AI Role In Workplace Safety

Smart helmets incorporating AI technology represent a significant advancement in safety, connectivity, and overall functionality. What do you think about this one? These helmets leverage artificial intelligence to enhance various aspects of the user experience. 1. Head Protection: Impact Detection: AI can be employed to detect and analyze the severity of impacts, providing real-time information about potential head injuries. Emergency Response: Smart helmets can automatically send distress signals or call for help in the event of a significant impact. 2. Augmented Reality Displays: Helmet-Mounted Displays: AR technology integrated into helmets can offer real-time information, such as navigation, speed, and relevant data, directly in the user's line of sight. Enhanced Situational Awareness: AI algorithms can analyze the surroundings and provide augmented information, like highlighting potential hazards. 3. Communication and Connectivity: Hands-Free Communication: AI-driven voice recognition enables hands-free communication, allowing users to make calls, send messages, or access information without removing the helmet. Intercom Systems: Smart helmets can facilitate communication between riders, improving group coordination during activities like motorcycling or cycling. 4. Gesture Recognition: Intuitive Controls: AI-powered gesture recognition enables users to control the features of the helmet through simple hand gestures, promoting a seamless and intuitive user experience. 5. Biometric Monitoring: Vital Signs Monitoring: Integrated biometric sensors can monitor vital signs such as heart rate and temperature, providing insights into the user's health and well-being. Fatigue Detection: AI algorithms can analyze data to detect signs of fatigue, alerting the user to take breaks when needed. 6. Navigation Assistance: Turn-by-Turn Navigation: Helmets equipped with AI can provide turn-by-turn navigation guidance, enhancing safety and convenience during travel. Route Optimization: AI algorithms can suggest optimal routes based on real-time traffic conditions. 7. Adaptive Lighting: Dynamic LED Lighting: Helmets with AI-controlled LED lighting can adapt to ambient conditions, improving visibility and safety, especially in low-light environments. 8. Automatic Tinting: Adaptive Visors: AI can control visors that automatically adjust tint based on changing light conditions, offering optimal visibility to the user. 9. Collaboration with IoT Devices: Integration with IoT: Smart helmets can seamlessly connect with other IoT devices, such as smartphones, smartwatches, or vehicle systems, creating an interconnected ecosystem. 10. Security Features: Facial Recognition: AI-driven facial recognition can enhance security by allowing authorized users access to specific features or functionalities. #innovation #helmet #ai via @ justhelmet

All valuable work will increasingly be done by Human-AI hybrids. An insightful research paper identifies both challenges and good practices from multiple case studies to propose an overall framework. The authors propose that generating effective human-AI hybrids is divided into two phases: Construction - in which Technical implementers design the architecture of the hybrid - and Execution - where Organizational implementers facilitate how participants engage and interact. They suggest 3 primary success factors: 🔧 Interface and Technical Design focuses on making AI systems accessible and reliable through code-free interfaces. The technical architecture should allow rapid testing of different approaches while being supported by effective data curation strategies. 🧠 Human Capability Development prepares people to work effectively with AI systems through training, in critical assessment and prompting techniques. Employees must understand AI's capabilities and limitations, and develop skills to integrate AI into existing workflows. 🤝 The Collaboration Framework structures successful human-AI interaction through aligned mental models and clear role definitions. It emphasizes improving underperforming areas rather than disrupting successful processes, while ensuring both human and AI agents contribute their unique strengths to achieve optimal outcomes.

What's the role of health and safety leadership when it comes integrating AI into high risk enterprise? I've been reflecting on this after a couple of days exploring the topic with a group of senior HSE leaders in Brissie. The conversation about AI exploration in most workplaces often centres on how corporate innovation and ICT teams drive AI adoption. But what is the proactive, enabling role of H&S leadership? A common perception positions us, safety leaders, as a handbrake for innovation; opting to focus on legislative compliance and the negative side of the risk equation. While compliance is foundational, our primary intent, perhaps? - should be to enable progress, responsibly. I believe H&S leaders can, and should, be the primary enablers of responsible AI integration. Our role is evolving whether we like it or not and we should be evolving ourselves; to actively co-create a safer, more efficient future through responsible AI adoption. To guide this, I propose the following principles for H&S leadership engagement with AI: ✨Proactive Collaboration & Co-Creation: We must be at the table from Day 1 with Innovation, ICT, Operations and our workforce; to co develop AI strategies and guardrails. Our expertise in risk management is best placed to shape AI use cases that delive genuine value which leads me to... ✨Value-Driven, Risk-Informed Innovation: AI adoption should be driven by clear H&S value propositions – improving risk identification, control effectiveness & worker wellbeing. This requires a sophisticated understanding of both AI capabilities & operational risks, moving beyond a purely compliance-focused mindset. ✨Human-Centric & Ethical by Design: AI systems must augment, not replace or alienate (can't think of a better word) our workforce. We must assess & mitigate the unintended consequences, especially psychosocial impacts, ensuring AI supports human decision-making, maintains worker agency & promotes trust. Ethical considerations, transparency & explainability are paramount. ✨Robust Data Governance & Security: We must champion AI solutions built on strong foundations of data privacy, cybersecurity & ethical data handling. This means aligning with & often helping to inform, enterprise standards, respecting individual rights & ensuring transparency in how H&S-related data is used by AI. ✨Evolving H&S Expertise & Mindset: We must commit to fostering new competencies. Understanding AI fundamentals, data literacy & the ethics of algorithmic (augmented) decision-making – to effectively guide, govern & champion AI in safety. This is about reimagining our role to meet future challenges AND opportunities; it's about a fundamental shift in HOW we approach safety. I'm keen to hear your perspectives: - How can H&S leaders best champion responsible AI innovation in high-risk environments? - What guardrails are essential from an H&S perspective? - What challenges or opportunities am I missing? #safetytech #safetyinnovation

This Smartwatch Doesn’t Just Track Your Health. It Can Intervene to Protect It>> ⌚️ZEM Life is developing a smartwatch that can detect signs of a fentanyl overdose and automatically inject Narcan if the user doesn’t respond to an alert ⌚️The device monitors key signals like oxygen saturation, pulse rate, and movement to predict overdose risk using existing wearable sensor technology ⌚️If a potential overdose is detected, the watch triggers an alarm. If not manually canceled, it shares GPS location with emergency services and delivers the medication ⌚️ZEM Life plans to expand beyond overdoses, with cartridges for conditions like asthma, seizures, heart attacks, and septic shock. ⌚️The FDA approval process is being tackled in stages: first the device, then the medication cartridges, which use already-approved drugs. ⌚️Other wearables, like Ayuda’s armband, can detect overdose and call for help, but ZEM’s watch is the first aiming to deliver treatment too 💬 This is another example of wearables evolving from passive data trackers to actually intervening and providing treatment #digitalhealth #wearables

Industrial Cyber Security—Layer by Layer OT environments can't rely on repackaged IT security checklists. Frameworks like IEC 62443 and NIST SP 800-82 demand a defence-in-depth strategy tailored to physical processes, real-time constraints, and integrated safety systems. This layered defence model visualizes the approach, moving from the physical perimeter to the core data: ✏️ Perimeter Security: Starts with physical controls like site fencing and progresses to network gateways that enforce one-way data flow. ✏️ Network Security: Involves segmenting the network (per the Purdue model), using industrial firewalls, and securing all remote access points. ✏️ Endpoint Security: Focuses on locking down devices with application whitelisting, ensuring secure boot processes, and using anomaly detection to spot unusual behavior. ✏️ Application Security: Secures the software layer through code-signing for logic downloads and hardening engineering workstations. ✏️ Data Security: Protects information itself with encrypted backups, PKI certificates for authenticity, and integrity monitoring. This entire strategy rests on two pillars: 1. Prevention: Proactive measures like architecture reviews, role-based access control (RBAC), and disciplined patch management. 2. Monitoring & Response: OT-aware security operations, practiced incident response playbooks, and the ability to perform forensics on industrial controllers. Why it matters: The data is clear. Over 80% of recent OT incidents exploited weak segmentation or unmanaged assets. Conversely, plants with layered controls have cut their mean-time-to-detect threats by 60% (Dragos 2024). Which of these security rings do you see most neglected in real-world plants? #OTSecurity #IEC62443 #NIST80082 #DefenseInDepth #IndustrialCyber #CriticalInfrastructure #CyberResilience

No one becomes a health & safety leader to be a glorified administrator. And yet, after 15+ years working with thousands of organisations, I’ve seen how easily H&S leaders get stuck in reactive mode: buried in spreadsheets, juggling disconnected tools, and fighting manual processes. At their core, safety leaders are passionate about people. They want to drive meaningful change, reduce risk, and build stronger, safer workplaces. So why is it still so hard to break out of the compliance trap? The truth is: the potential for real impact only emerges when we move beyond the silo and bring the whole organisation on the journey. Sounds simple - but very few manage to make it happen. Here’s what I’ve learned about turning that vision into reality: 🔹 Engage the frontline. If it’s not easy for workers to participate, they won’t. Map existing processes, remove friction, and make safety a seamless part of their day. Without frontline engagement, nothing else sticks. 🔹 Empower managers and team leaders. Decentralise tasks that don't need to sit with the H&S team. Equip leaders with tools to build safety into their daily routines. When ownership shifts from you to them, safety becomes everyone's business. 🔹 Eliminate point solutions. Spreadsheets and isolated apps might feel convenient, but they limit insight. Unify your EHS data under one platform. The magic happens when you cross-reference information and uncover the bigger picture. 🔹 Turn your EHS system into a collaboration hub. This isn’t just about logging data. Your system should drive accountability, connect stakeholders, and become a flywheel for continuous improvement. 🔹 Leverage AI - but only when the foundation is strong. With clean, connected data and real user engagement, AI can elevate everything: from user experience to insights to decision-making. It won’t fix a broken system, but it can supercharge a good one. Health & safety leaders want to make a difference. But it’s hard to let go of the way things have always been done. Dusty policy folders and audit checklists aren’t enough anymore. We’re not in the '90s. It's time our EHS systems caught up. Let’s embrace technology - not for its own sake but to build safer, stronger, more connected organisations. #SafetyLeadership #EHS #SafetyTech #DigitalTransformation #AIinSafety #WorkplaceSafety #HSE #FutureOfWork 👇 Swipe through the carousel to explore the key shifts.

So Anthropic just dropped Claude 4, and for once we get to see the receipts on how a "frontier lab" is managing AI security and AI safety risk with suitable detail. As someone who spends way too much time explaining why AI security isn't the same as AI safety, this is actually quite useful. Quick and shallow recap for those playing along at home: 🛡️ AI Safety = Stopping the model from saying and doing dodgy things 🔒 AI Security = Stopping dodgy people from doing things to the system Claude Opus 4 is apparently the world's best coding model (72.5% on SWE-bench), but more importantly for my lot, they've implemented ASL-3 protections and actually told us what that means: ☢️ 405 people spent 3,000+ hours trying to break it ☢️95% of jailbreak attempts failed (which is pretty solid) ☢️Only 0.38% extra false positives in production (translation: barely noticed by real users) ☢️23.7% computational overhead for safety measures (not cheap, but not ridiculous) Security bits that caught my eye: ☢️They monitor data flows to spot if someone's trying to steal model weights ☢️Need two people to authorise access to the actual model files ☢️Real-time input/output monitoring with "Constitutional Classifiers" (fancy name, decent idea) ☢️5.2% of their workforce does security (decent ratio, most orgs could learn here) Most AI companies just say "trust us, we're being responsible." Anthropic's actually published their homework. These metrics give the rest of us something concrete to benchmark against instead of making it up as we go. Plus, no one found a universal jailbreak despite $15K bounties. That's not luck—that's what proper engineering looks like. What metrics are you using for assessing your org's AI security and AI safety risk? Because "we think it's probably fine" isn't a risk framework. #AISafety #AISecurity #ResponsibleAI #Claude4 #BenchmarksActuallyMatter https://lnkd.in/djBwwy78

AI holds incredible promise and potential, but something I find especially inspiring is how it can help us solve some of the biggest environmental challenges we face today, such as the global wildfires.    In Canada, Microsoft is working with the Government of Alberta and AltaML on a new AI tool which leverages machine learning to predict the risk of new wildfires by region and even by hour. Capable of analyzing tens of thousands of data points, it provides insights that help firefighting agencies plan ahead, allocate resources efficiently, and prevent fires from spreading out of control – and it’s showing great promise for other wildfire-prone regions around the world.     Learn more here: https://aka.ms/AAmlsim 

The Green Hell 🌲 Racing driver Jackie Stewart coined this nickname for the the Nurburgring Nordschleife racetrack following his victory in the 1968 German Grand Prix, amid a driving rainstorm and thick fog 🌫 Considered by many to be the world's most legendary, demanding and deadly permanent racetrack, the Nordschleife ("North Loop") is 20.8 km long, has significant elevation changes, is known for its changeable weather, and boasts 154 corners 🏎 Accidents are frequent; due to high speeds and limited run-off areas, they can often be fatal (~70 in racing or private testing conditions, and with an additional 100 or so in the popular open tourist sessions) 💥 A particular aim is to prevent secondary accidents: a car travelling at full-speed hitting an immobilised vehicle ⛑ Earlier this month there was a quantum-leap in track safety, with a new milestone being reached thanks to innovation and digitalisation 👩💻 A comprehensive track monitoring system and new digital early warning system became active, pairing 100 cameras and 46 large LED panels - supported by Artificial Intelligence (AI) 🤖 One of the most significant construction and infrastructure projects in the almost 100-year history of the Nurburgring, the upgrades are expected to reduce the number of accidents 📉 Keeping an eye on such a unique and complex circuit is challenging, so over $12 million has been invested in smart technology with Fujitsu, with full operation due to complete in 2025 💰 The bespoke solution involves reinforcing the track with cameras, connecting them to fibre-optic cables, and utilising AI algorithms to analyse footage and identify potential safety hazards 📹 The system uses AI to process live camera feeds, detect obstacles, and provide real-time alerts to prevent further accidents 💡 Real-time detection and analysis of incidents enables Race Control personnel to make critical decisions quickly, preventing accidents and ensuring the safety of those on and off track 🏍 The use of machine learning algorithms can help identify objects in the race track, such as crash barriers and other hazards 👁 However, the software also needs to take into account anomalies and unexpected events, such as animals on the track 🦌 The consequences of misjudging a situation can be severe, and therefore, the software needs to be able to respond quickly and accurately ⏱ Once an object is detected, the next step is to analyse its behavior and classify it accordingly 🎯 For example, a slow-moving car can be classified as a hazardous object, and the software can trigger an alert for approaching traffic 🚨 The AI is in its infancy, and is expected to undergo continual tuning and refinement to hone its hazard-detection abilities over time 🔄 While motorsport is safer than ever today, it remains a dangerous pursuit ⚠ A great example of comprehensive digitalisation tangibly solving a specific problem and delivering real value: both today and for the future 🆕

AWS News - Detective can now query Security Lake! If you're not familiar Amazon Security Lake is a specialized Data Lake which is fully managed where you can centralize security data from any source. Yes, ANY source, so not only AWS data like logs or events from AWS services, but also data from your multi-cloud workloads (yes, including those in Azure, GCP and any others), SaSS or even on-premise. It normalizes everything into OCSF format (an open standard) and you can then take it from there using your favorite analytic tools to help you increase your security posture and uncover insights or opportunities for improvement in security across your whole digital solutions. Detective, on the other hand, is all about helping you conduct investigations by ingesting data from various sources to put the pieces together and help you figure out any correlations between logs and events to identify, categorize them so you can act. And now they work together! 😎 So while using Detective to work out any potential security issue or breach you can now query Security Lake to augment the evidence and have more data available to co-relate and help you determine whether something is indeed a threat or not. Right now, you can collect any raw logs from CloudTrail and VPC Flow Logs that are stored in Security Lake to help you with your investigation. There are no additional charges to query these logs from Detective though usual charges apply for underlying services usage like Athena, S3, etc. Learn more about it here: https://lnkd.in/gHEPR4Nv