Science-Based Policy Implementation

“Mapping Cybersecurity Threats to Defenses: A Strategic Approach to Risk Mitigation” Most of the time we talk about reducing risk by implementing controls, but we don’t talk about if the implemented controls will reduce the Probability or Impact of the Risk. The below matrix helps organizations build a robust, prioritized, and strategic cybersecurity posture while ensuring risks are managed comprehensively by implementing controls that reduces the probability while minimising the impact. Key Takeaways from the Matrix 1. Multi-layered Security: Many controls address multiple attack types, emphasizing the importance of defense in depth. 2. Balance Between Probability and Impact: Controls like patch management and EDR reduce both the likelihood of attacks (probability) and the harm they can cause (impact). 3. Tailored Controls: Some attacks (e.g., DDoS) require specific solutions like DDoS protection, while broader threats (e.g., phishing) are countered by multiple layers like email security, IAM, and training. 4. Holistic Approach: Combining technical measures (e.g., WAF) with process controls (e.g., training, third-party risk management) creates a comprehensive security posture. This matrix can be a powerful tool for understanding how individual security controls align with specific threats, helping organizations prioritize investments and optimize their cybersecurity strategy. Cyber Security News ®The Cyber Security Hub™

This image is from an Amazon Braket slide deck that just did the rounds of all the Deep Tech conferences I've been at recently (this one from Eric Kessler). It's more profound than it might seem. As technical leaders, we're constantly evaluating how emerging technologies will reshape our computational strategies. Quantum computing is prominent in these discussions, but clarity on its practical integration is... emerging. It's becoming clear however that the path forward isn't about quantum versus classical, but how quantum and classical work together. This will be a core theme for the year ahead. As someone now on the implementation partner side of this work, and getting the chance to work on specific implementations of quantum-classical hybrid workloads, I think of it this way: Quantum Processing Units (QPUs) are specialised engines capable of tackling calculations that are currently intractable for even the largest supercomputers. That's the "quantum 101" explanation you've heard over and over. However, missing from that usual story, is that they require significant classical infrastructure for: - Control and calibration - Data preparation and readout - Error mitigation and correction frameworks - Executing the parts of algorithms not suited for quantum speedup Therefore, the near-to-medium term future involves integrating QPUs as accelerators within a broader classical computing environment. Much like GPUs accelerate specific AI/graphics tasks alongside CPUs, QPUs are a promising resource to accelerate specific quantum-suited operations within larger applications. What does this mean for technical decision-makers? Focus on Integration: Strategic planning should center on identifying how and where quantum capabilities can be integrated into existing or future HPC workflows, not on replacing them entirely. Identify Target Problems: The key is pinpointing high-value business or research problems where the unique capabilities of quantum computation could provide a substantial advantage. Prepare for Hybrid Architectures: Consider architectures and software platforms designed explicitly to manage these complex hybrid workflows efficiently. PS: Some companies like Quantum Brilliance are focused on this space from the hardware side from the outset, working with Pawsey Supercomputing Research Centre and Oak Ridge National Laboratory. On the software side there's the likes of Q-CTRL, Classiq Technologies, Haiqu and Strangeworks all tackling the challenge of managing actual workloads (with different levels of abstraction). Speaking to these teams will give you a good feel for topic and approaches. Get to it. #QuantumComputing #HybridComputing #HPC

Most enterprises treat quantum computing as a nerdy R&D curiosity. A mistake. Critical business problems, which are fundamentally constrained by classical computing today, are likely to be solved by 2030. With a hybrid combination of high performance computing and quantum approaches. Three sectors stand out: Pharma, Life & Material Sciences: Drug discovery is essentially a molecular simulation challenge. Classical systems approximate. Quantum systems are designed around quantum mechanics itself. Thus, it is not just about faster research, but the ability to model molecular interactions with higher fidelity. For protein folding, compound optimization, personalized therapeutics. Reaching quantum advantage first in pharma won’t merely accelerate pipelines — it will redefine them. Financial Services: Banks, insurers, stock exchanges operate enormous optimization, transaction or probability engines. E.g., for risk simulations, or fraud detections. Many of these problems scale exponentially in complexity. Quantum algorithms are particularly promising where classical Monte Carlo simulations hit practical limits. And, quantum computing is becoming a cybersecurity challenge. Post-quantum cryptography migration will likely be one of the largest infrastructure transitions the financial sector has seen for decades. Complex Logistics & Supply Chains: Airlines, shipping companies, manufacturers, energy grids, and global retailers all face combinatorial optimization problems. These systems already operate at scales where small efficiency gains create major business impact. Enterprises operating in these segments should get „quantum-ready“ now: • Identify quantum-relevant business problems • Work with quantum partners who advocate an open approach • Build internal quantum literacy • Develop hybrid workflows • Prepare your security stack for the post-quantum era. Additionally we need quantum computing companies delivering at production scale. IQM Quantum Computers calls this Production Quantum. Which is the delivery of a production-ready full stack solution rather than just a scientific solution for a specific problem. This is the same pattern we saw with #AI. The competitive gap formed before the technology fully matured. #Quantum readiness is becoming a strategic capability and critical timing question. For an increasing number of enterprises. Not only for R&D departments.

Fixing Agriculture’s Core Issue: Market Linkage and Policy Bias!! Farmers feed the world, yet many struggle to access markets that fairly value their produce. This market linkage gap, combined with policies prioritizing cheap food for consumers, traps farmers in poverty, threatens food security, and stifles agricultural progress. With smallholders producing 70% of global food, solving this is urgent. Why It Matters Poor market access costs farmers billions—40% of produce in sub-Saharan Africa alone rots before reaching buyers. Meanwhile, policies like price caps and subsidies keep basic commodities like grains and rice affordable for consumers but depress farmgate prices, penalizing farmers. This dual challenge demands bold solutions. Key Barriers Weak Infrastructure: Poor roads and storage cause massive post-harvest losses. Information Gaps: Farmers lack real-time market data, leaving them vulnerable to exploitative value chains. Limited Networks: Smallholders miss out on large markets due to scale and connections. Financial Constraints: No credit means no investment in quality or technology. Policy Bias: Price controls and consumer-focused subsidies undervalue farmers’ work, as seen in systems like India’s MSP, which often favor select crops. Solutions That Work Tech Platforms: Apps today connect farmers to buyers, boosting incomes by 30%. Better Infrastructure: Public-private investments in roads and cold chains cut losses. Cooperatives: Models like Kenya’s Tea Agency show collective bargaining unlocks global markets. Value Addition: Training in processing or certifications opens premium markets. Fair Policies: Shift from price controls to income support and market diversification to balance consumer needs with farmer livelihoods. The Way Forward Low consumer prices shouldn’t come at farmers’ expense. Bridging market gaps and reforming biased policies can slash waste, boost incomes, and ensure resilient food systems. The impact—thriving farmers, stronger economies, and sustainable agriculture—is worth fighting for. Join the Conversation What’s working in your region to improve market access or fix policy imbalances? Share your ideas below—let’s build a fairer future for agriculture.

From Farmers to Agri-Entrepreneurs: Paving the Way for Viksit Bharat India’s agriculture-driven economy relies on millions of hardworking farmers, yet challenges like low income, outdated infrastructure, and limited market access persist. It’s time for a transformational shift to empower farmers as Agri-Entrepreneurs with modern tools, financial support, and global trade opportunities. In Lok Sabha, I proposed a five-pillar approach to strengthen the agricultural value chain: The Five Pillars of Agricultural Transformation ✅ Production – Boosting farm productivity through AI-driven techniques and high-yield crops. ✅ Storage – Expanding cold storage & warehouses to reduce ₹1.5 lakh crore in annual post-harvest losses. ✅ Transportation – Strengthening rural roads, logistics & rail networks for efficient farm-to-market supply. ✅ Retail – Promoting direct market linkages, digital platforms & fair pricing policies for better farmer income. ✅ Consumer – Ensuring affordable, high-quality agricultural products reach every household seamlessly. Expanding Agricultural Opportunities Abroad To truly transform Indian agriculture, we must expand beyond borders. I urged the government to facilitate land allocation for Indian farmers abroad, opening up cultivation and export markets. ✅ Global market access will make our farmers competitive worldwide. ✅ Partnering with agriculture-friendly nations will create investment & job opportunities. ✅ Strengthening export-oriented policies will boost India’s global agricultural influence. Key Priorities for Farmers' Prosperity 🚜 Increase Farmers' Income – Their daily earnings must improve for economic stability. 🌿 Promote Sustainable Farming – Encouraging natural farming & AI-based monitoring. 🌍 Strengthen Agri-Trade – Implementing barrier-free export policies to drive growth. The Road to Viksit Bharat through Agriculture Our farmers are innovators & entrepreneurs who power the nation. Equipping them with modern technology, infrastructure & policies will ensure their success. "A prosperous farmer means a prosperous nation. We must redefine them as Agri-Entrepreneurs with fair market access & technology." As a son of a farmer, I understand these challenges. I remain committed to working with policymakers, industry leaders & farmers to build a globally competitive, sustainable agricultural sector. A strong Viksit Bharat begins with empowered farmers. Let’s build the future together! 🌾🚜

"Drawing on our analysis of eight case studies prepared by independent academic and industry experts, this white paper proposes next steps to address AI evaluation and testing challenges and opportunities by: ・Synthesizing insights from the eight case studies, also published separately, and extracting lessons relevant to AI (Part 1); ・Surveying key multistakeholder initiatives that are driving AI evaluation science and practice forward (Part 2); and ・Presenting recommendations for policymakers aiming to advance the AI evaluation and testing ecosystem and strengthen AI governance (Part 3). ... While approaches to risk evaluation and testing vary significantly across the case studies, there was one consistent, top-level takeaway: evaluation frameworks always reflect trade-offs among different policy objectives, such as safety, efficiency, and innovation.   Experts across all eight fields noted that policymakers have had to weigh trade-offs in designing evaluation frameworks. These frameworks must account for both the limits of current science and the need for agility in the face of uncertainty. They likewise agreed that early design choices, often reflecting the “DNA” of the historical moment in which they’re made, as cybersecurity expert Stewart Baker described it, are important as they are difficult to scale down or undo later.  Strict, pre-deployment testing regimes—such as those used in civil aviation, medical devices, nuclear energy, and pharmaceuticals—offer strong safety assurances but can be resource-intensive and slow to adapt. These regimes often emerged in response to well-documented failures and are backed by decades of regulatory infrastructure and detailed technical standards.   In contrast, fields marked by dynamic and complex interdependencies between the tested system and its external environment—such as cybersecurity and bank stress testing—rely on more adaptive governance frameworks, where testing may be used to generate actionable insights about risk rather than primarily serve as a trigger for regulatory enforcement.   Moreover, in pharmaceuticals, where interdependencies are at play and there is emphasis on pre-deployment testing, experts highlighted a potential trade-off with post-market monitoring of downstream risks and efficacy evaluation.  These variations in approaches across domains—stemming from differences in risk profiles, types of technologies, maturity of the evaluation science, placement of expertise in the assessor ecosystem, and context in which technologies are deployed, among other factors—also inform takeaways for AI."

Is it a waste of time and money to measure biodiversity outcomes — instead of just acting on pressures? It’s a real debate. And the short answer is: ✅ Act on pressures, absolutely. ❌ But we still need to track biodiversity outcomes. Here’s why ⬇️     ✅ Yes, pressures are a very relevant level to focus on to: - set targets (as the Science Based Targets Network (SBTN) does) - track progress for public policies (the Biodiversity Plan - or Global Biodiversity Framework – has several targets focusing on pressures), corporate & financial institution commitments - identify concrete actions   - attribute responsibility     ❌ It's not enough Monitoring biodiversity state is necessary because:   1️⃣ Not all pressures are equal. Land-use change is often far more damaging than, say, noise pollution.   2️⃣ A vision centered on pressures is piecemeal. We may miss unmonitored or emerging drivers.   3️⃣ Knowledge gaps distort action. We might focus on the wrong levers.   4️⃣ Nature may react in unpredictable ways.     For all these reasons, while measuring pressures is relevant and necessary, it is insufficient to demonstrate nature positive outcomes. The consensus is clear (cf. Align discussion paper): there is a hierarchy of data to demonstrate such outcomes and measuring biodiversity state should be preferred.     🔺 The SPR pyramid   At The Biodiversity Footprint Intelligence Company (BioInt), we built the pyramid chart attached through discussions with others. It clarifies when to use state (S), pressure (P) or response (R) indicators:   🟢 State indicators - measure changes in the State of Nature or SoN (biodiversity): how much is remaining and how much has been lost - harder to attribute responsibility and to understand how to act - required to demonstrate nature positive outcomes (and ensure actions actually improve biodiversity) ➡️ at the top of the pyramid 🔎 Only direct measurement can measure changes in the SoN (see slide 2).   🟠 Pressure indicators - measure contribution to drivers of biodiversity loss (land use, pollution, etc.) - relevant to track levers on which businesses can act upon and which are within their control 🔎 Pressures can be used to model potential changes in the SoN (see slide 3).   🟣 Response indicators - measure the actions / practices (e.g. % of packaging from certified wood, etc.) - especially useful to understand and track the actions required Response & pressure indicators can be used at the level of individual sites / assets. State & pressure indicators can be aggregated at the company level. They serve different purposes.     So yes we should prioritize action on pressures. But we cannot run blind. We need biodiversity outcome measurement to ensure resources are well spent and achieve the desired results. We need response, pressure and state of nature indicators. 💬 This is a hot debate, which I have been having with many people since 2018. Genuinely interested by your take on it!

The recent inadvertent exposure of classified U.S. military plans by top defense and intelligence leaders serves as a stark reminder that even the most capable cybersecurity tools and well-defined policies can be rendered meaningless if ignored or misused. In this case, senior leaders relied on the Signal messaging app to communicate sensitive data but unintentionally exposed critical information to unauthorized parties. The leaked details—time-sensitive plans for a military operation—could have not only placed personnel in greater danger but also undermined the mission by alerting adversaries to an imminent attack. While #Signal is a widely respected, consumer-grade, end-to-end encrypted communication tool, it does not provide the same level of security as classified government systems. National security organizations typically utilize Sensitive Compartmented Information Facilities (SCIFs) to safeguard classified data from leaks and eavesdropping. However, SCIFs and other highly-secure methods are not as convenient as less secure alternatives—such as personal smartphones. In this instance, Signal's encryption was not the issue; rather, the exposure occurred when an unauthorized individual was mistakenly added to the chat. This human error resulted in sensitive information being disclosed to a reporter. Lessons Learned: This incident highlights critical cybersecurity challenges that extend beyond the military and apply to organizations everywhere: 1.     Human behavior can undermine even the most robust security technologies. 2.     Convenience often conflicts with secure communication practices. 3.     Untrained personnel—or those who disregard security protocols—pose a persistent risk. 4.     Even with clear policies and secure tools, some individuals will attempt to bypass compliance. 5.     When senior leaders ignore security policies, they set a dangerous precedent for the entire organization. Best Practices for Organizations: To mitigate these risks, organizations should adopt the following best practices: 1.     Educate leaders on security risks, policies, and consequences, empowering them to lead by example. 2.     Ensure policies align with the organization’s evolving risk tolerance. 3.     Reduce compliance friction by making secure behaviors as convenient as possible. 4.     Recognize that even the strongest tools can be compromised by user mistakes. 5.     Anticipate that adversaries will exploit behavioral, process, and technical vulnerabilities—never underestimate their persistence to exploit an opportunity. #Cybersecurity is only as strong as the people who enforce and follow it. Ignoring best practices or prioritizing convenience over security will inevitably lead to information exposures. Organizations must instill a culture of cybersecurity vigilance, starting at the top, to ensure sensitive information remains protected. #Datasecurity #SCIF #infosec

To create good policy you need responsible foresight, enabling ethical, sustainble, accountable future design. AI now can massively enable human-centered responsible foresight, in helping address uncertainty, assess risks, and set policies for creating better futures. María Pérez Ortiz's new paper "From Prediction to Foresight: The Role of AI in Designing Responsible Futures" describes responsible foresight in policy and the role of computational foresight tools. Notable approaches to using AI in responsible foresight include: 🤝 Participatory Futures for Inclusive Planning. Engaging diverse stakeholders in foresight practices democratizes the future-planning process. AI tools streamline public participation by analyzing preferences, simulating collective decisions, and creating urban plans that reflect community values, fostering equity and resilience. 🧠 Superforecasting for Precision and Insight. Superforecasting uses disciplined reasoning and probabilistic thinking to predict uncertain events. AI-powered assistants improve human forecasting accuracy by 23%, aggregating data and refining predictions through collective intelligence and advanced analytical models. 🌐 World Simulation for Systemic Insights. Advanced modeling frameworks simulate interconnected global systems, enabling policymakers to test "what-if" scenarios. AI accelerates these simulations, providing precise forecasts and dynamic platforms to visualize the long-term consequences of policy decisions across economic, social, and environmental domains. ⚙️ Simulation Intelligence for Decision Optimization. By integrating AI with high-fidelity simulations, simulation intelligence explores complex systems to uncover optimal strategies. This tool assists in crafting effective policies for urban planning, sustainable agriculture, and climate resilience, offering actionable pathways for addressing systemic challenges. 📜 AI-Assisted Narrative Techniques. Large language models contribute to speculative futures by generating detailed "value scenarios" that integrate ethical, technological, and societal considerations. These AI-driven narratives enable policymakers to visualize desirable outcomes and evaluate potential trade-offs. 🔗 Hybrid Intelligence for Enhanced Foresight. Combining human creativity with AI’s computational strengths creates a robust foresight framework. Intuitive interfaces, explainable AI, and participatory design ensure that tools remain transparent and aligned with ethical considerations, empowering policymakers to navigate complex challenges collaboratively. ♻️ Iterative Foresight with Feedback Loops. Continuous monitoring and real-time adaptation enhance foresight processes. AI’s ability to process evolving data and generate actionable insights ensures policies remain responsive, flexible, and aligned with long-term objectives. The power of AI in assisting foresight is just beginning to come to fruition.

This new UN Trade and Development (UNCTAD) report explores how Technology Foresight and Technology Assessment support evidence-based policymaking for sustainable development. It highlights their complementary roles in shaping immediate decisions and long-term strategies, fostering inclusive dialogue and forward-looking innovation policies across sectors such as energy and infrastructure.