Science Risk Assessment Methods

🔍 HAZID vs HAZOP In process safety, selecting the right risk assessment method at the right time makes a critical difference. HAZID (Hazard Identification) is a risk identification technique focused on identifying potential hazards associated with a process, system, or operation typically at an early stage. It is largely based on structured brainstorming and aims to answer a simple but powerful question: “What could go wrong?” HAZOP (Hazard and Operability Study), on the other hand, is a far more detailed and systematic approach. It focuses on process flow and examines hazards arising from deviations in key process parameters such as flow, temperature, and pressure. Using guidewords and a structured methodology, HAZOP studies thoroughly evaluate deviations from design intent, identify hazards, and define corrective actions. 🎯 In summary 💥 HAZID captures risks early and at a high level, ⛔ HAZOP dives deep into process behavior and deviations. 👉 Right analysis, right time = safer facilities.... #ProcessSafety #HAZID #HAZOP #RiskAssessment #Engineering #SafetyCulture

How do we safeguard the openness and transparency that global science depends on while managing growing security risks? That question is at the heart of our latest OECD blog post on research security by Carthage Smith and Yoran Beldengrün, PhD. The post describes what research security is, why it matters, and what countries are already doing to get ahead of the issue. A few key takeaways: 1. Research security risks are diverse and acute. These include data breaches, misappropriation of research findings, undue foreign influence, and challenges to academic freedom, particularly in areas involving sensitive or dual-use technologies. 2. Policy responses are shifting from awareness to action. In just a few years, countries have moved from general guidance to concrete measures: formal strategies, oversight bodies, disclosure rules, training, and risk-assessment tools. 3. A balanced approach is essential. Strong research security frameworks must coexist with open scientific exchange to maintain trust, attract talent and ensure high-quality research. At the OECD - OCDE, we’ll continue to support countries in sharing evidence, learning from each other, and building policies that protect both scientific excellence and shared values. Read more here: https://lnkd.in/eFXmNgSr

𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 𝐃𝐚𝐢𝐥𝐲 𝐂𝐡𝐞𝐜𝐤𝐥𝐢𝐬𝐭 𝐟𝐨𝐫 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐀𝐧𝐚𝐥𝐲𝐬𝐭𝐬 🔹 SIEM Alerts: Begin your day by thoroughly reviewing the critical and high-severity alerts generated by the Security Information and Event Management (SIEM) system over the past 24 hours. Prioritize alerts that could indicate potential breaches or significant threats to the organization’s cybersecurity posture. 🔹 Firewall and Intrusion Detection System (IDS) Logs: Examine the logs from firewalls and IDS for any blocked connections, unusual port scanning activities, or unexpected traffic patterns. Pay special attention to any repeated incidents that could suggest a persistent threat or reconnaissance activity. 🔹 Authentication Logs: Analyze the authentication logs to identify any instances of failed login attempts, unusual sign-ins from atypical locations, or access patterns that deviate from the norm. This helps in spotting potential unauthorized access attempts or compromised accounts. 🔹 Endpoint Security Status: Confirm that all Endpoint Detection and Response (EDR) and antivirus (AV) solutions are operational and updated to the latest version. Check for any outstanding security threats that may require immediate attention, ensuring all endpoints are secure. 🔹 Backup Verification: Verify the status of overnight backups to ensure they were successfully completed. Investigate any failures promptly, as data integrity and availability are crucial to the organization’s operations. 🔹 Patch Updates: Keep an eye on any critical Common Vulnerabilities and Exposures (CVEs) or zero-day exploits that have been reported. Check the patch management status across all systems to ensure timely updates are applied to protect against known vulnerabilities. 🔹 Threat Intelligence Monitoring: Review threat intelligence feeds for any new Indicators of Compromise (IOCs) or ongoing campaigns that could pose a risk to your industry. Staying informed about emerging threats helps in adapting your defensive measures accordingly. 🔹 User Reports Review: Take the time to go through any reports from users regarding phishing attempts or suspicious activities they may have encountered. This information can provide valuable insights into potential vulnerabilities or human factors in security breaches. 🔹 System Health Check: Ensure that all key security tools—including SIEM systems, firewalls, and EDR solutions—are functioning properly and have no operational issues. This ensures the integrity of your security architecture and readiness to respond to incidents. 🔹 Documentation and Escalation: Document any suspicious findings or anomalies you encounter during your review process. If any serious concerns arise, escalate them to the appropriate response team for further investigation and action. 𝐃𝐢𝐬𝐜𝐥𝐚𝐢𝐦𝐞𝐫 - This post has only been shared for an educational and knowledge-sharing purpose related to Technologies. #technology #cybersecurity #ciso

🧠 Quantum computing: What business leaders need to do right now Right now, criminal and state-sponsored hackers are intercepting and storing encrypted data they cannot yet decode. Likely targets include everything from corporate secrets and medical records to legal agreements and military communications. Why would these actors bother to steal data they can’t read? Because they are betting on developments in quantum computing that will eventually let them crack this encrypted data wide open. This isn’t a fringe theory. The NSA (National Security Agency), NIST (National Institute of Standards and Technology), and ENISA (European Agency for Cybersecurity) are all treating this “harvest now, decrypt later” scenario as a live threat that is serious enough to demand immediate action. The NSA has mandated that all U.S. national security systems must transition to quantum-resistant cryptography by 2035—with new acquisitions required to be compliant by 2027. In Europe, ENISA issued updated guidance in April 2025 warning that the threat is “sufficient to warrant caution, and to warrant mitigating actions to be taken,” and recommending that organizations begin deploying post-quantum cryptography immediately. NIST has launched a parallel global effort to develop the new cryptographic standards on which these transitions will depend. The message from all three bodies is the same: Organizations run a grave risk if they wait to begin upgrades until quantum computers can break current encryption standards. That is the reason business leaders need to pay attention to quantum computing now — not because the technology is ready, but because the risk is grave, and the cost of preparation is trivial compared with the cost of being caught flat-footed. 🔗 Find out how in our new Fast Company article here: https://lnkd.in/g54y88UE.

Risk Based Inspection in the age of Industry 4.0 Traditional Risk Based Inspection has served industry well, but the actual risk is not static. We assess risk at a point in time, then wait months or years before reassessing → hoping nothing significant changes in between. In the age of Industry 4.0 and Predictive Analytics, that approach is rapidly becoming obsolete. The evolution from traditional to monitoring-enhanced RBI represents more than just technological advancement → it's a fundamental shift in how we understand and manage asset integrity. Traditional RBI Foundations: Built on API 580 and 581 standards, traditional RBI provides structured frameworks for calculating Probability of Failure (PoF) and Consequence of Failure (CoF). These periodic, static assessments create inspection schedules based on risk rankings at specific moments in time. The monitoring enhanced Evolution: Modern RBI integrates real-time sensor data, predictive analytics, and machine learning to create dynamic risk profiles that evolve continuously. Instead of waiting for scheduled reassessments, risk calculations update automatically as conditions change. Here are the key technological enablers: → Smart sensors and IoT networks providing continuous condition monitoring → Data-driven FMEA models that identify failure patterns humans might miss → Predictive Analytics simulate degradation scenarios under various operating conditions → Risk visualization platforms that make complex data accessible to decision-makers API Standards Integration: This evolution aligns with existing API frameworks → 580/581 for quantitative risk modeling. The transformation delivers tangible benefits: earlier anomaly detection, optimized inspection planning, reduced costs, and enhanced regulatory compliance. Most importantly, it transforms risk management from a periodic exercise into a continuous capability. The technology exists today to make this transition. The question is not when but how fast the organizations will adopt this evolution or wait for others to prove its value. How is your facility preparing to integrate real-time data into your risk-based inspection strategy?

Tail risk refers to the likelihood and impact of rare, extreme moves in investment returns typically those beyond three standard deviations from the mean events that standard normal-based models fail to capture Real-world return distributions exhibit excess kurtosis meaning extreme outcomes (both losses and gains) occur more often than a normal distribution would predict Practical Techniques to Model Tail Risk 1. Value at Risk (VaR) & Expected Shortfall (ES / CVaR) VaR computes the maximum expected loss at a given confidence level (e.g., 95% or 99%) over a certain horizon. It's simple but doesn't capture the magnitude of losses beyond that threshold Expected Shortfall (ES), aka Conditional VaR (CVaR) or Tail VaR, measures the average loss in the worst-case tail beyond the VaR threshold—offering a more comprehensive view of tail behavior ES is coherent and subadditive (unlike VaR), making it more suitable for portfolio risk management In practice, ES can be computed using closed-form formulas for certain distributions or via simulation (e.g., Monte Carlo) 2. Extreme Value Theory (EVT) / Peaks-Over-Threshold (POT) Focuses on modeling the tail distribution directly, rather than the entire return distribution. The POT method fits a Generalized Pareto Distribution (GPD) to the values that exceed a high threshold sidestepping parametric assumptions over the full range EVT approaches are highly practical in risk management used for forecasting VaR and ES more accurately, especially when data exhibit heavy tails Academic work shows combining GARCH filtering for volatility clustering with EVT on residuals improves tail risk estimates 3. GARCH and Time-Series Models Return volatility clusters over time. GARCH (and its variants) models this conditional heteroskedasticity: ARCH/GARCH models estimate time-varying volatility, improving tail risk estimates by accounting for changing market regimes These models are often paired with EVT for enhanced tail modeling: filter returns via GARCH, then apply EVT (like POT) to the standardized residuals 4. Stochastic‐Volatility and Jump Models (SVJ) These models capture both volatility dynamics and discontinuous jumps: SVJ models (e.g. Bates, Duffie–Pan–Singleton) blend stochastic volatility with jump components, enabling fat tails, skewness, volatility clustering, and large jumps all in one model They’re particularly useful for tail risk modeling in derivatives pricing and hedging applications thanks to their market realism 5. Copulas for Multivariate Tail Risk To model joint tail dependencies across assets: Copulas enable constructing joint distributions from individual marginals, capturing dependence structures including during extreme events Useful for portfolio-level tail risk, systemic risk, or stress testing scenarios where multiple assets may suffer extreme losses simultaneously 

Last week, Ethereum announced it is forming a post-quantum working group because they can read the room: cryptography isn’t a “future upgrade,” it’s a ticking dependency and a grown-up admission that digital trust has a shelf life. In 𝑵𝒐𝒘 𝑾𝒉𝒂𝒕? I called this the Big Crunch: the moment quantum collapses the economics of breaking today’s public-key cryptography. Unlike Y2K, this isn’t a bug you patch. It’s a global migration you either start early or you finish in panic. And timelines are already wobbling, Google research from 2025 suggested breaking RSA could need 20x fewer qubits than previously thought of. Unfortunately, most leaders treat quantum like a storm on the horizon: “interesting, but not today.” That’s a mistake. Attackers can already copy encrypted traffic and files now, store it, and unlock it later when quantum tools get good enough. That’s not theory. It’s a rational investment strategy from an adversary's perspective. And if a major system ever gets quietly cracked, you won’t hear about it when it happens. You’ll hear about it after someone has made money from it. After all, the incentives reward silence; think Enigma, but automated, monetized and at scale. The smart path is boring, but effective: start upgrading before the break, and form working groups like Ethereum to start today. It also means running hybrid encryption, today’s algorithms paired with post-quantum ones, across the places where trust lives: web connections (TLS), logins and identity, enterprise software, key management and HSMs, cloud services, and blockchain signatures. Do it early and you turn a cliff-edge event into a controlled rollout. Wait too long and it’s not just your future data at risk, old encrypted backups, archived emails, contracts, customer records, IP can become readable years later. In other words: you don’t just lose security going forward. You lose your history.

BAYESIAN REGRESSION: FROM POINT ESTIMATES TO PROBABILITY DISTRIBUTIONS 📊 In empirical finance and economics, classical OLS regression often gives us false confidence through point estimates that ignore parameter uncertainty. When sample sizes are small or data is noisy—common in emerging markets or early-stage ventures—this overconfidence can lead to costly decisions. 🎯 The Bayesian approach transforms regression from a single "best fit" line into a rich distribution of plausible relationships, naturally quantifying our uncertainty about model parameters. The fundamental shift in thinking: Classical: "The true slope is 1.47 (±0.23)" Bayesian: "Given the data, we believe the slope is most likely around 1.47, with 90% probability between 1.05 and 1.89" This probabilistic framework offers three key advantages for applied research: 📈 Prior Integration: Incorporate domain expertise or previous studies directly into the analysis—invaluable when working with limited data or combining multiple information sources 🔄 Natural Uncertainty Propagation: Parameter uncertainty flows seamlessly into predictions, giving honest confidence intervals that reflect both estimation uncertainty and inherent variability 📊 Richer Inference: Extract any quantity of interest from posterior distributions—tail risks, probability of economic significance, or decision-theoretic optimal choices Grid approximation, while computationally limited to low dimensions, provides profound value. By discretizing the parameter space and computing posteriors explicitly, we demystify the "black box" of Bayesian inference—making it accessible to practitioners and stakeholders alike. Real-world applications where this matters: • Estimating risk premia with short time series • Policy evaluation with limited pilot data • Cross-border investment decisions under regime uncertainty • Incorporating expert judgment in forensic economics • Robust forecasting when historical relationships may be shifting The beauty lies not in abandoning classical methods, but in acknowledging when uncertainty quantification becomes as important as point estimation itself. Currently exploring applications in financial econometrics and decision science—always interested in connecting with researchers and practitioners tackling similar challenges! What domains in your work could benefit from honest uncertainty quantification? 🤔 #BayesianEconometrics #QuantitativeFinance #DataScience #RiskAnalysis #EmpiricalResearch #StatisticalModeling

FOOD HAZARDS-THE BIGGEST THREAT TO FOOD SAFETY A hazard is defined as a biological chemical or physical agent in a food or condition of the food with the potential to cause an adverse effect. Biological hazards are living organisms, including microbiological organisms, bacteria, viruses, fungi, and parasites. Chemical hazards are in two categories: naturally occurring poisonous substances and chemicals or deleterious substances. The first group covers natural constituents examples being aflatoxins and shellfish poison. The other group covers poisonous chemicals or deleterious substances that are intentionally or unintentionally added to food at some point in the production chain, examples are pesticides and fungicides as well as lubricants and cleaners. A physical hazard is any material not normally found in food that causes illness or injury. Physical hazards include glass, wood, stone, bone, and metal. RISK ANALYSIS APPROACH Risk analysis plays an important role for a National Food Control System. It is a powerful tool to carry out science-based analysis and to achieve a sound and consistent solution to food safety problems. It provides information on hazards in food to be linked directly to data on the risk to human health and to improve the food safety decision-making process. How Risk can enter into the food chain? Production: Poor agriculture practices Processing: Improper handling and processing, storage, and packaging Transportation: Improper unhygienic transportation Retail: Poor hygiene and sanitation The FSS Act 2006 defines: Risk assessment is a scientifically based process consisting of four steps: Step 1 Hazard identification: “Could this food or anything in it be harmful?’ Risk assessors collect and review scientific data and identify biological or chemical hazards in food. Step 2 Hazard characterization: “What effects do the hazards cause?” Risk assessors evaluate scientific data to determine whether evidence is strong enough to demonstrate that a substance has the potential to cause harm and the nature of the harm. Step 3 Exposure assessment: “Who may be harmed and at what level of exposure may be harmful?’ Experts estimate how much of the food or ingredient consumers in general population groups (e.g. infants, children, adults) or sub-populations (e.g. vegetarians, vegans) are likely to be exposed to under real-life conditions where both dose and duration are considered. . Step 4: Risk characterization: “How likely is it that people will experience exposure at a level that can cause harm in real life?’ The level of exposure that can cause harm is compared to the actual level of exposure that someone would experience in real life. Risk management is the process of weighing policy alternatives in consultation with all interested parties, considering risk assessment and other factors relevant to the health protection of consumers, and if needed, selecting appropriate prevention and control measures

The AI workflow produced great results, yet people did not feel safe relying on the output. ⛔ That was the situation I encountered in a client workshop in Brussels last week, and it is far more common than most organisations like to admit. The team had invested time and effort into designing an AI-supported workflow. The use case was clear, the technical setup was sound, the data quality was acceptable, and the people involved had already received training on how to use AI. Despite all of this, the workflow was barely used in practice. People ran the AI step, reviewed the output, and then quietly redid the work themselves. During the workshop, we mapped the real workflow together, step by step, focusing not on how the process was documented but on how the work actually happened on a normal working day. At one point, a participant looked at the whiteboard and said: “I only trust the result after I have checked it myself anyway.” That sentence shifted the entire conversation. As we continued mapping the process, a pattern became visible: Everyone validated AI outputs differently.  Some checked everything, even low-risk drafts.  Others barely checked high-risk decisions. Accountability was assumed but never explicitly defined. Human validation was happening constantly, but it was invisible, inconsistent, and highly personal. We redesigned the workflow and introduced a simple checklist for built-in human validation. 💡 This checklist replaced individual safety habits with a shared, explicit process. ✅ Define the risk level of the output. Clarify whether the AI output is a draft, a recommendation, or a decision with external impact. ✅ Decide if validation is required. Make it explicit which outputs require human review and which can flow through without intervention. ✅ Specify the validation moment. Define when validation happens in the workflow and before which downstream step. ✅ Assign clear responsibility. Name the role that validates the output and the role that makes the final decision. ✅ Separate generation from judgment. Ensure the AI prepares content or options, while humans remain accountable for approval and outcomes. ✅ Remove unnecessary checks. Regularly review the workflow to eliminate validation steps that add friction without reducing risk. Once this checklist was applied, people felt much more confident about the AI output because they knew when human judgment was required. 👉 Is human validation in your AI workflows clearly designed, or is it still improvised? Let’s discuss.