Applying Analytical Rigor to High-Risk Decisions

I had a call last week with someone starting their first FAIR analysis. They asked how I spend my time on a typical risk assessment. When I told them I spend more time on scenario building than on the actual quantification, they were surprised. "Isn't the math the hard part?" It's not. The math is the easy part. Getting the scenario right and making sure it connects to a real decision is where most analyses go sideways. Here's the flow I use: Start with the decision. What's the business trying to figure out? "Should we invest in X?" or "How do we prioritize these five risks?" If you don't know what decision you're informing, stop. No amount of analytical rigor will save you. Then clarify the objective. What is the business trying to achieve, and what would success look like? This grounds everything that follows. Now ask: what could derail that objective? Not threats in the abstract, workshop out specific uncertainties that could get in the way. I like to ask: "What's the headline you don't want to read?" Then define the loss event. Get specific: not "data breach" but "an external attacker exfiltrates customer PII from the payments database, triggering regulatory notification and customer churn." Now pick your tool. You've got a well-formed scenario connected to a real decision. How do you analyze it? Sometimes the answer is obvious, and you don't need a formal method at all. When it's not, when you're weighing options, justifying a budget, or comparing unlike risks, that's when quantification shines. Remember that risk quantification isn't the only game in town. Decision trees, cost-benefit analysis, and much more can be the right call depending on the context. Pick what fits the decision, not what you're most comfortable with. Always remember, the output isn't the number. It's the recommendation. The analysis should loop back to that original decision with a clear "here's what we should do and why." FAIR beginners are understandably excited and want to jump straight to quantification, but garbage scenarios produce garbage outputs. Even good analyses are worthless if they don't change a decision. Scenario FIRST, connect it to a decision. The rest will follow.

𝗔𝗤𝗯𝗗 𝗶𝗻 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲: 𝗙𝗿𝗼𝗺 𝗔𝗧𝗣 𝘁𝗼 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲𝗱 𝗠𝗲𝘁𝗵𝗼𝗱 Most AQbD papers stay theoretical. This one does not, and it comes straight from Sanofi's QC lab. Isabelle MOINEAU, Chambon, Perret, Gouit, Zamora, Macumi, Fertier-Prizzon & Pitiot (2024) published an open-access paper in the 𝘑𝘰𝘶𝘳𝘯𝘢𝘭 𝘰𝘧 𝘊𝘩𝘳𝘰𝘮𝘢𝘵𝘰𝘨𝘳𝘢𝘱𝘩𝘺 𝘉 walking through a complete AQbD workflow from ATP to MODR to Analytical Control Strategy for a QC method in a commercial hexavalent vaccine. The application is specific, but the 𝗔𝗤𝗯𝗗 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝘁𝗵𝗲𝘆 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝗶𝘀 𝘂𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹𝗹𝘆 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝗯𝗹𝗲. 𝗧𝗵𝗲 𝗔𝗤𝗯𝗗 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗶𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲: 🔹 𝗔𝗧𝗣: purpose and performance criteria defined upfront, directly linked to release specifications and regulatory requirements 🔹 𝗥𝗶𝘀𝗸 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁: FMEA applied step-by-step across the full method, with transparent RPN scoring to identify and rank CMVs 🔹 𝗗𝗼𝗘 (𝗧𝗮𝗴𝘂𝗰𝗵𝗶 𝗟𝟭𝟴): 7 CMVs studied simultaneously across 18 experiments to establish the MODR 🔹 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: each CMV gets a tailored control measure, whether MODR, SST, procedural control, or training video 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗶𝘀 𝗽𝗮𝗽𝗲𝗿 𝘂𝘀𝗲𝗳𝘂𝗹: ✅ The FMEA scoring rules are fully documented so you can see exactly how risk levels were assigned, not just the outcome ✅ CMVs with different risk levels are handled differently: high risk goes into DoE, medium risk gets a characterization study, low risk is accepted under GMP ✅ The MODR is validated against a quantitative statistical model (R² = 88%), not just a range but a justified design space ✅ The method was successfully submitted to EMA, providing real regulatory validation of the AQbD approach ✅ The authors explicitly link their approach to ICH Q14 and USP <1220>, useful for regulatory positioning At Kantisto - Analytical Experts, sharing AQbD knowledge is at the heart of what we do. If you are looking for a concrete, industry-tested reference for how AQbD translates into a complete method development dossier, this paper belongs in your library. 📄 Open access, free to read and share. Moineau et al., 𝘑𝘰𝘶𝘳𝘯𝘢𝘭 𝘰𝘧 𝘊𝘩𝘳𝘰𝘮𝘢𝘵𝘰𝘨𝘳𝘢𝘱𝘩𝘺 𝘉 1233 (2024) 123946 https://lnkd.in/ewCdTSTE #AQbD #AnalyticalDevelopment #MethodValidation #ICH #QbD #AnalyticalQualityByDesign #Pharma #Vaccines #RegulatoryScience #ICHQ14 Sanofi

Point-of-Care Testing (POCT) has transformed how quickly clinical decisions are made. But as POCT scales across healthcare networks, speed alone is no longer the challenge. The real question is: how do we ensure POCT remains safe, reliable, and clinically meaningful at scale? High-reliability POCT is not achieved by technology alone. It is built through system design, governance, and objective measurement across the entire testing lifecycle. From Results to Reliability POCT performance is often judged by isolated checks: Did QC pass? Was a result produced? Was the device available? These are necessary, but not sufficient. High-reliability POCT requires us to demonstrate that: Results are assigned to the correct patient High-risk results are recognised, communicated, and acted upon Analytical issues are detected before patient impact Controls work across the system, not just locally A Practical Framework: Risk → Control → Proof Across our POCT network, every testing domain is assessed using the same framework: Risk – What could go wrong and impact patient safety? Control – What prevents, detects, or mitigates that risk by design? Proof – What objective evidence shows the control is effective? This structure applies consistently across pre-examination, examination, and post-examination phases — and it scales. Why This Matters Many of the greatest POCT risks sit outside the analyzer: Patient identification errors Undetected analytical drift across devices Missed or undocumented critical results Hidden safety patterns that only emerge at scale High-reliability systems address these risks through embedded controls, network-level surveillance, and meaningful KPIs that reflect clinical risk — not operational convenience. The Takeaway High-reliability POCT is not about doing more checks. It is about measuring the right things, in the right way, across the entire system. When POCT is designed around risk, controlled through workflow and governance, and proven through objective data, it becomes not just fast, but safe, consistent, and trustworthy at scale. That is what measuring what matters truly means.

Hi - I'm Steve. I am a professional fail-er. Many times data teams are asked questions that pertain to things that the business has never done before. This might be creating opportunity sizing for a new feature, forecasting adoption or performance of our customers, or building recommendations, either for the business or the customers to suggest relevant improvements or features to adopt. The challenge with many of these problems is that there's not always a black-or-white answer, and in addition, we tend not to have complete datasets that enable us to paint the full picture. As a result, we end up having to build assumptions into our models, basing this off of past experience, similar features, user behavior and other correlational analysis. Data teams that are not comfortable with the concept of failing fast can fall into the pitfall of 'paralysis by analysis', whereby we fail to make a recommendation due to the uncertainty that implicitly exists in the data. The easiest thing to do to delay a project or deliverable is to ask for more data, which inevitably will beget more questions and sometimes cause us to lose sight of the goal we were trying to accomplish in the first place. A much more effective approach, I have found, is to clearly draw out what assumptions we must make to size the feature, or conduct the analysis. Establish clearly the risk of us being wrong on any of those assumptions, and clearly evaluate one-way (irreversible) and two-way (reversible) decisions. The goal is to have enough 'low stakes' experiments, where we can easily roll back the change to gain enough confidence in the assumptions you must make for the 'high stakes' decisions where reversing the change is either incredibly costly, or sometimes infeasible. Through this approach, we're able to dedicate a lion share of the analysis time firming up the hypothesis we must make for the 'high risk' decisions, and apply the highest level of rigor in terms of experimentation and burden of evidence. 'Low risk' areas enable us to broaden our scope of knowledge of the product, building confidence in our assumptions, and creating data for us to explore 'why wasn't my assumption accurate?' Creating controlled environments to fail fast will not only enable you to learn faster, but it will enable teams to build confidence in their abilities to test their assumptions and debug when the stakes are high. If you create an environment where *every* decision requires an insurmountable burden of evidence, you run the risk of stifling innovation and having a data team that's not equipped to debug situations when our assumptions were inevitably wrong. My suggestion to data teams is to embrace (controlled) failure. No one asks the question 'why did this roll-out go so well?', but certainly the question always arises 'what went wrong' when our predictions do not materialize. Ensure you're prepared for those situations by learning *how* to fail.

Folks around San Jose may have caught LatentView Analytics' billboards in a couple of places recently. The message is simple—One smart decision changes the game. I am reminded of a project we worked on a few years ago, where we built a fraud detection model for an e-commerce client. We used everything available: device data, spend velocity, location, and transaction history. The model was good. But it had plateaued. No matter what we tuned, performance wouldn’t move. That’s when we made a smart decision. We decided to step back and look at the situation from a different perspective. Instead of asking, “How do we improve this?” we asked, “What is the data not telling us?” That single shift in perspective uncovered a massive blind spot. We realized the systems were only storing successful transactions. All the failed attempts in the same session weren't being captured at all. But that is exactly how fraud works. A fraudster usually tries multiple times before a transaction finally goes through. So we added one simple signal: If there were three or more failed attempts in the same session before a success, we flagged it as high risk. That one decision transformed everything. Accuracy jumped by around 95%. Sometimes, the smartest decision you can make is to step back and look at the problem from a different perspective. That is how you change the game.

#Risk_Based_Inspection (#RBI) is a strategic approach used in high-risk industries (oil & gas, petrochemicals, power) to optimize inspection planning by focusing resources on equipment posing the highest risk. It balances safety, reliability, and cost-effectiveness by prioritizing inspections based on a structured assessment of probability of failure (#PoF) and consequences of failure (#CoF). #Key_Components: 1. #Probability_of_Failure (#PoF): Evaluates likelihood of equipment failure using factors like corrosion rates, material degradation, operating conditions (temperature, pressure), historical inspection data, and maintenance history. Predictive models or industry standards (e.g., API 581) often guide this analysis. 2. #Consequences_of_Failure (#CoF): Assesses impact if failure occurs, including safety hazards (injuries, fatalities), environmental damage (spills, emissions), production downtime, repair costs, and reputational harm. Quantitative methods may assign monetary values to these outcomes. #Benefits: - #Enhanced_Safety: Targets high-risk assets, reducing catastrophic failure risks. - #Cost_Efficiency: Reduces unnecessary inspections, minimizing downtime and labor costs. - #Regulatory_Compliance: Aligns with standards (e.g., API 580, API 581, ISO 31000) and demonstrates proactive risk management. - #Data_Driven_Decisions: Uses analytics to prioritize actions, extending asset lifespan. #Implementation_Steps: 1. #Asset_Criticality_Ranking: Identify equipment critical to operations/safety (e.g., pressure vessels, pipelines). 2. #Data_Collection: Gather design specs, operating conditions, corrosion data, and past inspection reports. 3. #Risk_Assessment: Calculate risk as Risk = PoF × CoF, categorizing assets into low/medium/high risk. 4. #Inspection_Planning: Allocate resources to high-risk assets, adjusting methods (e.g., #NDT techniques) and frequencies. 5. #Continuous_Monitoring: Update risk profiles post-inspection, incorporating real-time data (e.g., IoT sensors, predictive analytics). #Challenges: - #Data_Quality: Relies on accurate, up-to-date operational and inspection records. - #Expertise_Requirements: Demands skilled personnel for risk modeling and interpretation. - #Initial_Costs: Setup (software, training) can be resource-intensive, though long-term savings offset this. #Technological_Advancements: - #Predictive_Analytics: Machine learning models forecast degradation trends. - #IoT_Sensors: Enable real-time monitoring of parameters like thickness or vibration. - #Digital_Twins: Simulate asset behavior under varying conditions to refine risk assessments. #Standards: - #API_580/#API_581: Provide RBI methodologies and quantitative risk calculations for the process industry. - #ISO_31000: Offers broader risk management guidelines applicable to RBI.

A prospective client recently asked KSS to provide three close protection operatives for a week-long business development sprint across two major cities. I asked the question: 'Why three?' The number was already fixed in his mind. The expectation was that we would validate it, deploy accordingly, and move straight to pricing. This is precisely where our industry too often loses its way. Many providers begin with staffing, not risk. The default first step becomes, “How many people do you want?” rather than, “What are we actually protecting against?” In that moment, negotiating headcount would have been the wrong move. The correct approach was to work backwards from intelligence. Before discussing any deployment model, we needed to understand a few fundamentals: • What credible threats existed for the Principal, and were specific to each city - not what felt uncomfortable, but what could be evidenced. • Where the client was genuinely vulnerable. • What the realistic consequences of an incident would be. • What controls already existed. Only after that analysis could we determine whether one operative, two, three - or a different model entirely - was proportionate, defensible, and effective. Headcount should be the last decision, not the first. Credible protection must be reverse engineered from intelligence. People, technology, and procedures should be the product of informed analysis, never its substitute. This is why rigorous, clearly defined risk assessments are non-negotiable. They are not merely paperwork, they are the backbone of proportionate planning, compliance, and defensible governance when decisions are later scrutinised by regulators, insurers, or courts. When intelligence leads, decisions are precise and justifiable. When it doesn’t, even the most elaborate and advanced security posture is fragile. Boards must stop treating security as a downstream staffing exercise and recognise it for what it is - an upstream governance responsibility. Presence for presence’s sake is not protection. It's performative. #closeprotection #executiveprotection #travelrisk

Measure Twice, Sign Once: Verifying AI-Generated Risk Analysis Before Making Decisions Meanwhile, GenAI supports humans in every aspect of corporate risk management, or should I say, it replaces humans in every aspect? Indeed, GenAI continues to make some mistakes. Sometimes, it fails to reason like an experienced CRO. Sometimes, it treats the board’s risk appetite as if it were the firm’s risk-bearing capacity; sometimes it computes the chance that at least one risk will occur in a portfolio of positively correlated risks incorrectly; sometimes it solves a risk problem analytically instead of simulation-based. Sometimes it offers industry benchmark risk data, such as risk exposures, accepted risk appetite limits, or key risk lists; however, upon reviewing the sources and numbers, it becomes clear that these figures are entirely hallucinated. The benefits of GenAI in risk management are rising at an incredible pace, but so is the risk of relying solely on these outputs. These problems do not disqualify the technology; it's more the humans who don’t understand its (current) limitations and lack risk management knowledge. Viewing these GenAI-produced outputs as those of a junior whose drafts require senior human review and refinement is a good idea: First, apply independent verification. For example, we can replicate the most crucial analyses using Monte Carlo simulation, scenario analysis, or structured expert judgment. Once our model’s error rate lies within an acceptable limit, we can move to a stability phase, in which we may select only a small sample of highly relevant risk analysis outputs for replication each time before making important decisions. Second, every verified risk figure could be enriched by a decision-supportive narrative, translating the quantified consequence into business targets. Human narratives and AI outputs collaborate to ensure that decision-makers understand why the analysis is relevant and how it affects the decision at hand. Third, hallucination back-testing is a procedure that can be used after every AI-generated risk analysis. Regularly select a few of the model’s analysis outputs, for example, the “22 % chance that a major project is two months late,” and compare them with trusted empirical data, such as the last ten years’ project records. If a sampled risk number deviates by more than the allowed margin, the AI failure is recorded, and an additional human double-check is required. GenAI has earned its place in risk management, but only within some “human safeguards”. I use it daily for risk management purposes. My digital twin is likely already more knowledgeable and consistent than I am, in many cases, but not in all. And that is where skilled humans still have their crucial place: Challenging, contextualizing, and noticing when AI is wrong. Institut für Finanzdienstleistungen Zug IFZ Lucerne University of Applied Sciences and Arts

Statistical analysis elevates corrosion monitoring far beyond misleading averages, providing the crucial lens to distinguish between benign uniform wear and catastrophic localized threats. By moving from reactive guesswork to predictive modeling based on standards like API 574, engineers can anticipate failures in high-risk zones (e.g., pipe elbows) before they occur. This data-driven approach transforms inspection strategies, optimizing resource allocation by focusing solely on areas that genuinely threaten mechanical integrity. Mastering these statistical tools is not just an exercise in compliance; it is the foundation of proactive asset management and the definitive shield against unexpected process failures.

🛡️ LOPA vs Risk Matrix vs Risk Graph — What’s the Difference and When to Use Each? Risk assessment is central to functional safety, but confusion arises when different tools are mixed without understanding their purpose. These three methods often appear in HAZOPs and SIL studies: • Risk Matrix • Risk Graph • LOPA They are complementary tools, not competitors, for different levels of decision-making. 📊 Risk Matrix A qualitative or semi-quantitative tool ranking risk using consequence and likelihood. Where it fits: • Early project phases • High-level screening during HAZOP • Prioritising hazards Strengths: • Simple, fast, and easy to communicate • Good for initial risk ranking Limitations: • Subjective • Poor resolution between risk levels • Not suitable for SIL justification 📈 Risk Graph Structured, semi-quantitative method (IEC 61508 / 61511). Uses consequence, exposure frequency, probability of avoidance, and demand rate. Where it fits: • SIL determination workshops • When more structure is needed than a risk matrix • Early SIL targeting Strengths: • More consistent than a risk matrix • Standardised logic • Widely accepted Limitations: • Still relies on judgement • Limited transparency once SIL assigned • Can oversimplify complex scenarios ⚖️ LOPA (Layer of Protection Analysis) Semi-quantitative method that evaluates independent protection layers and calculates risk numerically. Where it fits: • SIL validation & justification • High-risk or complex scenarios • Regulatory & audit-driven environments Strengths: • Transparent & traceable • Shows risk reduction from each layer • Strong basis for SIL assignment Limitations: • Requires quality data • More time & expertise needed • Not ideal for early screening 💡 Key Takeaway Think of these tools as a progression, not a choice: Risk Matrix → Risk Graph → LOPA As risk increases and decisions become critical, analysis must become more rigorous. ⚠️ Common Mistake: Using a risk matrix or risk graph alone to justify a high SIL is a red flag in audits. For higher-risk scenarios, LOPA or equivalent quantitative methods must support the decision. #FunctionalSafety #LOPA #RiskAssessment #RiskMatrix #RiskGraph #IEC61511 #IEC61508 #ProcessSafety #SIL #SIS #SIF #HazardAnalysis #HAZOP #OilAndGas #Mining #ChemicalEngineering #Instrumentation #ControlSystems