Day: February 26, 2026

Meghna AggarwalSciences Po Paris | International Security, Political Risk, Trade & Investments Everyone has a plan until they get punched in the face. But what if, you could know exactly when the punch is coming, at what intensity, and where you should move to avoid the blow? That’s what threat intelligence is for organisations. Threat intelligence refers to any information organisations use to better understand their adversaries. It provides context for companies to make proactive decisions about their physical security. And as a result, teams can better forecast and respond to incidents that have the potential to disrupt operations. The pace of business is a cliché for good reason – speed matters. The ability to act quickly and decisively is now a true competitive advantage. It is no longer sufficient for security teams to play the ‘guard at the gate,’ simply waiting to stop bad actors. Instead, they must predict, prevent, and prepare for attacks before they even occur. Unlike traditional security measures that depend on static defences such as cameras and guards, threat intelligence draws on real-time information from surveillance systems, access logs, open-source intelligence (OSINT), social media, and incident reports. As a result, threat intelligence, though traditionally viewed as a security function, has become a powerful way for security to position itself as a key business differentiator within the organisation. Nowadays, physical security teams work to defend a range of assets, from worksites and employees to infrastructure and intellectual property. Moreover, as companies expand internationally, they also must stay up to date on events around the world. Yet, with a shortage of qualified analysts, few have the time to comb through vast stacks of data. Compounding this challenge is the sheer noise of social media, which can easily drown out critical signals and make it difficult to verify what is authentic. Genuine threats are often missed or deprioritised. During the flooding in Houston after Hurricane Harvey in 2017, for example, an image of a shark supposedly swimming down a submerged highway went viral, distracting the public from reliable updates about flood zones and emergency assistance. As a result, by the time an incident occurs, it is often too late to react; and that can come with a big price tag, through fines, damages, and business disruptions for a company. This is where AI makes a decisive difference. The Threat Intelligence Life Cycle, Redefined By enhancing nearly every stage of the threat intelligence lifecycle, AI is fundamentally reshaping how corporate security teams operate. The combination of AI and human expertise enables organisations to build effective human–machine partnerships that augment threat detection and sharpen decision-making. Here’s how: Figure 1: Traditional Threat Intelligence Life Cycle Planning and Collection Traditionally, analysts were required to manually select and monitor a diverse range of news outlets and open sources, a labour-intensive, time-consuming effort just to filter out the noise and identify relevant incidents. Today, AI can continuously scan trusted, pre-verified sources at scale, automatically highlighting the events that matter most. Through tools such as Application Programming Interfaces (APIs), raw data can be efficiently transformed to meet specific client requirements. For instance, data collected via Twitter’s APIs can be used to monitor key influencers and detect malicious activity by terror actors, ensuring rapid, real-time escalation of potential risks. Thus, AI enables live intelligence updates while fundamentally reshaping the analyst’s role from painstaking data collection to the far more valuable task of validating and interpreting meaningful signals. Processing and Analysis In this stage, analysts would typically trawl through multiple sources, interpret event details, place them in context, and then manually craft an operational alert or risk report. Now, AI can ingest large volumes of data and generate a preliminary alert or assessment within seconds. Threat intelligence refers to any information organisations use to better understand their adversaries. It provides context for companies to make proactive decisions about their physical security. And as a result, teams can better forecast and respond to incidents that have the potential to disrupt operations Here, AI also plays a critical role in countering disinformation and misinformation. Advanced, AI-driven systems can analyse patterns, language, and contextual cues to support content moderation, fact-checking, and the identification of false narratives, achieving accuracy rates of up to 97% when classifying news articles as genuine or misleading. Once again, the analyst’s role shifts away from heavy processing towards focused verification, ensuring outputs are free from hallucinations, biases, or gaps in relevance. Crucially, AI systems also learn continuously from this human feedback, improving their precision over time and further reducing turnaround times. This marks a shift from manual security work to intelligence-augmented decision-making, a new standard for speed, scale, and sophistication. Production and Dissemination Traditionally, customising deliverables was both costly and time-consuming. Designing them required significant manual effort and dedicated resources, and even then, distribution was often limited due to fixed algorithms. With AI, however, the entire process is redefined. Deliverables can now be tailored effortlessly using client-specific data, such as their role, industry, or company, while design becomes quick, scalable, and low-effort. Intelligent, AI-driven distribution can also adapt to individual client preferences, ensuring the right content reaches the right people at the right time. Additionally, AI can support multilingual threat reporting, translating intelligence into multiple languages for a truly global audience. Evaluation Prediction Perhaps most importantly, AI shifts the threat intelligence cycle from simply evaluating what’s happening now to actually predicting what might happen next. Rather than exclusively alerting an organisation to a current threat, AI anticipates how that threat may evolve, giving teams far greater foresight. In the past, meaningful threat prediction was limited and highly selective. It required expensive models and vast computing resources, something only wealthy nations or military organisations could realistically afford. However, GenAI changes that equation. By working across massive datasets at scale and leveraging trend analysis, it makes advanced prediction widely accessible, delivering outputs that are specific, detailed, and focused. Wondering whether the protests in Nepal might turn violent? By analysing historical data and patterns, AI can estimate the…

Atul LuthraCo-Founder & Principal Consultant5Tattva and CEO of Zeroday Ops The modern internet can be described as ‘a consensual hallucination experienced daily by billions.’ William Gibson called it cyberspace in Neuromancer (1984), imagining a shared, invisible space where human consciousness, identity, and power intermingle without physical boundaries. Today, that vision is reality. The internet is deeply woven into identity, economy, governance, and daily life – and with that integration comes constant exposure to risk. What once felt like an exciting digital frontier is now a shared space where data, trust, and personal identity are continuously targeted. Early internet users operated in an environment of implicit trust, often unaware of privacy, Personally Identifiable Information (PII), or digital threats. Today, that lack of awareness is no longer an option. The scale, speed, and sophistication of cyber risks have expanded so rapidly that online safety has become a daily responsibility for every user, regardless of age or profession. The modern internet can be described as ‘a consensual hallucination experienced daily by billions.’ William Gibson called it cyberspace in Neuromancer (1984), imagining a shared, invisible space where human consciousness, identity, and power intermingle without physical boundaries. Today, that vision is reality. The internet is deeply woven into identity, economy, governance, and daily life – and with that integration comes constant exposure to risk The modern threat landscape is far more complex than traditional ideas of ‘hackers’ and ‘viruses.’ It spans identity and authentication systems, cloud misconfigurations, supply chain dependencies, APIs and machine-to-machine communication, and data storage exposure. Data itself has become more valuable than many physical assets, making individuals, businesses, and institutions constant targets. Adults are routinely affected through financial fraud, identity theft, phishing attacks, and reputation damage, while misinformation and manipulation campaigns exploit the same digital platforms people rely on for news and communication. The gap between how quickly threats evolve and how slowly user awareness adapts remains one of the biggest security challenges. A significant shift in recent years is the rise of AI-driven tools and conversational platforms. Large language models and generative AI systems are powerful, but they also introduce new forms of information exposure. Users often treat AI chats as private, informal spaces, sharing details they would never post publicly. However, these platforms should be approached with the same caution as any online environment. Even well-intended conversations can lead to unintended disclosure of sensitive or personal information. This marks a new chapter in digital risk, where human behavior, not just technical vulnerability, becomes a primary security factor. While Safer Internet Day serves as an important annual reminder, digital safety cannot be limited to a single day. A few practical habits, practiced consistently, can drastically lower digital risk: Apart from these key steps, following simple daily digital safety habits can further lower your risk. Update passwords for sensitive accounts periodically, especially after a suspected breach. Install trusted antivirus or anti-malware software, and keep screen locks and device encryption enabled to protect data if a device is lost or stolen. Back up important files regularly to a secure cloud service or encrypted external drive. Review privacy settings on social platforms and limit publicly visible information. Share only the minimum necessary details when filling out online forms. Encourage family and friends to adopt safe digital practices to build a more secure online environment. Be cautious of SMS or WhatsApp messages claiming to be from your bank – avoid clicking links, verify requests directly with your bank, and never install apps from such messages. Enable transaction alerts, monitor sender IDs, and report suspicious activity immediately. Protect your SIM card, and if you suddenly lose network signal, act quickly. Block your card and inform your bank at once if you suspect fraud. The internet today functions like a shared public street. Awareness, caution, and responsible behavior are no longer optional – they are essential to protecting digital identity in an increasingly connected world. Read More

As we enter 2026, the convergence of artificial intelligence (AI) and IoT infrastructure is reshaping industries, unlocking unprecedented opportunities to optimize operations, enhance security, and improve sustainability. Yet with great technological power comes great responsibility, and the AIoT industry is increasingly focused on ensuring AI develops in ways that are safe, ethical, and beneficial to all. Here are the five key trends shaping the AIoT landscape in 2026. Scenario-based AIoT solutions are rapidly unlocking new business value Thanks to AIoT, we are witnessing a profound digital shift moving beyond basic IT informatization to deep integration with Operational Technology (OT). In this transition, business value is no longer created by fragmented data collection, but increasingly by harvesting insights naturally and continuously from daily operations. By embedding perception capabilities into specific real-world scenarios, AIoT is enabling organizations to move from manual management to much more agile, automated control. This is creating operational capabilities that were once impossible, enabling real-time decision-making which can rapidly deliver new business value. In the field of industrial safety, for example, we see workshops shifting from reactive response to proactive prevention. Hazardous manual inspections are being replaced by advanced spectral technologies such as TDLAS, which remotely detect natural gas leaks in seconds. The result is a dramatic reduction in response times to emergency situations. It’s a similar story with quality control. Food manufacturers, for example, are now leveraging AI-driven X-ray systems to instantly identify foreign objects like stones, glass, and bone that were once invisible. Or consider inventory management, where mining and feed plants are now utilizing 3D millimeter-wave radar to automatically scan silos. This is yet another application of AIoT that, in this case, is creating a new level of precision in volumetric data, eliminating human error, and enabling fully automated, real-time control. Large-scale AI models are evolving into new capabilities for ‘AI+’ Large-scale AI models are empowering the core analysis and processing flow through ‘AI+’ integration. While large language models have revolutionized human-digital interaction, industry-specific models are now reshaping how IoT data interacts with the physical world. We can already see that by embedding AI into data analysis and signal processing, these models significantly enhance precision and efficiency. For example, traffic and perimeter security models, trained on massive datasets, are pushing the limits of perception. By processing complex data, they minimize false alarm rates for incidents and intrusions. Meanwhile, in audio sensing, ‘AI+ signal processing’ is redefining audio capture by filtering background static and isolating human voices in noisy environments. This technology improves the signal-to-noise ratio, ensuring clear sound pickup even in challenging conditions. Deeply anchored in this multi-modal understanding, AI Agents are now bridging the gap between perception and human intent. Powered by large language models, these agents enable users to communicate naturally using everyday language. Commands like “Find the person wearing purple clothes who parked a blue SUV this morning” are processed by intelligent security systems to automatically retrieve relevant video segments. Such capabilities are transforming AIoT systems from specialized tools that require professional training into intelligent assistants that are accessible to everyone. Edge AI is transforming devices from data collectors to intelligent analyzers Another shift we are seeing is towards edge computing. Increasingly, the ‘Cloud + AI’ model is no longer the only option for enterprise digitalization. By moving AI functions from the cloud to the edge, organizations can achieve millisecond-level response times, operate seamlessly offline, and maintain on-premises privacy. It’s an architectural shift that eliminates bandwidth dependency and significantly reduces infrastructure overhead. Because devices process raw data directly, this localized architecture extends its value by greatly optimizing storage efficiency. This is particularly significant for complex video analysis, powered by visual AI models. Here, edge devices can now precisely identify key targets such as people or vehicles at the source. Based on this accurate segmentation, the system applies differentiated encoding – preserving critical foreground details, while compressing background areas that contribute little investigative value. This AI-driven approach drastically reduces storage requirements without sacrificing visual clarity. For organizations deploying thousands of cameras across multiple sites, this naturally translates into substantial savings on storage infrastructure, lower ongoing costs, and simplified data management, making large-scale AIoT deployments economically viable. Responsible AI is embedding ethics into every stage of innovation AI is transforming our lives, work, and business at an unprecedented pace. Yet, this revolution brings a critical responsibility – to ensure innovation unfolds safely, ethically, transparently, and beneficially for all. Responsible AI is no longer optional – it is both a moral imperative and a strategic necessity that builds trust, mitigates risk, and drives long-term innovation. As public awareness and regulatory oversight intensify globally, from Europe’s regulatory pioneering to regional initiatives worldwide, international collaboration becomes essential to harnessing AI’s potential while, at the same time, promoting security, prosperity, and human well-being. Responsible AI practices, then, must permeate the entire AI lifecycle – from research and development to deployment and real-world application. This includes establishing guiding principles and governance frameworks, adopting responsible approaches throughout development, and ensuring safety, accountability, and transparency in products and solutions. It is a systematic endeavor requiring industry-wide coordination and collective action across sectors and borders, involving policymakers, industry partners, researchers, and other stakeholders. Only through sustained commitment and open collaboration can we shape an AI future that truly serves humanity. AIoT is expanding technology’s role from business to society and environment Another key trend that we are seeing is the rapid expansion of application areas for AIoT. In addition to the traditional business solutions, AIoT is now being widely adopted for broader social and environmental applications, demonstrating how intelligent systems can serve humanity and nature. In ecological protection, for example, specialized AIoT devices are revolutionizing conservation efforts, from wildlife monitoring to vegetation health tracking. Indeed, crop growth monitoring systems that leverage AIoT technologies for large-scale, real-time analysis of crop health are becoming increasingly widespread in agriculture. This capability addresses the inefficiencies of manual inspections, enabling precise management and optimizing yields through digitization. AIoT is also being used to improve public safety….

Sumiit KatyalFounder & Managing Directorwww.focusVu.ai For decades, CCTV systems have been deployed as the backbone of security and surveillance across cities, transport systems, factories, campuses, and critical infrastructure. Yet, despite massive investments, most CCTV deployments continue to operate in a passive mode – recording footage that is largely reviewed after an incident has already occurred. The limitation is not technology alone; it is human dependency. Watching multiple camera feeds at a command-and-control center is a monotonous, fatiguing task. After prolonged hours of monitoring screens, even trained officials experience reduced attention spans, visual fatigue, and in extreme cases, hallucination-like effects caused by continuous concentration. Expecting humans to detect every anomaly, threat, or safety violation in real time is neither realistic nor scalable. This is where AI-powered video analytics fundamentally changes the paradigm, transforming CCTV systems from passive observers into active, intelligent systems capable of delivering pre-alerts to a predefined operational hierarchy. From Watching to Acting: CCTV in Active Mode AI video analytics enables CCTV cameras to see, understand, and alert, instead of merely recording. Once integrated, AI algorithms continuously analyze live video feeds and generate real-time alerts for defined events, anomalies, or violations. Rather than security teams staring at screens, the system proactively notifies ground-level supervisors, shift in-charges, safety officers, control room managers, and senior leadership (where required). This event-driven monitoring dramatically improves response time, accountability, and operational efficiency. Critical Security & Safety Use Cases Fire & smoke detection – The most critical pre-alert Fire and smoke detection through AI-based video analytics is among the most essential applications across industries. Human negligence or delayed response can result in massive financial losses, injuries, and even fatalities. AI can detect smoke patterns before visible flames, early-stage fire indicators, abnormal heat signatures (where integrated with thermal feeds). These alerts enable preventive action, not damage control. PPE & safety compliance monitoring In factories, plants, construction sites, and mining operations, AI-driven CCTV can automatically detect: This significantly reduces workplace accidents and ensures continuous safety compliance without manual supervision. Access control & unauthorized entry AI analytics enhances perimeter and access security by detecting unauthorized entries, intrusion in restricted zones, tailgating incidents, and entry during prohibited hours etc. Such alerts are delivered instantly, enabling rapid intervention. Crowd, mob & behavioral alerts In public spaces, transport hubs, and large facilities, AI can generate alerts for crowd build-up beyond thresholds, unusual movement patterns, aggressive or suspicious behavior etc. This is particularly valuable for law enforcement, metro rail networks, airports, and large campuses. Face recognition & ANPR Advanced AI systems support face recognition for watchlists and access validation, and automatic number plate recognition (ANPR) for vehicle tracking, violations, and audits. These capabilities enhance both security and investigation workflows. Beyond Security: AI Video Analytics and ROI One of the most overlooked advantages of AI-based CCTV is its direct impact on Return on Investment (ROI) – especially in industrial and enterprise environments. Operational intelligence inside factories & plants AI analytics enables detection of unattended machines, alerts for machines overheating or operating abnormally, identification of idle or resting labor during work hours, monitoring pre- and post-lunch productivity patterns, and compliance with safety gear and operational SOPs. This converts surveillance infrastructure into a management and productivity tool, not just a security expense. Unlocking value from archived video data A powerful yet underutilized capability of AI video analytics is post-event data extraction from archived footage. Organizations can mine historical video data to extract structured intelligence such as road conditions (pre and post analysis), hoardings and signboard mapping, household and business identification, streetlight inventory and status, tree counting and green cover assessment, and infrastructure condition monitoring etc. This opens up applications across urban planning, smart cities, asset management, utilities, and municipal governance – without the need for fresh surveys. AI is no longer optional Traditional CCTV systems answer one question: “What happened?” AI-powered video analytics answers: By delivering pre-alerts instead of post-mortems, AI shifts organizations from reactive to proactive operations. Conclusion AI video analytics represents a decisive evolution in security and surveillance – turning cameras into intelligent sensors that enhance safety, security, productivity, and governance. It reduces human fatigue, minimizes negligence-driven losses, improves compliance, and delivers measurable RoI across sectors. As organizations continue to invest in CCTV infrastructure, the real question is no longer whether to deploy AI, but how quickly they can convert existing systems into intelligent, action-oriented platforms. Read More

The new security reality: Convergence over silos Modern security threats operate across domains that traditional models never contemplated. Surveillance cameras function not as isolated optical instruments but as networked autonomous computing endpoints susceptible to remote takeover. Attackers can erase visibility exactly when physical intruders test perimeter weaknesses. Access control systems have evolved from mechanical deadbolts to cloud-managed software platforms vulnerable to credential manipulation that grants unauthorised entry without physical force. Smart building infrastructure, including elevators, HVAC units, lighting grids, and fire suppression systems, interconnects through IoT protocols. These systems deliver operational efficiency while simultaneously multiplying entry points for sophisticated disruption. Real-world incidents reveal the stakes clearly. Cyber operators disable CCTV feeds moments before coordinated physical breaches occur. Compromised building automation systems flood corridors with confusing signals or jam emergency exits, paralyzing organized response efforts. Private security personnel confront this hybrid chaos daily. They must interpret jammed radio signals, identify deepfake credentials presented at access points, and coordinate manual interventions amid false automation triggers. These professionals serve as the first line of defence in factories, corporate campuses, hospitals, societies and transport hubs, often without integrated tools, real-time intelligence sharing, or unified response protocols. Physical security merges irrevocably with cybersecurity and AI enabled autonomous systems. Separate teams managing CCTV networks, enterprise firewalls, and building controls create dangerous blind spots. A firewall penetration in one domain cascades into physical lockdown failures elsewhere. Security guards must now decode AI-prioritised alerts streaming from multiple sources, liaise with remote Security Operations Centres, execute precise manual overrides, and generate detailed audit documentation, all while maintaining personal safety and ensuring legal compliance in high-stakes environments. Legacy guarding approaches crumble under these pressures. Manual foot patrols provide a visible presence but deliver limited situational awareness. Reactive response postures create unacceptable delays amid overwhelming data volumes. Compliance requirements falter in regulated sectors such as airports, power plants, and healthcare facilities, where integrated audit trails prove essential. CAPSI addresses these challenges through carefully designed integrated operating models that preserve human command authority while amplifying capabilities through cross-domain intelligence fusion. India’s unmatched workforce scale transforms what could be a vulnerability into a foundation for global leadership in resilient security operations. Historical evolution: From physical deterrence to digital ecosystems The private security industry’s development in India parallels the nation’s economic liberalisation and rapid urbanisation. Emerging in the late 20th century, the sector filled critical gaps in public policing capacity as factories expanded, shopping malls proliferated, and gated residential communities multiplied across urban landscapes. Early operations emphasised straightforward deterrence through uniformed personnel stationed at factory gates, office building lobbies, and compound entrances. Demand grew steadily with infrastructure development, creating a manpower-centric model that generated substantial employment opportunities across diverse social strata while providing reliable baseline protection for private assets. Technology adoption proceeded incrementally during the initial decades. Analogue CCTV systems captured incidents primarily for post-event review and documentation. Handheld GPS devices logged patrol routes to verify coverage. Biometric readers began streamlining entry verification processes at high-traffic points. These tools enhanced specific operational tasks but remained disconnected add-ons orbiting around human operators. Security guards constituted the core operational unit, with technology serving peripheral documentation and verification functions rather than fundamentally transforming workflows. The digital transformation arrived with increasing intensity over the past decade. IP-based cameras evolved into cloud-connected analytics platforms capable of real-time object detection and behavioural pattern recognition. Visitor management kiosks amassed comprehensive biometric archives transmitted across unsecured networks. Security Operations Centres emerged as centralised nerve centres ingesting video feeds, access logs, and sensor data from sprawling client portfolios spanning multiple cities. This shift toward software-defined operations unlocked unprecedented visibility and coordination capabilities but simultaneously introduced profound cybersecurity vulnerabilities. Ransomware attacks encrypted access control databases, preventing legitimate entry during emergencies. Spoofed video feeds misled operators during active intrusions. API vulnerabilities enabled lateral movement from perimeter systems into core enterprise networks. Building automation systems deepened this operational entanglement. Campus-wide Building Management Systems orchestrated gate operations, chiller plants, public address systems, and environmental controls originally designed for occupant comfort. Hospitals integrated patient monitoring with corridor access controls. Warehouses deployed automated forklift navigation tied to inventory sensors. These efficiency-driven implementations seduced widespread adoption. However, without embedded security hardening, single-point IoT exploits created facility-wide failures, including elevators trapping responding personnel and strategic lighting blackouts, obscuring pursuit operations. CAPSI analyses this trajectory not as a series of disconnected technological upgrades but as inevitable architectural convergence. Security guards no longer protect static physical assets but navigate complex constellations of networked systems where digital precursors manifest as physical threats. The evolutionary imperative demands a transition from deterrence achieved through numerical presence to defence accomplished through disciplined system integration. This positions private security as the operational backbone supporting national infrastructure protection rather than peripheral service provision. Artificial Intelligence: The essential force multiplier No technology addresses modern security operations’ fundamental challenge of information overload more effectively than artificial intelligence. Cameras generate continuous high-resolution feeds capturing every movement across expansive facilities. Sensors pulse environmental shifts, including temperature anomalies, vibration patterns, and air quality fluctuations. Access logs chronicle minutiae from badge swipes to facial recognition confidence scores. These sources collectively produce petabytes of data daily, volumes that overwhelm human processing capacity despite dedicated control room staffing. Artificial intelligence masters this deluge through systematic filtering, correlation, and prioritisation. Sophisticated algorithms discard benign false positives, such as birds triggering perimeter motion detectors or shadows mimicking human forms. They elevate subtle indicators, including prolonged loitering near electrical substations or unauthorised vehicle idling patterns. Cross-domain correlation links fence vibrations detected by seismic sensors with simultaneous anomalous WiFi probes originating from mobile devices, creating actionable threat hypotheses. Physical security applications demonstrate immediate impact. Advanced video analytics crowd density metrics in transportation hubs, flagging potential stampede conditions before congestion reaches critical thresholds. Behavioural baseline models establish normal activity patterns for employees, maintenance staff, and delivery personnel, spotlighting insiders deviating from established routes or lingering in restricted zones. Object classification engines isolate potential weapons, abandoned packages, or unauthorised drones operating within secured airspace. Predictive analytics layers mine historical incident data to…

The Union Budget has always been more than a financial statement. It is a policy compass that signals the government’s priorities, strategic concerns, and development roadmap for the year ahead. In the context of a rapidly evolving threat landscape – ranging from urban fires and industrial hazards to cyber-physical attacks, border tensions, and climate-induced disasters – the Budget assumes even greater significance for the security, fire safety, and disaster management ecosystem. The Union Budget 2026-27 reflects a continued emphasis on internal security, police modernisation, intelligence strengthening, and disaster preparedness. Increased allocations for surveillance infrastructure, border management, forensic capabilities, and response forces indicate a shift toward technology-driven, integrated, and resilient safety frameworks. These investments are not only aimed at strengthening national security but also at enhancing the safety of critical infrastructure, industrial facilities, urban centres, and public spaces. For the security and fire safety industry, the Budget’s provisions are likely to translate into new opportunities in advanced surveillance, AI-enabled analytics, fire detection and suppression systems, emergency response equipment, and integrated command-and-control platforms. At the same time, the industry continues to look toward policy support in areas such as domestic manufacturing, standards harmonisation, skill development, and incentives for technology adoption. In this special feature, SecurityLinkIndia brings together the perspectives of leading industry experts, solution providers, consultants, and stakeholders to decode the real-world implications of the Union Budget 2026. Their insights offer a ground-level view of emerging opportunities, potential challenges, and the policy directions needed to build a safer, smarter, and more resilient India. Key takeaways from Union Budget 2026 The Union Budget 2026-27 marks a decisive shift in how India views safety and security – no longer as isolated line items, but as core national infrastructure. Enhanced allocations for internal security, intelligence gathering, police modernization, border management, and disaster preparedness clearly indicate the government’s intent to move from reactive responses to preventive and intelligence-led systems. What is particularly encouraging is the implicit acknowledgment that modern security challenges cannot be addressed through manpower alone; technology, integration, and data-driven decision-making are now central to the strategy. For the security, fire, and disaster management sectors, the budget reinforces the importance of integrated surveillance, early warning systems, and resilient command-and-control platforms. The focus is not just on expanding coverage, but on improving response quality, situational awareness, and coordination across agencies – an approach that aligns closely with the realities faced by large cities, critical infrastructure operators, and emergency responders. Expected opportunities and challenges for the industry in the coming year From an industry standpoint, the budget opens up significant opportunities in advanced surveillance, edge-based AI analytics, smart guarding, fire detection and suppression, and integrated disaster response solutions. Demand will increasingly favor systems that can deliver actionable intelligence in real time, reduce operator fatigue, and remain operational during high-stress scenarios such as natural disasters or security incidents. However, these opportunities come with equally significant challenges. Execution remains the industry’s biggest test. Certification readiness, cybersecurity compliance, and system interoperability continue to be areas where projects often slow down. Additionally, while budgets are being allocated, the ecosystem still faces a shortage of trained personnel capable of operating and maintaining increasingly complex systems. Vendors will need to invest not only in technology, but also in training, lifecycle support, and long-term service models to meet rising customer expectations. Policy or budgetary measures still needed to accelerate growth and preparedness While Union Budget 2026 sets the right direction, a few structural enablers are still required to translate intent into impact. Faster and more predictable certification processes – particularly for security and surveillance equipment – would significantly reduce deployment timelines. Clearer and harmonized cybersecurity frameworks for connected security devices would help both buyers and suppliers align expectations early in the project lifecycle. There is also a strong case for deeper incentives to promote indigenous design and manufacturing, especially in high-value components and software. Beyond capital expenditure, greater emphasis on capacity building – training police forces, fire services, and disaster response teams – will be critical to ensure that advanced systems are used to their full potential rather than becoming underutilized assets. Outlook for the Indian safety and security ecosystem in FY 2026–27 Looking ahead to FY 2026-27, India’s safety and security ecosystem stands at an inflection point. The convergence of policy support, budgetary allocation, and technological maturity presents an opportunity to build systems that are not only smarter, but also more resilient and sustainable. The focus will gradually shift from standalone deployments to integrated platforms that combine surveillance, analytics, communication, and command functions into a unified operational view. In this environment, success will favor organizations that move beyond box-selling and focus on outcomes – reliability, compliance, and long-term partnership. The next phase of growth will belong to solution providers who understand that security is not just about detection, but about trust, continuity, and preparedness. If executed well, the measures outlined in Union Budget 2026 could lay the foundation for a safer, more resilient India in the years to come. Key takeaways from Union Budget 2026 The Union Budget 2026 signals a decisive shift in India’s technology and security journey, with a clear focus on building capability at home. The strengthened push under the India Semiconductor Mission 2.0 is not only about self-reliance, but about ensuring that the intelligence, computing power, and hardware powering next-generation AI systems are designed and manufactured in India. Policy or budgetary measures still needed to accelerate growth and preparedness The government’s emphasis on artificial intelligence reflects a move from experimentation to real-world, mission-critical deployment. As AI becomes central to public safety, surveillance, and smart infrastructure, this Budget lays the foundation for scalable, secure, and responsible adoption across the country. Outlook for the Indian safety and security ecosystem in FY 2026–27 For homegrown technology companies, this policy clarity creates long-term confidence to invest locally, innovate for Indian needs, and build globally competitive solutions. It positions India not just as a consumer of advanced technologies, but as a trusted creator of AI-led security and infrastructure solutions aligned with the vision…