Day: September 28, 2025

Ashvani SinghHead of Security India, ASEAN & South Asiafor Standard Chartered Bank Introduction Artificial Intelligence (AI) has emerged as a central component in reshaping how organizations perceive, assess, and respond to risk. Within predictive intelligence, AI plays a pivotal role by enabling institutions to anticipate threats, disruptions and opportunities, through systematic, large-scale data analysis. Predictive intelligence involves the collection and interpretation of signals from diverse sources – including real-time news, social media, financial transactions, geospatial feeds, and operational data. Through machine learning, natural language processing and pattern recognition, AI-driven systems assist organizations in shifting from reactive crisis management toward proactive, forward-looking resilience. The growing reliance on predictive intelligence platforms reflects the evolution of modern risk management practices. For banks with extensive and distributed operations such platforms serve as centralized systems that consolidate alerts, monitor multiple risk categories, and contextualize events affecting people, assets, and operations. By integrating live incident monitoring, risk analytics, forecast modelling, and historical data, AI-enabled platforms transform risk management from a fragmented, manual exercise into a continuous, near real-time process. Supported by AI, these systems deliver actionable insights that not only provide advance warning of immediate threats but also highlight long-term vulnerabilities, thereby safeguarding continuity in uncertain environments. Advantages of AI in Predictive Intelligence Speed and capability A key advantage of AI in predictive intelligence is its ability to process data at speeds and scales that exceed human capability. Traditional monitoring depends on static reports or periodic updates, whereas AI systems are capable of scanning millions of signals from multiple geographies in real time, filtering irrelevant information to surface only the most pertinent developments. This capability enables anomaly detection, early warning, and anticipation of emerging disruptions. For banks operating in high-stakes environments, this results in enhanced situational awareness, faster decision-making, and reduced exposure to operational and reputational risks. Forecast capability Another significant benefit is the transition from descriptive awareness to predictive foresight. AI systems are designed not only to identify current developments but also to forecast potential outcomes based on recurring trends, planned events, or correlated triggers. Platforms that combine live monitoring with predictive calendars and historical archives allow institutions to prepare for a broad spectrum of risks, including geopolitical risks, travel risks, critical infrastructure risks, civil disturbances, political instability, regulatory changes, health-related emergencies, environmental hazards, natural disasters, crime, external threats, and extremism. Anticipating such risks provides decision-makers with the necessary context and lead time to allocate resources effectively, develop contingency measures, and minimize operational disruptions. Operational efficiency Operational efficiency further underscores the value of AI in predictive intelligence. Automating monitoring and alerting reduces the manual workload of banking security and risk teams, enabling a greater focus on strategic planning. Proximity alerts, concentration risk indicators, and customizable watchlists allow banks to prioritize the risks most relevant to their facilities, branches and assets. Analytical modules such as risk heat maps and comparative vulnerability assessments strengthen understanding of exposure across both regional and institutional levels. Collectively, these tools optimize limited resources, ensuring that critical risks are addressed promptly while also preventing fatigue from irrelevant or low-priority alerts. Artificial Intelligence (AI) has emerged as a central component in reshaping how organizations perceive, assess, and respond to risk. Within predictive intelligence, AI plays a pivotal role by enabling institutions to anticipate threats, disruptions and opportunities, through systematic, large-scale data analysis. Predictive intelligence involves the collection and interpretation of signals from diverse sources – including real-time news, social media, financial transactions, geospatial feeds, and operational data Key challenges and limitations Data quality Nonetheless, challenges persist – chief among these is the issue of data quality. AI models are only as reliable as the inputs they process; and poor, biased, or incomplete data can generate false positives or lead to critical threats being overlooked. Within banking environments, particularly those under strict regulatory oversight, inaccurate predictions can result in significant financial or reputational damage. Explainability A related concern is explainability. Predictive models frequently operate as opaque ‘black boxes,’ which makes it difficult for analysts and executives to understand how outputs are derived. This lack of transparency can impede adoption, especially in industries such as banking where accountability is fundamental. Integration with legacy systems Integration with legacy systems presents another layer of complexity. Many banks continue to rely on fragmented IT infrastructures, rendering the deployment of advanced predictive intelligence platforms – both costly and technically challenging. Building and maintaining such systems necessitates expertise in data science, threat intelligence, and financial risk management – skills that remain limited in the wider market. Ethical and regulatory considerations Beyond these technical barriers, ethical and regulatory considerations are increasingly critical. Data privacy requirements, cross-border data flow restrictions, and concerns surrounding surveillance or algorithmic bias necessitate the establishment of robust governance frameworks to ensure the responsible use of AI. Traditional monitoring depends on static reports or periodic updates, whereas AI systems are capable of scanning millions of signals from multiple geographies in real time, filtering irrelevant information to surface only the most pertinent developments. This capability enables anomaly detection, early warning, and anticipation of emerging disruptions Applications in the banking sector The banking sector illustrates both the potential advantages and the challenges associated with AI-driven predictive intelligence. Banks operate across multiple jurisdictions, process vast volumes of transactions, and face constant exposure to fraud, cybercrime, geopolitical instability, and physical security threats. Predictive intelligence platforms support the mitigation of these risks in several ways. Real-time monitoring of global events enables banks to anticipate disruptions affecting branch operations, financial markets, or customer confidence. Proximity alerts highlight incidents such as protests, violence, or natural disasters near critical facilities, thereby facilitating timely protective actions. Fraud detection remains a particularly relevant application. AI-driven systems can analyze transaction data to identify anomalies before they escalate into significant financial losses. Predictive models further strengthen credit risk management by evaluating borrower behavior more accurately than conventional credit scoring methods, thereby reducing default rates while broadening access to credit. Beyond financial risks, predictive intelligence enhances compliance by highlighting emerging issues that could potentially breach regulatory thresholds. Customer services also…

Col Kanwal Kishore (Retd.)Head FM at L&T Realty forPan India Projects Introduction The security challenges facing organizations today are unlike anything seen before. Digital and physical environments have become inseparably linked, meaning a disruption in one often cascades into the other. Attacks that once targeted only IT systems can now cripple supply chains, stall manufacturing, or compromise employee safety. At the same time, regulatory expectations, customer trust, and brand reputation hinge on how quickly and effectively an organization can respond. The Integrated Operations Centre (IOC) has emerged as the next stage of enterprise resilience. Building upon the traditional Security Operations Centre (SOC), which primarily focused on cybersecurity, the IOC brings together multiple domains such as cybersecurity, physical security, safety, environment, facilities, medical response, and even customer- facing operations under a single umbrella. The result is not merely a control room but a nerve centre of organizational continuity. A recent Gartner study predicts that by 2026, nearly 60 percent of large enterprises will consolidate their security and resilience functions into integrated command centres. This shift signals a clear trend – ‘the IOC is not just an innovation but a necessity.’ What is an IOC? An Integrated Operations Centre can be thought of as the central command hub of an enterprise. It continuously monitors risks, correlates events across multiple functions, and orchestrates responses in real time. While SOCs traditionally looked at IT and cyber incidents, the IOC expands this scope to ensure that disruptions in one domain do not spill over unchecked into others. Consider the example of a data centre under attack. A SOC might detect malicious network traffic, but an IOC goes further – it also correlates building management alerts on cooling failures, physical access anomalies at server rooms, and even employee safety protocols. This integrated lens enables an organization to respond not piecemeal but holistically. The evolution of security centres a. First stage (1970s-1990s) Security centres began modestly largely focused on detecting basic computer viruses or unauthorized logins. Their role was limited, reactive, and operational only during business hours. b. Expansion phase (2000s) The explosion of digital commerce and rising cybercrime pushed large organizations, particularly banks and telecom firms, to build round-the-clock SOCs. Compliance requirements added pressure, as regulators demanded evidence of security monitoring. c. Transformation phase (2010s) Persistent and sophisticated cyberattacks changed the stakes. SOCs adopted Security Information and Event Management (SIEM) platforms, threat intelligence feeds, and automation tools. They became more capable of handling thousands of alerts and mounting advanced investigations. d. Integration era (2014 to Present) The interdependence of digital and physical risks gave rise to the IOC. Cyber breaches could disrupt operational technology, while physical intrusions could expose data. Organizations began consolidating diverse command functions – security, safety, IT, and crisis response – into integrated centres. Why organizations need IOCs a. Complex threats Modern threats rarely remain confined to one channel. A ransomware attack, for example, can paralyze IT systems, lock down physical access controls, and stall production lines. An IOC provides integrated visibility and coordinates across functions, preventing isolated teams from missing the bigger picture. This was evident in a global financial services firm where analysts noticed unusual employee login attempts at the same time access control logs flagged suspicious badge activity. Because the IOC correlated cyber and physical data, it escalated the issue within minutes. The ransomware attack was contained before encryption spread, saving the company millions in downtime and penalties. b. Regulatory demands Industries such as healthcare, finance, and energy are tightly regulated. Regulations like GDPR in Europe or the Digital Personal Data Protection (DPDP) Act in India demand evidence of comprehensive monitoring and governance. An IOC, with its centralized reporting and audit trails, enables organizations to demonstrate compliance with confidence. c. Business continuity Every minute of downtime has a financial and reputational cost. According to IBM’s 2024 Cost of a Data Breach Report, the average cost of an outage now exceeds USD4.5 million. IOCs minimize such risks by orchestrating recovery across IT, safety, and operations, ensuring business continuity. A case in point comes from an energy major that runs one of Asia’s largest oil refineries. Sensors flagged unusual pressure in a critical pipeline. The IOC correlated this with historical maintenance records and environmental monitoring data, identifying a potential fault before it escalated. The refinery rerouted production and carried out predictive maintenance, avoiding a shutdown worth tens of millions in losses while ensuring worker safety. d. Resource efficiency Running separate control centres for IT, security, and facilities is inefficient. An IOC consolidates these silos, streamlining manpower, tools, and processes. This results in both cost savings and improved effectiveness. Core building blocks of an IOC a. People An IOC thrives on skilled personnel. Analysts handle everything from triaging basic alerts to conducting advanced forensics. Specialists cover IT, health, safety, and continuity, while risk analysts monitor geopolitical developments. Leadership roles ensure accountability, and training officers instil a culture of readiness through drills and awareness programs. b. Process Well-defined processes provide structure. Incident response playbooks outline how alerts are escalated and resolved. Governance frameworks ensure compliance with regulations, while post-incident reviews and tabletop exercises feed into a cycle of continuous improvement. c. Technology Technology is the backbone. Cyber tools such as SIEM and SOAR integrate with IoT-enabled fire and safety systems, medical response platforms, and geospatial intelligence mapping. APIs and middleware unify disparate systems, while AI analytics detect anomalies and predict risks. Resilience features like backup power and redundant connectivity keep the IOC operational during crises. Designing and setting up an IOC a. Operations floor The main monitoring zone operates around the clock, with analysts working on dashboards, large video walls, and integrated consoles. Ergonomic design and redundant workstations ensure efficiency and resilience. b. Support area Technical staff, based here maintains and troubleshoot systems, ensuring smooth operations without disrupting the analysts on the main floor. c. Situation room Reserved for crises and high-severity events, the situation room hosts leadership briefings with secure communications and collaboration tools. d. Forensics lab This controlled environment allows safe…

Siddharth Dahiya, CEO, Peregrine Guarding The conversation around technology and the workforce often falls into a familiar narrative – machines replacing humans. In the security industry, this debate is most visible when discussing artificial intelligence (AI) and the future of guarding. Will AI-driven surveillance, analytics, and autonomous systems make the human guard obsolete? The reality is quite the opposite. The future of guarding is not human versus AI but human plus AI. Security is, at its heart, a people-centric service. Guards represent not only the physical presence of protection but also trust, intuition, and judgment – qualities that machines cannot replicate. At the same time, AI-powered tools are rapidly enhancing the detection, analysis, and response to threats. The combination of these strengths offers a more effective, adaptive, and resilient model of security for the future. If we imagine a purely human-driven model of guarding, challenges become evident. Guards are limited by their physical stamina, reaction times, and the ability to continuously monitor large or complex environments. A guard cannot, for instance, watch dozens of CCTV feeds simultaneously or analyze large volumes of data to predict risks. On the other hand, a purely AI-driven model also has limitations. Machines may identify anomalies but lack the contextual understanding to interpret them correctly. An unattended bag at an airport could be a threat or simply a misplaced item. Without human judgment, decision-making risks becoming either overly rigid or dangerously lenient. This is why the most effective security strategies do not view humans and AI as substitutes but as complementary partners. AI is transforming the role of guards rather than replacing them. Here are a few ways this partnership is already reshaping the industry: Enhanced surveillance AI-powered cameras can detect unusual behavior patterns, unauthorized access, or crowd surges in real time. Guards receive alerts instantly, enabling faster responses. Predictive insights Machine learning can analyze historical data to predict potential security risks, helping guards focus on proactive prevention rather than reactive response. Reduced fatigue  Automation of repetitive tasks – such as monitoring video feeds – frees guards to focus on higher-value functions like situational assessment and direct engagement. Remote capabilities Integration with command centers allows guards on the ground to be supported by remote teams who have access to AI-enhanced data, improving coordination and efficiency. Rather than rendering guards redundant, AI is giving them sharper tools to be more effective at their jobs. While AI provides speed, scale, and precision, humans bring qualities that remain irreplaceable. Judgment and context Guards can interpret nuances of human behavior, cultural signals, and situational context that machines cannot. Empathy and reassurance In high-stress environments, the presence of a human guard provides comfort, authority, and emotional intelligence – something no machine can replicate. Ethical Decision-Making Decisions around the use of force, intervention, or de-escalation require moral reasoning that AI cannot deliver. Adaptability Humans can think creatively, improvise, and adapt to unforeseen scenarios, whereas AI operates within defined parameters. These strengths highlight why security will always require a human face, even as it becomes increasingly technology-enabled. To fully realize the potential of human-AI collaboration, the industry must rethink how it designs and delivers guarding services. A few guiding principles stand out: This hybrid model ensures that technology amplifies human strengths rather than competing with them. The security industry stands at a pivotal moment. With threats becoming more complex – ranging from sophisticated breaches to evolving physical risks – the demand for smarter, faster, and more adaptive solutions is clear. AI offers the tools to process information at scale and detect anomalies with unmatched precision. Humans bring the intuition, ethics, and adaptability that no machine can replicate. The real breakthrough lies in combining these strengths. Guards empowered by AI are not diminished by technology; they are elevated. Together, they can deliver security outcomes that are more reliable, proactive, and resilient than ever before. The future of guarding, therefore, is not about choosing between people or technology. It is about creating a partnership where each enhances the other. In a world of increasing uncertainty, the strongest line of defense will be built not by humans or AI alone, but by their collaboration.