Category: Feature

Why corporates must understand the differ- ence between identification and verification Dr. Rajiv Mathur, PartnerMIGS Global Consulting Pvt. Ltd. Every morning at 9 o’clock, the same scene plays out in thousands of corporate offices across India. Employees arrive at the gate, show their ID cards, scan a QR code, smile at the security guard, and walk inside. The process looks smooth, efficient, and modern. Managers feel satisfied that their workplace is secure. But one day, a question quietly arises. ‘What if the person who walked in was not the person he claimed to be?’ This is where the difference between identification and verification becomes not just a technical issue, but a story about trust, risk, and resilience. Identification means knowing a name. Verification means proving the person is genuine. Most organizations stop at knowing the name. Very few check the truth. A small story with a big lesson Mr. Verma worked in a large corporate campus in Gurugram. He had an official ID card with his photograph and employee number. One evening, after a long day, he left his ID card on the tea stall near the metro station. By the next morning, it was gone. Two days later, a man entered the same office using that ID card. He looked somewhat similar to Mr. Verma. He wore formal clothes. He walked confidently. The security guard saw the card, saw the photo, and let him in. No one asked him to prove who he was. No system checked his face against stored records. No alert was generated! The office had performed identification. It had not performed verification. Nothing serious happened that day, but it could have. And that is what makes the story dangerous. Security failures are not measured by what happened yesterday, but by what can happen tomorrow. Understanding the difference in simple words I dentification is when someone says, “I am Rajesh,” and shows something to support that claim. It could be a card, a number, or a QR code. The system accepts that claim and moves on. Verification is when the system asks, “Are you really Rajesh?” and checks evidence. It may match a face, a fingerprint, or a secure digital record. Only after that proof is accepted does the system allow entry. Identification is like reading the name on a visiting card however, verification is like meeting the person and matching the face.One tells you what is presented. The other tells you what is true. Where Aadhaar enters the story India created Aadhaar to give every citizen a digital identity. It was a historic and powerful step. Aadhaar is now used in banking, telecom, government schemes, and many other services. It has brought efficiency and inclusion to millions of people. But Aadhaar is mainly an identification platform, not a complete verification system for corporate security operations. Aadhaar belongs to the ecosystem of UIDAI Aadhaar and was designed for national identity, not for managing access inside offices, factories, ports, or laboratories. Aadhaar can say, “This Aadhaar number exists.” It does not always say, “This person standing here is truly the owner of this identity in this operational context.” That difference is very important. The vulnerability of identification In many organizations today, Aadhaar QR codes or Aadhaar numbers are used as a proof of identity for entry or verification. This creates a feeling of safety because Aadhaar is trusted nationally. But trust without checking becomes blind trust. Aadhaar cards can be photocopied, QR codes can be shared, numbers can be leaked, and photos can be edited. When Aadhaar is used only as an identifier, it becomes just another card. And like any card, it can be lost, stolen, or misused. Imagine a contractor working at a construction site of a large refinery. His Aadhaar card is used for entry every day. One day, someone else borrows that card and enters. The system records the Aadhaar number, not the real person. If an incident happens, the company will have no way to know who truly entered. The system knew an identity. It did not know the human being. Why corporates need more than identity Corporates today are not just protecting buildings. They are protecting data, intellectual property, machinery, and human life. A wrong person in the wrong place can cause damage that cannot be reversed. Factories run with hazardous chemicals, IT parks handle sensitive data, hospitals deal with patient records, airports manage national security zones, ports handle cargo and customs. In all these critical places, just knowing a name is not enough – one must know the truth. This is why verification is stronger than identification. Verification actively checks authenticity. It does not assume honesty. It tests it. Resilience means the ability of a system to continue safely even when something goes wrong. A resilient system is built on verification, not on assumption. A day in two different offices Let us imagine two corporate offices. In the first office, the guard checks the ID card and allows entry. The system logs the card number. That is identification. In the second office, the system scans a QR code and matches the face of the person with a stored secure record. It checks whether the person is allowed in that area at that time. It logs the entry and alerts if something does not match. That is verification. One day, an intruder tries to enter both offices using a stolen card. The first office fails quietly. The second office stops him at the gate. Which office is resilient? The second one. The human side of verification Verification does not only protect the organization. It also protects honest employees. When systems verify people properly, there is clarity about who was present, when, and where. This prevents false blame and confusion. Imagine an incident in a laboratory. Without verification, anyone whose ID card was used that day can be blamed. With verification, the system knows exactly who entered. Verification creates. accountability. Identification creates records. There is a big difference. Why this matters…

We are no longer debating whether AI belongs in network security – that conversation is over. The real question facing network and infrastructure leaders today is whether their organizations are moving fast enough to harness it meaningfully. Modern networks have become staggeringly complex – sprawling across hybrid environments, multi-cloud architectures and thousands of connected endpoints, generating torrents of traffic data that no human team can analyze at the speed and scale today’s threats demand. Meanwhile, adversaries are exploiting that very complexity, probing for misconfigured segments, lateral movement opportunities and zero-day vulnerabilities faster than traditional network monitoring tools can flag them. The organizations that will define the next era of network security are not those simply bolting AI onto aging network infrastructure, but those fundamentally rethinking how their networks are monitored, defended, and made resilient with intelligence and automation at the core. This is not just a technology upgrade; it is a strategic rethinking of how networks are protected in a world where the perimeter no longer exists. The growing complexity of modern networks Today’s enterprise network looks nothing like it did a decade ago. The modern network is no longer a contained, manageable perimeter, it is a dynamic, borderless ecosystem, and securing it demands an entirely new way of thinking. Why traditional security approaches are reaching their limits Traditional network security has long relied on rule-based systems and manual monitoring. But as networks grow larger and more interconnected, this model is beginning to show its limits. Security teams today deal with an overwhelming volume of alerts and log data generated by multiple tools across the network. It’s not uncommon for analysts to face thousands of alerts in a single day. The result is alert fatigue where teams spend significant time sorting through notifications, trying to determine which ones actually signal a real threat. At the same time, cyberattacks are moving faster than ever. Threat actors can gain access, escalate privileges, and move laterally within minutes. Security processes that depend heavily on manual investigation often struggle to respond at the same speed. There’s also the challenge of detecting unknown or sophisticated threats. Many traditional tools rely on predefined rules or known signatures, which means they are effective against familiar attack patterns but less capable of identifying new or evolving techniques. As a result, organizations are increasingly finding that conventional security approaches alone are no longer enough. The scale and speed of modern threats require more adaptive capabilities, an area where AI is starting to play a critical role. The threat landscape has fundamentally changed, and so must we. AI is no longer a future investment, it is the operating infrastructure of secure, resilient organizations today. Our commitment is to build security into the architecture of everything we do, not as an afterthought, but as a foundation How AI is reshaping network security Artificial intelligence is reshaping how organizations defend their networks by enabling security systems to analyze massive volumes of data, recognize patterns, and respond to threats far more quickly than traditional approaches allow. Instead of relying purely on static rules, AI introduces a more adaptive and intelligent layer to security operations. Key capabilities include: By combining these capabilities, AI is helping organizations move toward a more proactive and responsive security approach, one that is better equipped to keep up with the scale and sophistication of modern cyber threats. Security is no longer just an IT conversation, it is a brand trust conversation. When we talk to customers and partners, they want to know their data and operations are protected by intelligent, adaptive systems. AI-powered security is not just a technical differentiator; it is a message that resonates at every level of the business From reactive to predictive security For a long time, cybersecurity has largely been reactive. Security teams would detect an alert, investigate the incident, and then respond after a threat had already entered the network. While this approach worked in slower and more predictable threat environments, today’s attack landscape demands a more forward-looking strategy. Artificial intelligence is helping organizations shift from simply reacting to threats to anticipating and preventing them. By continuously analyzing large volumes of network activity and security data, AI systems can uncover patterns that may signal potential risks long before they escalate into full-scale incidents. The strategic value of AI in security operations Beyond improving threat detection, AI is also creating meaningful operational advantages for organizations. As security environments grow more complex, AI helps teams manage workloads more effectively and focus their attention where it matters most. Challenges and considerations in AI adoption While the benefits of AI in network security are significant, adopting these technologies also requires careful consideration. A balanced strategy recognizes both the opportunities and the practical challenges involved. The path forward AI is not a silver bullet but it is fast becoming a non-negotiable foundation for any serious network security strategy. The complexity of modern networks, the speed of evolving threats, and the limitations of traditional approaches have collectively created a reality that human teams and rule-based systems alone cannot address. AI bridges that gap not by replacing the expertise of security professionals, but by amplifying it. The organizations that will lead in network security over the next decade are those that treat AI not as a bolt-on capability, but as a core architectural principle, embedded into how threats are detected, analyzed, and contained. The shift is already underway. The only question that remains is how decisively your organization chooses to move. Read More

Sumiit Katyal, CEO, Asim Navigation India Pvt Ltd. Transforming Security Planning with360° Mapping, AI & Real-Time Intelligence In an era where urban complexity, population density, and security threats are rapidly increasing, traditional methods of surveillance and planning are no longer sufficient. Law enforcement agencies today require proactive, data-driven, and immersive tools that enable them to anticipate risks, plan effectively, and respond with precision. One such transformative technology is the Digital Twin, powered by advanced 360° imaging, AI analytics, and geospatial intelligence. What is a Digital Twin in Security? A Digital Twin is a highly accurate, real-world digital replica of physical environments, enabling authorities to visualize, analyze, and simulate real-world scenarios in a virtual space. Using systems like the Mosaic Meridian 360 Camera, entire urban environments – roads, public spaces, religious sites, and high-density zones – can be captured in high-resolution 360° imagery and converted into an interactive digital platform. When combined with AI-driven analytics platforms like FocusVu.ai, this transforms static mapping into a live, intelligent security ecosystem. Relevance for public safety & crowd management India frequently witnesses large-scale gatherings including – religious processions, political rallies, public demonstrations, cultural events and etc. These events present significant challenges for police and security agencies such as crowd control, route planning, emergency response readiness, identification of vulnerable zones and so on. A Digital Twin platform provides a powerful pre-planning and execution tool, enabling authorities to move from reactive management to proactive preparedness. Pre-planning of events & processions One of the most critical applications of Digital Twin technology is in pre-event planning. Using 360° mapped environments – authorities can virtually walk through the entire route of a procession or rally; identify bottlenecks, choke points, and sensitive areas; plan entry/ exit routes, diversion strategies, and emergency access points; and pre-define barricading zones and access control points. With virtual barricading and access control features enabled through FocusVu.ai, planning committees can digitally mark restricted zones, simulate crowd movement, and allocate personnel effectively This allows even field personnel and patrolling units to clearly understand deployment plans before stepping on ground. Real-time monitoring & AI-based alerts During live events, integration of Digital Twin with real-time video feeds provides – live situational awareness from multiple camera sources; AI-based pre-alerts for unusual activity, overcrowding, or breaches; and faster decision-making at command centers. The system shifts operations from – manual surveillance to Intelligent, automated monitoring. This is particularly crucial in high-risk scenarios, where even minor delays can escalate into serious incidents. High accuracy mapping & asset visualization Modern Digital Twin systems go beyond visual representation and deliver engineering-grade accuracy. Using high-resolution 360° imaging (Mosaic systems), LiDAR-based scanning technologies, and advanced geospatial processing – authorities can achieve less than 5cm positional accuracy, creation of detailed 3D models of environments, and real-time mapping of assets such as barricades, CCTV cameras, entry/ exit gates, emergency facilities etc. This enables precise planning and execution, especially in dense urban environments. Support for field units & patrol teams One of the biggest challenges in large-scale deployments is communication and clarity for field personnel. Digital Twin platforms address this by providing visual references instead of textual instructions, enabling teams to view exact deployment locations, and Helping patrol units understand – routes, restricted zones, and emergency response plans. This significantly reduces confusion, miscommunication, and response time. Pre & post-event inspections Digital Twin technology also plays a critical role in – pre-event inspection such as verification of planned arrangements, identification of last-minute risks, and validation of security layouts – and post-event analysis by incident reconstruction, performance review of deployment, and identification of gaps for future improvement. This creates a continuous improvement cycle for security operations. Towards smart & predictive policing The integration of 360° Digital Twin (Mosaic systems), AI Video Analytics (FocusVu.ai), and LiDAR & high-accuracy geospatial mapping is enabling a shift towards Smart Policing, where – decisions are data-driven, risks are identified in advance, and resources are optimized efficiently. Conclusion As cities grow and public events become larger and more complex, security agencies must adopt advanced technologies to stay ahead of emerging challenges. Digital Twin technology offers a holistic solution – combining visual intelligence, real-time monitoring, and predictive analytics – to enhance public safety, operational efficiency, and strategic planning. By leveraging platforms like the Mosaic Meridian 360 Camera and FocusVu.ai, law enforcement agencies can transform the way they plan, monitor, and secure large-scale events, ensuring safer environments for citizens and more effective operations for security forces. Read More

Rajeev Sharad, Founder & CEO,Urbaforce Solutions Pvt Ltd (Consultant) A comprehensive review of the 100 Smart Cities Mission (2014-2025) Introduction The Smart Cities Mission, launched in 2014, stands as one of India’s most ambitious urban transformation programmes of present times. Spearheaded by the Ministry of Housing and Urban Affairs – MoHUA (then Ministry of Urban Development – MoUD) in partnership with State Governments, the mission aimed to develop 100 cities across the country into models of matured and self-sustainable bodies with technologically advanced urban living for ease of citizens. With its conclusion in 2025, the mission has become a significant touchstone for policy makers, urban planners, and citizens alike, offering insights into the challenges and opportunities of modern city development in India especially in brownfield areas. Genesis of the mission India’s urbanisation is projected to accelerate significantly over the coming decades. According to various demographic estimates, the urban population is expected to reach nearly 40% by 2030 and 50% of the country’s total population by 2050, compared to around 34% in 2024. This rapid growth underscored the urgent need for robust urban infrastructure planning, as millions migrate to urban centres or turning smaller cities into urban and commercial hubs, seeking better work opportunities and quality of life. The genesis of the Smart Cities Mission is linked to this growing urbanisation in India and the pressing need for cities to become more liveable, resilient, and inclusive. Indian cities have been grappling with rapid population growth, infrastructural bottlenecks, and environmental degradation, not to forget the socio-political alignments with demographic uniqueness. Recognising these challenges, the Government of India initiated the mission as part of a broader vision for planned urban development. The formal launch of the mission in June 2014 marked a shift towards integrating digital solutions and citizen-centric planning, positioning cities as engines of economic growth and innovation. Objectives and vision The Smart Cities Mission was underpinned by a clear vision – to promote cities that, with the use of technology or otherwise, provide core infrastructure, clean and sustainable environments, and a decent quality of life for their citizens. Central objectives include improving urban mobility, expanding affordable housing, ensuring robust water and energy supply, and enhancing safety and governance. Implementation framework MoHUA leads the mission’s implementation management, working in close collaboration with State Governments and urban local bodies through 50% financial assistance on the basis of project approvals. The selection of the 100 smart cities was the result of a competitive process, with cities evaluated on their preparedness, vision, and capacity for transformation. Each selected city developed its own smart city proposal, outlining projects and strategies tailored to local needs and aspirations. This decentralised approach enabled flexibility and innovation, while the Ministry provided guidance, funding, and oversight to ensure consistency and accountability. Coordinated approach between Central and State governments has been instrumental in driving the mission forward. Financial support has been channelled through a combination of central grants, state contributions, and leveraging private sector investment. The formation of Special Purpose Vehicles (SPVs) for each city ensured dedicated focus on project execution, monitoring, and stakeholder engagement, strengthening the mission’s governance framework. Key initiatives and strategies The Smart Cities Mission encompassed a broad range of projects and interventions, targeting area-based development of urban infrastructure, use of digital technology, and augmentation portals for citizen services. Major initiatives include the development of Integrated Command and Control Centres (ICCC) which are the aggregation nerve centres of technology based smart mobility solutions, utilities supply monitoring, real-time traffic management waste management, telecommunication services, energy-efficient street lighting, citizen service kiosks and digital governance platforms. Urban planning strategies under the mission emphasised mixed land use, compact city development, and revitalisation of public spaces. The adoption of area-based development allowed cities to focus on specific neighbourhoods, demonstrating tangible improvements before scaling up. Technological interventions – such as sensors, IoT devices, and GIS mapping – have been crucial in enabling data-driven decision-making and responsive urban management. Efficacy and achievements In 2024, MoHUA engaged the services of an esteemed audit and assessment organisation to study maturity index of the Smart Cities through measurable outcomes of the use cases and several other pillars of evaluation. Cities have been showcasing innovative models of waste management, sustainable transport, traffic control, weather reporting & e-governance platforms. The mission has catalysed the adoption of renewable energy, increased green cover, and promoted inclusive urban development. While progress varies across cities, the mission has driven a culture of innovation and accountability, setting new benchmarks for urban transformation in India. Though, many cities have witnessed a deluge of urban mobility, pollution control, and public safety measures through smart infrastructure projects, the efficacy has not been as planned. The establishment of integrated command centres has enabled real-time monitoring of utilities and emergencies, which expected to improve governance and citizen satisfaction, but the assessments say otherwise. Challenges and lessons Despite its achievements, the Smart Cities Mission has encountered several shortcomings and hurdles. Delays in procurement process, stakeholder bottlenecks, and program management at local levels have impacted progress and efficacy in certain cities. Coordination among multiple stakeholders, government agencies, private partners, and local communities has often posed challenges, requiring robust mechanisms for engagement and conflict resolution. Impact analysis The Smart Cities Mission has had far-reaching socio-economic and environmental impacts. It has stimulated job creation, attracted investment, and improved the quality of urban life for millions. Technological advancements have empowered city administrations, enabling more efficient service delivery and enhanced citizen participation. Environmental benefits include reduced energy consumption, better waste management, and increased resilience to climate change. From a development perspective, the mission has fostered urban innovation, strengthened institutional capacities, and promoted a culture of evidence-based policy making. By setting new standards for urban governance and infrastructure, the mission has inspired other cities and regions to pursue similar transformations, contributing to India’s broader urbanisation agenda. Some of the key concerns that have led to the under-utilised potential of the Smart Cities is the lack of following plans: Redefined roadmap for Smart Cities The Smart Cities Mission…

Kunwar Vikram SinghChairman, Central Association of Private Security Industry ( CAPSI) Women security guards in India represent strength, discipline, and dedication. They stand at the front lines of safety in residential complexes, offices, hospitals, malls, schools, and public institutions, quietly ensuring that communities remain secure and orderly. Their presence not only enhances physical security but also creates a sense of comfort and trust, especially for women, children, and the elderly. In recent years, the role of women in the private security sector has grown significantly. These women perform demanding duties such as access control, surveillance monitoring, crowd management, and emergency response. Often working long and irregular hours, they demonstrate professionalism, alertness, and courage. Their work requires not only physical stamina but also patience, emotional intelligence, and the ability to handle challenging situations with calmness and dignity. Beyond their professional responsibilities, women security guards continue to carry out their roles within the family. Many of them return home after a full shift to take care of household responsibilities. They nurture their children, guide them in education and values, and ensure that their families remain united and stable. Their role as mothers is particularly significant, as they often strive to give their children better opportunities through their own hard work and sacrifice. At the same time, they care for elderly parents and in-laws with respect and compassion. In many Indian households, women security guards contribute not only financially but also emotionally, maintaining harmony and stability within the family. They often support their husbands and share the responsibility of running the household, showing that partnership and mutual respect are the foundations of a strong family. What truly distinguishes these women is their moral strength and exemplary character. Despite the pressures of balancing demanding professional duties and family obligations, they uphold integrity, discipline, and cultural values. Their dedication to duty, honesty in work, and commitment to family responsibilities make them role models in their communities. Women security guards are therefore not just employees in the security industry; they are protectors of society and builders of strong families. They embody resilience, responsibility, and moral courage. Recognizing and supporting their contribution is essential for building safer communities and a more inclusive security sector in India. Their story is one of quiet heroism – serving the nation by protecting its people while nurturing the values and relationships that hold society together. Wishing every woman Security Guard & Officer a very happy International Women’s Day. Read More

Biplob BhattacharjeeZonal Business Manager-East, North East and SAARC CountriesVicon Security The evolution of Video Management Systems From NVRs to Intelligent Platforms Over the past decade, Internet Protocol (IP) video technology has become the preferred choice for security managers, system integrators, consultants, and architects. In the early stages of this transition, embedded Network Video Recorders (NVRs) gained widespread adoption due to their simplicity and cost-effectiveness. However, as surveillance requirements evolved with the adoption of high-resolution cameras, multi-sensor systems, and advanced analytics – traditional NVRs began to show their limitations. Their architecture was not designed to handle the increasing complexity, scalability, and intelligence required in modern deployments. The Rise of Video Management Systems (VMS) The limitations of NVRs paved the way for Video Management Systems (VMS), which today form the backbone of IP surveillance. A modern VMS ensures consistent video throughput across all channels, scalable architecture for growing deployments, centralised control and monitoring, and seamless integration with analytics and third-party systems. More importantly, a robust VMS transforms surveillance from simple video recording into a comprehensive security management solution. Key considerations when selecting a VMS Selecting the right VMS is critical for long-term system performance and scalability. Key factors include: These parameters define whether a system can scale and adapt to future requirements. The power of Valerus Valerus is an advanced, open-platform video management system designed to address the demands of modern security environments. More than a traditional VMS, Valerus functions as a centralised security management platform, integrating video surveillance, access control, Automatic Number Plate Recognition (ANPR), Vape detection and other specialised systems. All functionalities are unified through a single-pane-of-glass interface, allowing operators to manage complex environments without switching between applications. The platform also supports efficient multi-site management, enabling centralised monitoring and control of geographically distributed locations – an essential requirement for enterprises and infrastructure projects. Flexible and intelligent alarm management One of Valerus’ key strengths lies in its configurable alarm management system. Operators can define real-time alerts, configure automated workflows, and standardise incident response procedures. From simple notifications to automated actions such as partial or full lockdowns, Valerus ensures faster and more structured responses to critical events. Built-in system health monitoring System reliability is critical in any surveillance deployment. Valerus includes an integrated health monitoring dashboard that provides proactive alerts on system performance. This enables administrators to identify potential issues early, prevent system failures, reduce downtime, and maintain continuous operational efficiency. AI-powered investigation and search As surveillance systems generate increasing volumes of data, the ability to quickly extract relevant information becomes essential. Valerus leverages AI-driven analytics to significantly reduce investigation time by filtering out irrelevant footage. Key search capabilities These tools transform investigations from a time-intensive process into a targeted, data-driven workflow. Conclusion: The future of security management The role of video surveillance is evolving from passive monitoring to proactive security management. This shift requires platforms that can integrate multiple technologies, process data intelligently, and provide actionable insights in real time. Valerus is positioned to meet this need – serving as a central security management hub for multi-sensor, multi-application environments. With advanced search capabilities, seamless integration, intelligent alarm handling, and centralised multi-site control, Valerus enables organisations to build a data-driven and future-ready security ecosystem. As AI adoption continues to accelerate, platforms like Valerus will play a critical role in transforming Security Operations Centres into intelligent command centres – capable of responding faster, operating smarter, and scaling efficiently. Read More

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