Day: January 20, 2026

Ramit LuthraPrincipal Consultant – North America at 5Tattva Artificial intelligence has moved from strategic discussion to operational reality. For CIOs and CISOs, AI is no longer a future initiative to be evaluated. It is already embedded in development pipelines, service desks, analytics platforms, and business decision workflows, often through tools adopted faster than governance and security models can adapt. This creates a familiar leadership tension. The business expects speed and measurable outcomes. Technology and security leaders are expected to protect data, manage risk, and maintain regulatory posture. AI intensifies this challenge by introducing new data flows, opaque processing, and third-party dependencies that traditional controls were never designed to fully govern. What makes this moment different is not the technology itself, but the direction of travel. The way organizations adopt AI today is reshaping how cybersecurity risk is defined, how audits are conducted, and how confidence is established with boards, customers, and regulators. Taken together, these perspectives outline key technology and cybersecurity predictions for 2026, reflecting how AI governance, risk management, and audit practices are expected to evolve as AI becomes embedded across the enterprise. Rather than predicting specific tools or timelines, the most reliable way to discuss the future of AI governance is to identify the pressures that are already changing organizational behavior. Safe prediction #1: Most AI risk will come from normal business use, not attacks The dominant cybersecurity risk associated with AI will not be sophisticated adversaries or novel exploits. Instead, it will stem from ordinary employees and systems using AI as intended. Sensitive data will enter prompts, be retained in logs, reused by vendors, or embedded in downstream outputs without malicious intent. Traditional data loss prevention tools struggle in this environment because nothing appears abnormal. From an audit perspective, this means reviews will increasingly focus on how data moves through AI systems during legitimate use, not just whether AI tools are formally approved or blocked. Early enterprise adoption patterns indicate that this risk is already materializing as AI becomes part of routine business workflows. Safe prediction #2: Data exfiltration will be redefined by governance, not malware Historically, data exfiltration implied clear violations or breaches. In AI-enabled environments, data can leave the organization quietly, legally, and repeatedly. The core question shifts from ‘Was data stolen?’ to ‘Did we understand, approve, and monitor this data use?’ As a result, audit evidence will increasingly include data classification rules, AI usage policies, vendor retention terms, and monitoring of prompt behavior. This prediction aligns closely with how regulators already evaluate cloud and third-party risk. Taken together, these pressures point toward a broader shift in how audits themselves are designed and interpreted. Safe prediction #3: Audits will evolve from control checks to decision validation Technology audits are moving away from static control verification toward validation of decision-making processes. In the AI context, auditors will ask why a specific AI use case was approved, what risks were identified and accepted, how outcomes are monitored over time, and who has the authority to intervene if behavior changes. Governance artifacts such as AI inventories, risk tiering frameworks, approval records, and exception logs will become central audit evidence. This mirrors established trends seen in standards such as ISO 27001, ISO/ IEC 42001, and the NIST AI Risk Management Framework. Safe prediction #4: AI governance will become a confidence signal for leadership Boards, customers, and regulators are less interested in whether AI is used and more interested in whether it is governed. Organizations that can clearly explain how AI decisions are made, monitored, and corrected will face less friction, fewer surprises, and faster approvals. In this context, audits increasingly function as confidence mechanisms rather than mere compliance artifacts. Trust, rather than technical detail, will drive regulatory and customer confidence. While regulatory approaches will differ by geography, expectations around accountability and explainability are converging. Safe prediction #5: Strong audits will enable faster AI adoption, not slower Organizations without clear AI governance often swing between two extremes – freezing innovation altogether or allowing uncontrolled experimentation. Both outcomes increase risk. Well-designed audits that clarify boundaries, ownership, and accountability allow teams to move faster, with fewer internal debates and less reliance on shadow AI usage. Here, the audit function becomes an enabler of scale rather than a brake on innovation, echoing the role audits previously played during cloud adoption, outsourcing, and DevOps transitions. Why audits matter more as AI accelerates AI introduces uncertainty, while audits introduce structure. In an AI-enabled enterprise, audits now serve three audiences simultaneously. CIOs and CISOs gain clarity and defensibility, business teams gain permission to innovate safely, and regulators and customers gain assurance that risk is being governed. This triangulation explains why audits are becoming increasingly important, not less so, as AI adoption accelerates. What CIOs and CISOs should do now CIOs and CISOs should begin by assuming that AI is already in use and focus on discovery rather than prohibition. Mapping AI data flows is more important than cataloging AI tools alone, particularly understanding where sensitive data enter and exits AI systems. AI use cases should be classified by risk and impact so that governance is applied where it matters most. Audits should be designed around decisions rather than documents, ensuring they capture intent, oversight, and accountability. Finally, leaders should be prepared to explain AI governance in simple terms, because confidence comes from clarity, not technical depth. Conclusion The future of AI governance will not be defined by regulation alone or by technology breakthroughs. It will be shaped by how well organizations can demonstrate control, intent, and accountability as AI becomes embedded in everyday operations. The safest prediction is this – CIOs and CISOs who treat audits as forward-looking assurance mechanisms will govern AI more effectively, move faster with confidence, and earn greater trust from boards, users, and regulators. As AI becomes embedded across the enterprise, the greatest cybersecurity risks will come from normal business use, not malicious attacks. Safe, forward-looking audits are emerging as a critical tool for CIOs and CISOs to govern AI, manage…

With the rapid increase of volume, the e-commerce/ retail industry requires the high demand for rapid delivery. The traditional logistics and warehousing methods have become overwhelmed. In order to cope with the logistics pressure brought by events such as black Friday and double eleven festival, companies urgently need stable and reliable intelligent systems to release manpower. Hikrobot’s ecommerce solutions are designed to transform the way online retailers manage their logistics and supply chain operations. Their Autonomous Mobile Robots (AMRs) and intelligent logistics solutions address key challenges faced by ecommerce businesses, including: Some of the specific ecommerce solutions offered by Hikrobot include: Operational Challenges Solution overview: Aimed at unmanned handling, informatization of inventory management and high efficiency of picking, Hikrobot solution integrates robots, AI and the Internet of Things, facilitating goods inbound, sorting and collection with the help of RCS and IWMS, to improve operational efficiency and the level of automation. Solution Components Latent Mobile Robot (LMR) Hikrobot’s Latent Mobile Robot (LMR) is a game-changer for environments where space is limited. Its unique design allows it to slide underneath shelves, pallets, and other carriers, lift them, and transport them with ease. This eliminates the need for bulky lifting equipment and opens up valuable floor space. Hikrobot’s LMR is part of their comprehensive portfolio of Autonomous Mobile Robots (AMRs), designed to optimize operations, reduce costs, and improve safety. Key features: Forklift Mobile Robot (FMR) Hikrobot’s Forklift Mobile Robot (FMR) is very important component for warehouse automation! These robots are designed to handle standard bins and pallets with ease, using advanced laser SLAM navigation and vision technology for precise movement and obstacle avoidance. Key features: Popular models Hikrobot’s FMR series is designed to streamline your logistics and material handling processes, improving productivity and safety. Carton Transfer Unit (CTU) Hikrobot’s Carton Transfer Unit (CTU) is an automated, unmanned picking and handling robot designed to optimize warehouse operations. It can transfer multiple goods simultaneously, improving picking efficiency and storage capacity. Key features: The CTU is part of Hikrobot’s comprehensive portfolio of Autonomous Mobile Robots (AMRs), designed to streamline logistics and material handling processes in various industries, including e-commerce, manufacturing, and consumer electronics. iWMS1000 The iWMS1000 is a smart warehouse management system designed to optimize warehouse operations and logistics. It’s a comprehensive solution that integrates various technologies, including: By integrating with Hikrobot’s AMRs and other technologies, the iWMS1000 creates a smart, efficient, and agile warehouse ecosystem. Solution Advantages Rapid Fulfillment: Hikrobot’s AMRs enable fast and accurate order fulfillment, reducing delivery times and improving customer satisfaction Application Case Hikrobot’s solutions have been successfully implemented by various ecommerce businesses, including Superdry, which saw significant improvements in inventory accuracy, productivity, and order processing times. Background Superdry is an iconic, global fashion brand operating through 768 store locations in 65 countries. Maintaining product availability, keeping efficient fulfillment, and processing the returns rapidly are essential for ensuring the best customer experiences. To support future growth, deliver accurate and efficient picking, Superdry introduced Hikrobot intelligent mobile robots. Solution Superdry introduced 46 robots into the Burton-upon Trent DC and 20 robots in European DC for order picking and returns handling. Meanwhile, combination of PTL and indicator light ensured the accuracy of order fulfillment. Customer Benefit Read More