 𝐂𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭𝐬, 𝐈𝐬𝐬𝐮𝐞𝐬, 𝐚𝐧𝐝 𝐃𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐢𝐞𝐬 (𝐑𝐀𝐂𝐈𝐃) 🔵 As a Business Analyst, mastering these isn't just "good to know" — it’s absolutely critical for successful project delivery. Here's a practical breakdown 👇 ✅ 𝐑𝐢𝐬𝐤 = Future uncertainty that might impact project goals. ➔ Example: "If the vendor delays the API delivery, the system launch may get postponed." 📌 Why BAs must capture it? To proactively plan mitigations before problems occur. ✅ 𝐀𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧𝐬 = Things we believe to be true (but haven't verified yet). ➔ Example: "Users will have internet access while using the mobile app." 📌 Why BAs must capture it? If assumptions prove false later, it can derail the project. ✅ 𝐂𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭𝐬 = Limitations the project must operate within. ➔ Example: "The solution must integrate with the existing SAP system without extra licensing." 📌 Why BAs must capture it? To design realistic solutions and set proper expectations. ✅ 𝐈𝐬𝐬𝐮𝐞𝐬 = Current problems that need immediate attention. ➔ Example: "Test data isn't available, delaying QA activities." 📌 Why BAs must capture it? To escalate and support timely resolution, ensuring project flow. ✅ 𝐃𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐢𝐞𝐬 = Relationships where one task or team relies on another. ➔ Example: "UAT cannot start until the development team delivers the build." 📌 Why BAs must capture it? To highlight sequence priorities and avoid blockers. 🎯 𝐁𝐨𝐭𝐭𝐨𝐦 𝐋𝐢𝐧𝐞: A strong Business Analyst actively identifies, documents, tracks, and communicates RACID items throughout the project lifecycle. Ignoring them can mean scope creep, missed deadlines, or even project failure. 👉 Good documentation today = Fewer surprises tomorrow! BA Helpline

𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐑𝐢𝐬𝐤 𝐢𝐧 𝐒𝐜𝐫𝐮𝐦: 𝐘𝐨𝐮𝐫 𝐐&𝐀 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐒𝐮𝐜𝐜𝐞𝐬𝐬! Managing risks in Scrum isn’t just about resolving issues—it’s about staying ahead and ensuring seamless project execution. Let’s dive into some frequently asked questions about mitigating risks in Scrum and explore strategies to keep your team agile. ➡️ 𝐇𝐨𝐰 𝐂𝐚𝐧 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 𝐨𝐟 𝐃𝐨𝐧𝐞 (𝐃𝐨𝐃) 𝐇𝐞𝐥𝐩 𝐌𝐢𝐭𝐢𝐠𝐚𝐭𝐞 𝐑𝐢𝐬𝐤𝐬? 𝐐: What role does the Definition of Done (DoD) play in risk management? 𝐀: DoD is your safety net. Incorporate risk-related criteria into the DoD—like code reviews, automated testing, or performance benchmarks. By ensuring every increment meets quality and safety standards, you minimize risks tied to incomplete or suboptimal work. ➡️ 𝐇𝐨𝐰 𝐂𝐚𝐧 𝐄𝐧𝐠𝐚𝐠𝐢𝐧𝐠 𝐒𝐭𝐚𝐤𝐞𝐡𝐨𝐥𝐝𝐞𝐫𝐬 𝐑𝐞𝐝𝐮𝐜𝐞 𝐑𝐢𝐬𝐤? 𝐐: Why is stakeholder collaboration critical in Scrum? 𝐀: Sprint Reviews provide the perfect opportunity to collaborate with stakeholders. Their feedback helps uncover risks like evolving requirements, market trends, or dependencies. By aligning with stakeholders early, your team can pivot quickly and avoid surprises. ➡️ 𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐌𝐚𝐭𝐭𝐞𝐫? 𝐐: How can teams keep track of risks effectively? 𝐀: Visualization tools like burn-down charts or risk trend graphs help track risks alongside progress. Teams should reassess risks during Backlog Refinement or other informal discussions to stay proactive and informed. ➡️ 𝐇𝐨𝐰 𝐂𝐚𝐧 𝐂𝐨𝐧𝐭𝐢𝐧𝐠𝐞𝐧𝐜𝐲 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐇𝐞𝐥𝐩? 𝐐: What if unexpected risks arise mid-Sprint? 𝐀: Flexibility is key. Build a buffer in your Sprint to address high-priority risks as they arise. Use Scrum’s adaptive nature to pivot seamlessly when risks materialize, ensuring minimal disruption to the workflow. ➡️ 𝐀𝐠𝐢𝐥𝐞 𝐑𝐢𝐬𝐤 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐐: 𝐂𝐚𝐧 𝐒𝐜𝐫𝐮𝐦 𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐞 𝐰𝐢𝐭𝐡 𝐟𝐨𝐫𝐦𝐚𝐥 𝐫𝐢𝐬𝐤 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐭𝐨𝐨𝐥𝐬? 𝐀:Absolutely! Frameworks like RAID (Risks, Assumptions, Issues, Dependencies) logs or Failure Mode and Effects Analysis (FMEA) enhance Scrum’s risk-handling capabilities. These tools provide a structured way to analyze and address risks without disrupting the Agile flow. 𝐂𝐥𝐨𝐬𝐢𝐧𝐠 𝐓𝐡𝐨𝐮𝐠𝐡𝐭𝐬 Risk management in Scrum is a dynamic, collaborative effort. From refining the DoD to leveraging Agile frameworks, embedding these practices ensures your team stays resilient and delivers value consistently. What do you think of these strategies? Do you have specific questions or topics you’d like me to cover in future posts? I’d love to hear your thoughts and insights! 👉 Follow Chandan Kumar for regular updates, practical advice, and expert guidance on Agile and Scrum practices. Together, let’s tackle risks and unlock project success!

Qualitative and Quantitative Risk Assessment: A Comprehensive Technical Overview Effective #RiskManagement depends on deploying rigorous and structured risk assessment methodologies. The two predominant frameworks across enterprises are Qualitative Risk Assessment (QRA) and Quantitative Risk Assessment (QnRA). Both are essential for identifying, evaluating, and prioritizing risks but differ greatly in analytical approach, data granularity, and computational complexity. Qualitative Risk Assessment leverages expert judgment, structured workshops, and standardized scoring matrices (e.g., Low, Medium, High likelihood and impact) to estimate severity and probability of adverse events. Ideal for rapid screening where historical data is sparse, it employs tools like risk heat maps, risk registers, and Failure Mode and Effects Analysis (#FMEA). In contrast, Quantitative Risk Assessment utilizes mathematical models, probabilistic simulations (e.g., Monte Carlo analysis), and statistical inference to generate objective numerical risk values such as Expected Monetary Value (#EMV), Probability of Failure on Demand (#PFD), and Loss Exceedance Curves. It is vital in high-stakes sectors such as nuclear, aerospace, and financial services, often integrating fault tree analysis (#FTA), event tree analysis (#ETA), and reliability block diagrams (#RBD). Integrated Risk Assessment Workflow Overview: See attached This approach combines qualitative and quantitative methods in a dynamic architecture: Risk Identification: Inputs from operational data, audits, and expert interviews Qualitative Assessment: Scoring matrices, risk workshops, heat maps Quantitative Assessment: Data ingestion, statistical models, simulations Decision Support: Dashboards with drill-down analytics Governance & Compliance: Integrated with #GRC platforms for audit and reporting This workflow emphasizes real-time data exchange, iterative feedback loops, and role-based access control to ensure robust risk oversight. Key Stakeholders & Groups Involved: @Risk Management Teams — risk governance & strategy @Safety Engineers & Analysts — assessment & scenario modeling @Data Science & Analytics Teams — data modeling & simulations @IT & Security Operations — data integrity & incident response @Compliance & Audit Groups — regulatory validation @Executive Leadership & Boards — strategic risk oversight Mastering when and how to apply these complementary methodologies is crucial for building resilient, scalable risk management programs. This framework empowers professionals and leaders to leverage data-driven insights, promote continuous improvement, and embody the Safety Leader’s Mindset—grounded in knowledge, growth, and proactive leadership. #RiskAssessment #EnterpriseRiskManagement #SafetyLeadership #DataAnalytics #Compliance #Governance #RiskCulture #OperationalRisk #Leadership

From Theory to the Real-World Practice of AI Risk Identification While regulations and standards like the EU AI Act and ISO 42001 clearly mandate "identifying risks," they're silent on how to actually do it. In this article, I'll show you 5 techniques that work for real. When I ask teams about their risk identification process, the answers are often revealing (and worrying): "We do an annual assessment around a table.", "We convert audit findings into risks.", "We don't really have a formal process." My latest article tackles this head-on, translating from theoretical frameworks into the practical techniques I use and that I know work. I'm sharing these 5 approaches with the aim of helping AI Governance teams move beyond abstract checklists or frameworks to uncover how AI risks actually emerge: 🔮 Pre-Mortem Simulation - Imagine your AI has already failed catastrophically 🕵️ Incident Pattern Mining - Learn from others' AI disasters before repeating them ⏱️ Time-Horizon Scanning - Spot risks across different timescales to escape reactive firefighting 🎯 Red-Teaming - Deploy ethical hackers to find weaknesses others miss 🕸️ Dependency Chain Analysis - Map the hidden connections where minor issues cascade into major failures Each approach reveals different aspects of AI risk - from the human factors that pre-mortems surface to the intricate system dependencies that chain analysis exposes. Whether you're building an AI management system from scratch or looking to strengthen your risk identification process, these proven techniques will help you spot hidden hazards before they emerge. Read the full article (and please do subscribe for more - it's all free) at: https://lnkd.in/ggdZ77mE #AIGovernance #RiskManagement #AIEthics #ResponsibleAI