Engineering Risk Assessment Strategies

🚨 AI Privacy Risks & Mitigations Large Language Models (LLMs), by Isabel Barberá, is the 107-page report about AI & Privacy you were waiting for! [Bookmark & share below]. Topics covered: - Background "This section introduces Large Language Models, how they work, and their common applications. It also discusses performance evaluation measures, helping readers understand the foundational aspects of LLM systems." - Data Flow and Associated Privacy Risks in LLM Systems "Here, we explore how privacy risks emerge across different LLM service models, emphasizing the importance of understanding data flows throughout the AI lifecycle. This section also identifies risks and mitigations and examines roles and responsibilities under the AI Act and the GDPR." - Data Protection and Privacy Risk Assessment: Risk Identification "This section outlines criteria for identifying risks and provides examples of privacy risks specific to LLM systems. Developers and users can use this section as a starting point for identifying risks in their own systems." - Data Protection and Privacy Risk Assessment: Risk Estimation & Evaluation "Guidance on how to analyse, classify and assess privacy risks is provided here, with criteria for evaluating both the probability and severity of risks. This section explains how to derive a final risk evaluation to prioritize mitigation efforts effectively." - Data Protection and Privacy Risk Control "This section details risk treatment strategies, offering practical mitigation measures for common privacy risks in LLM systems. It also discusses residual risk acceptance and the iterative nature of risk management in AI systems." - Residual Risk Evaluation "Evaluating residual risks after mitigation is essential to ensure risks fall within acceptable thresholds and do not require further action. This section outlines how residual risks are evaluated to determine whether additional mitigation is needed or if the model or LLM system is ready for deployment." - Review & Monitor "This section covers the importance of reviewing risk management activities and maintaining a risk register. It also highlights the importance of continuous monitoring to detect emerging risks, assess real-world impact, and refine mitigation strategies." - Examples of LLM Systems’ Risk Assessments "Three detailed use cases are provided to demonstrate the application of the risk management framework in real-world scenarios. These examples illustrate how risks can be identified, assessed, and mitigated across various contexts." - Reference to Tools, Methodologies, Benchmarks, and Guidance "The final section compiles tools, evaluation metrics, benchmarks, methodologies, and standards to support developers and users in managing risks and evaluating the performance of LLM systems." 👉 Download it below. 👉 NEVER MISS my AI governance updates: join my newsletter's 58,500+ subscribers (below). #AI #AIGovernance #Privacy #DataProtection #AIRegulation #EDPB

Aerones is a Latvian robotics company focused on wind turbine inspection, maintenance, and repair. They use drones and crawler robots to check turbine blades inside and out. The systems handle lightning protection tests, drainage hole cleaning, visual inspections, and non-destructive testing. Aerones also provides robotic cleaning for blades and towers, removing dust, bugs, salt, algae, oil, and more. Robots can apply protective coatings, including ice-phobic and leading-edge coatings, directly on-site. A drone can scan a turbine in under 30 minutes with one button press. Data is uploaded to the cloud immediately and analyzed with AI to detect and classify issues. Compared to traditional methods, Aerones cuts downtime by 4–6 times and idle-stay periods by 5–10 times. Their technology is used worldwide by operators such as NextEra, GE, Vestas, Enel, and Siemens Gamesa, on both onshore and offshore turbines.

Medical device risk assessment isn’t just about what goes wrong but how it harms the patient/user ↴ Let's review some definitions: ✓ Harm = Injury or damage to the health of people, or damage to property or the environment. ✓ Hazard = Potential source of harm. ✓ Hazardous Situation = Circumstance in which people, property, or the environment is/are exposed to one or more hazards. ✓ Risk = Probability (P) of harm × Severity (S) of harm. Always remember: when answering ISO 14971, you're addressing this sequence: Hazard → Events → Hazardous Situation → Harm Note: One hazard can lead to multiple hazardous situations, which can lead to multiple harms. Don't forget that probability (P) can be split into: → P1 = Probability of a hazardous situation occurring. → P2 = Probability that situation causes harm. (This will be useful later.) Now, practical application: A device fails. A patient suffers. But was it direct harm… or indirect? That depends on your device.↴ Some devices fail, and the harm is immediate. Example: Hip prosthesis → A microcrack forms unnoticed. → The implant breaks inside the body. Direct Harm? ↳ Severe pain & immobility. ↳ Infection from broken implant fragments. Here's another example where the device isn’t the direct cause but still leads to harm. Example: Incorrect diagnostic output → A diagnostic device fails to detect a critical condition. → A clinician makes a wrong decision based on faulty data. → Outcome? Delayed/misguided treatment & more. To address indirect risks, I like to do this: → Assess risk across the entire system. → If multiple devices interact = System of Systems (SoS), analyze all interactions, sequence of events of your SoS (Device 1 ↔ Device 2 ↔ Patient) This is where splitting P1 & P2 can be a valuable strategy: → Helps understand event interactions. → Enables a combined risk approach for a comprehensive SoS risk assessment. I always ask myself this when evaluating an SoS: What is the probability of harm resulting from every hazardous situation? Need more for your medical device risk management ? Using our risk management template & methodology as a guide, you will be able to: → Use compliant process with ISO 14971 and MDR → Use a clear ISO 14971 methodology → Present your data clearly → Use tools proven in audits (our Hazard Traceability Matrix, RMP, and RMR). → Save time – no need to create templates from scratch. Our Risk Management bundle: https://lnkd.in/eTw2VVXp

Reliability, evaluation, and “hallucination anxiety” are where most AI programmes quietly stall. Not because the model is weak. Because the system around it is not built to scale trust. When companies move beyond demos, three hard questions appear: →Can we rely on this output? →Do we know what “good” actually looks like? →How much human oversight is enough? The fix is not better prompting. It is a strategy and operating discipline. 𝐅𝐢𝐫𝐬𝐭: ⁣Define reliability like a product, not a vibe. Every serious AI use case should have a one-page SLO sheet with measurable targets across: →Task success ↳Right-first-time rate and rubric-based acceptance →Factual grounding ↳Evidence coverage and unsupported-claim tracking →Safety and compliance ↳Policy violations and PII leakage →Operational quality ↳Latency, cost per task, escalation to humans Now “good” is no longer opinion. It is observable. 𝐒𝐞𝐜𝐨𝐧𝐝:  evaluation must be continuous, not a one-off demo test. Use a simple loop: 𝐏lan: Define rubrics, datasets, and risk tiers 𝐃⁣o: Run offline evaluations and limited pilots 𝐂heck: Monitor drift and regressions weekly 𝐀ct: Update prompts, data, guardrails, and workflows Support this with an AI test pyramid: →Unit checks for prompts and tool behaviour →Scenario tests for real edge failures →Regression benchmarks to prevent backsliding →Live monitoring in production Add statistical control charts, and you can detect silent degradation before users do. 𝐓𝐡𝐢𝐫𝐝: reduce hallucinations by design. →Run a short failure-mode workshop and engineer controls: →Require retrieval or evidence before answering →Allow safe abstention instead of confident guessing →Add claim checking and tool validation →Use structured intake and clarifying flows You are not asking the model to behave. You are designing a system that expects failure and contains it. 𝐅𝐨𝐮𝐫𝐭𝐡: make human-in-the-loop affordable. Tier risk: →Low risk: Light sampling →Medium risk: Triggered review →High risk: Mandatory approval Escalate only when signals demand it: low confidence, missing evidence, policy flags, or novelty spikes. Review becomes targeted, fast, and a source of improvement data. 𝐅𝐢𝐧𝐚𝐥𝐥𝐲: Operate it like a capability. Track outcomes, risk, delivery speed, and cost on a single dashboard. Hold a short weekly reliability stand-up focused on regressions, failure modes, and ownership. What you end up with is simple: ↳Use case catalogue with risk tiers ↳Clear SLOs and error budgets ↳Continuous evaluation harness ↳Built-in controls ↳Targeted human review ↳Reliability cadence AI does not scale on intelligence alone. It scales on measurable trust. ♻️ Share if you found thisuseful. ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI #AI #AIReliability #TrustAtScale #OperationalExcellence

After decades of working in project risk analysis—and building our own Monte Carlo-based software tool (HawkEye)—I’ve been refining a practical way to bridge the gap between schedule risk and cost risk. That method eventually became RISA: Risk Impact Sensitivity Analysis. In my last article, I focused on schedule risks. But in this new one, I take the next step: 👉 Integrating schedule AND cost risks into one rational, quantitative model. Why does that matter? Because schedule risks don’t just delay projects—they ripple into labor costs, procurement, contracting strategies, and the overall project budget. Yet many teams still treat schedule and cost analysis separately. In this article, I walk through: ⚙️ How to integrate schedule and cost simulations 🎯 How RISA helps prioritize the risks 🛠️ How mitigation strategies change outcomes 📊 How to calculate contingency reserves based on data, not optimism And yes—there’s a real case study to make it practical. Hope you enjoy the read—and I’d love to hear your thoughts or experiences. 📄 Article link below 👇 https://lnkd.in/gi-S7HVg #ProjectManagement #RiskManagement #MonteCarloSimulation #RISA #ProjectControls #CostEngineering #ConstructionProjects #DataDrivenDecisions #PMO #ScheduleRisk #CostRisk #RiskAnalysis #EngineeringManagement #ProjectControlAcademy

Human error is not the cause… it’s the consequence. We often rush to blame people after incidents: “Why didn’t he follow the procedure?” “Why did she ignore the rule?” But modern safety science tells a different story: When unsafe behavior is repeated, the system "not the person" is usually at fault. Think of a work system that assumes: • The worker never gets tired • Never gets distracted • Always reads instructions • Always makes rational decisions That’s not a system, that’s a fantasy. In the real world? Fatigue, pressure, uncertainty, and repetition are always in play. Poorly designed systems create human error. Well-designed systems reduce the chances of it. Today’s safety thinking embraces the principle of “Designing for Human Error” building procedures and controls that: • Align with human limitations • Reduce complexity • Detect mistakes before they escalate Here’s the truth: Don’t overload the worker. Design the system to support them, not to test them. #SafetyScience #HumanFactors #SafetyByDesign #HSE #LeadershipInSafety #RiskEngineering #NEBOSH #SystemsThinking

🔍 What Is a Risk Assessment Methodology? A risk assessment methodology is the structured approach an organization uses to identify, analyze, evaluate, and prioritize risks. It ensures consistent, repeatable assessments across all business areas and is essential for risk-informed decision-making. ⸻ ✅ Core Components of a Risk Assessment Methodology: 1. Risk Identification • Pinpoint what could go wrong (risk events). • Sources: business processes, historical incidents, regulatory changes, third-party risks, IT systems, etc. • Tools: brainstorming, risk checklists, process walkthroughs, SWOT, interviews, PESTLE. 2. Risk Analysis • Determine the likelihood and impact of each risk. • Approaches: • Qualitative (e.g., High/Medium/Low or Heat Maps) • Semi-quantitative (e.g., scoring systems 1–5 for likelihood and impact) • Quantitative (e.g., Monte Carlo, VaR, financial modeling) 3. Risk Evaluation • Compare risk levels to your risk appetite and tolerance thresholds. • Decide which risks are acceptable, and which need treatment or escalation. 4. Risk Prioritization • Rank risks based on their score to allocate resources effectively. • Often visualized in a risk matrix or heat map. 5. Risk Treatment (Optional in Assessment Phase) • Recommend how to handle critical risks: • Avoid • Transfer • Mitigate (via controls) • Accept 📊 Common Methodologies Used: 1️⃣ISO 31000 Framework Emphasizes integration, structure, and continuous improvement in risk management. 2️⃣ COSO ERM Framework Aligns risk with strategy and performance across governance, culture, and objective-setting. 3️⃣ Basel II/III for Financial Risk Used in banking and finance, focusing on credit, market, and operational risk. 4️⃣ NIST Risk Assessment Applied in cybersecurity and federal agencies, emphasizing threats, vulnerabilities, and impacts. 🎯 Best Practices: • Use both inherent and residual risk ratings. • Involve first-line teams for accurate process-level risk input. • Align methodology with risk appetite and strategic objectives. • Document risk criteria (likelihood/impact definitions) clearly. • Update the risk assessment periodically or after significant events.

🔥 The Dominant Consequence Is Often Not the Dominant Risk ... (Part TWO) "From Consequence to Risk: Why Location and Exposure Matter More Than Severity Alone" We often argue about which consequence is worst: Jet fire? VCE? BLEVE? But that question misses the point. From a single hazardous release, multiple consequences can evolve, each with different footprints, reach, and interaction with people and assets. 🧠 What matters is not just how severe a phenomenon is, but: • Where it occurs • Who is exposed • For how long • How vulnerable the receptor is A few familiar thresholds illustrate are listed below: 🔥 Thermal Effect: Jet Fire / Pool Fire / Fire Ball 37.5 kW/m² → escalation and equipment failure threshold for (3-5 min exposure) 12.5 kW/m² → 30% Lethality for Indoors Onshore & 70% Lethality for Outdoor & Offshore 🔥 Flash Fire: (Within LFL contour)  → 100% Lethality 💥 Explosion Effect (0.5 bar) Overpressure → (50% - 100%) Lethality for Personnel Onshore (0.2-0.3 bar) Overpressure → (100%) Lethality for Personnel Offshore ☠️ Toxic Release (No Ignition) ERPG-2 / AEGL-2 (chemical-specific) → serious irreversible health effects (For about 1hr exposure) This is why tools like PHAST and SAFETI model consequence chains, not single events, and why facility siting and QRA do not depend on severity, but on understanding where and how consequences propagate. 👉 Dominant consequence ≠ dominant risk This distinction is also central to how risk decisions are ultimately made. Risk reduction is not about eliminating the most severe consequence at any cost, but about understanding which scenarios drive exposure often enough to justify additional safeguards. That balance among consequences, frequency, and practicality is where ALARP (As Low As Reasonably Practicable) comes into play. (Will be discussed in coming posts) References: 📚 CCPS Guidelines for Quantitative Risk Assessment 📚 TNO Purble Book & Green Book 📚 IOGP / OGP 434 Reports #ProcessSafety #RiskEngineering #ConsequenceModelling #QRA #FERA #OBRA #FacilitySiting #LossPrevention #ALARP

🚨 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

Risk Assessment. Risk assessment is “The process of quantifying the probability of a risk occurring and its likely impact on the project”. It is often undertaken, at least initially, on a qualitative basis by which I mean the use of a subjective method of assessment rather than a numerical or stochastic (probablistic) method. Such methods seek to assess risk to determine severity or exposure, recording the results in a probability and impact grid or ‘risk assessment matrix'. The infographic provides one example which usefully visually communicates the assessment to the project team and interested parties. Probability may be assessed using labels such as: Rare, unlikely, possible, likely and almost certain; whilst impact considered using labels: Insignificant, minor, medium, major and severe. Each label is assigned a ‘scale value’ or score with the values chosen to align with the risk appetite of the project and sponsoring organisation. The product of the scale values (i.e. probability x impact) resulting in a ranking index for each risk. Thresholds should be established early in the life cycle of the project for risk acceptance and risk escalation to aid decision-making and establish effetive governance principles. Risk assessment matrices are useful in the initial assessment of risk, providing a quick prioritisation of the project’s risk environment. It does not, however, give a full analysis of risk exposure that would be accomplished by quantitative risk analysis methods. Quantitative risk analysis may be defined as: “The estimation of numerical values of the probability and impact of risks on a project usually using actual or estimated values, known relationships between values, modelling, arithmetical and/or statistical techniques”. Quantitative methods assign a numerical value (e.g. 60%) to the probability of the risk occurring, where possible based on a verifiable data source. Impact is considered by means of more than one deterministic value (using at least 3-point estimation techniques) applying a distribution (uniform, normal or skewed) across the impact values. Quantitative risk methods provide a means of understanding how risk and uncertainty affect a project’s objectives and a view of its full risk exposure. It can also provide an assessment of the probability of achieving the planned schedule and cost estimate as well as a range of possible out-turns, helping to inform the provision of contingency reserves and time buffers. #projectmanagement #businesschange #roadmap