Dr Ryad Soobhany, Deputy Academic Head of School of Mathematical & Computer Sciences, Heriot-Watt University Dubai 

Camera systems, such as CCTV, are mainly used for video surveillance within organisations. Passive video surveillance has been part of the security ecosystem for years, providing organisations with the ability to monitor different areas simultaneously, while recorded footage can be inspected after an event occurs.

The evolution of computer chips and increased processing power has led to improved image quality, including better performance in low-light conditions, particularly at night. The advent of edge computing, with processing performed on edge devices within a network, has made object and scene recognition more readily accessible. This evolution has led to more active video surveillance through video analytics, where events can be identified and addressed in real time. Examples include number plate recognition systems and facial recognition for access control.

With the combination of AI and video data, computer vision can be integrated with business analytics to transform video surveillance from a security infrastructure into a business intelligence tool. Video surveillance is moving from passively recording events to sensing what is happening in the environment. This helps to answer questions such as which areas are congested, how customers are moving, how long they dwell in particular areas, and whether unusual events are occurring. Businesses can use this data to optimise their operations, such as improving the customer checkout process, monitoring asset or staff movement, and managing crowds or traffic flows. Organisations can also incorporate contextual business information to enhance the analytics by combining behavioural data with CRM or IoT data, providing additional context and insights for decision-making and improving the customer or visitor experience.

Video surveillance can be used to optimise operations, improve customer experiences, and support strategic decision-making across industries. Video itself does not create business intelligence; rather, its value increases when visual information is combined with other operational and contextual data, while computer vision is used to understand and enhance business operations. The pipeline from video to business intelligence can be represented as obtaining data from video sources such as fixed cameras, IoT-enabled cameras, or robots. Computer vision is then applied to the video to detect or track people, objects of interest, or activities. Behavioural data are taken into account, such as customer dwell time in particular product areas, occupancy of rooms or halls, movement of people across a building, and anomalies in behaviour. At this stage, operational data are also considered. The final stage is the business intelligence stage, where trends are identified and insights are inferred. At this stage, data visualisation plays an important role, with dashboards and data storytelling displaying correlations, predictions, and infographics that provide more than simple visual summaries of surveillance data. The camera becomes an operational sensor for the organisation. The important transformation occurs when visual data are combined with business data, such as sales, operational, and IoT data, to convert visual observations into business knowledge that informs operational, tactical, and strategic decision-making within an organisation.

The levels of business intelligence can be viewed as three analytical capabilities: descriptive intelligence, diagnostic intelligence, and combined predictive and prescriptive intelligence. Descriptive intelligence usually addresses what is happening. Visual analytics can identify footfall, occupancy, queue length, waiting time, dwell time, traffic flow, movement patterns, and detected events. Visualisation dashboards can present these indicators in real time or over historical periods, allowing managers to understand the current state of an operation. For example, a museum dashboard can show heat maps of visitor numbers, dwell time, and movement across different exhibition areas, while analysing visitor behaviour and providing insights into the optimal placement of exhibitions. An airport dashboard could display passenger volumes and queue lengths across terminals.

OPPO A7 Pro 5G puts longevity at the centre of the smartphone experience!

With an 8,000mAh battery, dual 50MP cameras, IP69K protection and a five-year smoothness promise, OPPO’s latest A Series smartphone makes a strong case for devices designed around longer-term ownership.

Smartphone launches have traditionally revolved around faster processors, increasingly sophisticated cameras and, more recently, AI. With the A7 Pro 5G, OPPO is putting another consideration firmly into the conversation: how well a smartphone can hold up over time.

At the centre of that proposition is an enormous 8,000mAh battery. Large-capacity batteries are becoming increasingly common, but what is interesting about the A7 Pro 5G is how little the battery dictates the physical character of the phone.

Despite the capacity, the device does not immediately look or feel like a rugged smartphone. The Shine Titanium review unit has an understated finish, while the alternative Surfing Blue introduces a more distinctive Dynamic 3D Wave Texture.

That relatively conventional appearance hides some serious durability credentials.

Built for longer ownership

The A7 Pro 5G carries IP69K dust and water resistance alongside military-grade shock resistance. Rainstorm Touch is designed to keep the display responsive even when used in heavy rain.

The longevity argument extends to the battery itself. OPPO says it can retain more than 80% of its rated capacity after 2,000 complete charging cycles, underpinning the company’s six-year battery durability proposition.

This matters because battery degradation remains one of the most noticeable compromises as smartphones age. Increasing capacity solves part of that equation; maintaining useful capacity several years into ownership potentially solves another.

Reverse wired charging also allows the A7 Pro 5G to supply power to another connected device, adding some practical value to that substantial battery reserve.

Selfies get the 50MP treatment

OPPO has also placed considerable emphasis on the front-facing camera.

The 50MP Ultra-Wide AI Zoom Selfie Camera offers a 100-degree field of view and can automatically move between 1x and 0.6x framing when additional people enter the shot.

It is a useful approach for group photographs, travel and increasingly video-led social content, where a wider front camera can make considerably more sense than simply increasing resolution. Ultra-steady video has also been included to improve handheld recording.

At the rear is another 50MP camera, using a larger 1/2-inch sensor that OPPO says captures 70% more light than its predecessor.

AI Portrait Glow, AI Popout and AI Remix Collage bring the increasingly familiar layer of computational editing into the camera experience, allowing users to manipulate images without moving immediately to third-party applications.

Five years of smoothness?

Underneath, the A7 Pro 5G is powered by the MediaTek Dimensity 6360 MAX, accompanied by OPPO’s NetworkBoost Chip S1 and AI LinkBoost 4.0.

A 4,300mm² Glacier VC Vapor Chamber handles cooling, while software-based resource and memory management is designed to maintain responsiveness as workloads increase.

Perhaps more interesting than outright performance figures is OPPO’s 5-Year Smoothness Protection. The company says the device has passed its five-year smoothness testing, reflecting a wider attempt to position performance around consistency rather than simply launch-day speed.

Naturally, five-year performance cannot be established during a conventional review period, but the emphasis itself is notable. Smartphone replacement cycles are lengthening, making sustained performance, battery health and software optimisation increasingly relevant purchasing considerations.

AMOLED keeps the experience contemporary

The front houses a 6.57-inch FHD+ AMOLED display with a 120Hz refresh rate, up to 1,400 nits of brightness and a 92.8% screen-to-body ratio.

The combination provides the fluid scrolling and vibrant presentation expected from a contemporary AMOLED smartphone, while complementing a device otherwise heavily focused on practical considerations.

Warranty coverage also extends across the GCC, Pakistan, India and Bangladesh, potentially useful for users who regularly travel between these markets.

The 8,000mAh battery will inevitably attract most of the initial attention, but the A7 Pro 5G becomes more interesting when viewed as a complete package.
OPPO is combining battery capacity with physical durability, thermal management, connectivity enhancements and longer-term performance optimisation. At the same time, it has avoided turning the device into something that visually resembles a specialist rugged phone.

BY: SRIJITH KN

A different definition of smartphone performance that may ultimately be the A7 Pro 5G’s more relevant proposition. Rather than asking how much faster a smartphone can become every year, OPPO is increasingly asking another question: how much longer can it remain useful?

Nearly every enterprise believes its AI agents are properly scoped. Only a third have actually made sure of it.

Today, new research from Cequence Security, the leader in application, API, and agentic AI protection, and Enterprise Management Associates (EMA) found that 94% of enterprise IT and security leaders are confident their AI agents do not have more access than they need, yet only 33% actually provision agents with least-privilege access. The remaining two-thirds run on broad standing permissions that are reviewed periodically, rarely reviewed, or never reviewed at all.

That gap between confidence and practice is already showing up in production, not a theoretical risk, but as incidents enterprises are living with right now. Among the organizations surveyed:

• 65% have experienced an AI agent take an action outside its intended scope, including 29% with measurable business impact, including data exposure, financial loss, operational disruption, or reputational damage. Another 36% caught a near-miss before it caused damage.

• Only 32% can detect and contain an out-of-scope agent action within minutes through automated means; 55% need hours and manual steps to respond.
• In approximately 4% of organizations surveyed, the first sign of trouble came from a customer or outside partner, not an internal system.

The findings point to one clear story. Governance has not kept pace with the speed of agentic AI deployment, and that gap is showing up at every stage of the agent lifecycle, from how agents are provisioned, to how their actions are authorized, to how they are decommissioned once a pilot ends. Other key findings from the report include:

Enterprises Have Moved Past the Pilot Stage

The scale of deployment makes the gap more urgent. 46% of organizations report they are already scaling agentic AI across multiple departments and production workflows, and 79% are running generative and agentic AI simultaneously. Further, more than 92% report an increase in AI and bot-driven traffic targeting customer-facing applications and APIs.

Authorization is Checked at the Wrong Time, Or Not At All

That governance gap extends to how access is enforced in the moment an agent acts. Only 34% of organizations evaluate an AI agent’s authorization at the moment it attempts a specific action. The majority rely on periodic policy reviews or standing permissions set once at provisioning and never revisited, meaning an agent’s access can quietly outlive the task it was originally granted for, and keep working long after anyone signed off on it.

Abandoned Pilots Are Leaving Live Credentials Behind

Additionally, there’s an increasing risk in how enterprises manage agents that don’t make it to production. 31% of agentic AI pilots have been paused indefinitely, discontinued, or abandoned. Many were real deployments with real system access and credentials that were never cleaned up. Every abandoned pilot with live credentials is exposure nobody is actively watching.

External Connectivity Carries the Same Risk

14% of organizations allow AI agents to connect to outside tools and data sources via the Model Context Protocol (MCP) without restriction. Among the majority who do limit those connections to an approved list, fewer than half, just 49%, have a dedicated team actively maintaining and auditing that list on a regular basis.

Christopher M. Steffen, CISSP, CISA, VP of Research at EMA, said: “This research shows enterprises have moved well past experimentation with agentic AI right into production, and governance has not kept pace with that shift. The gap isn’t a lack of awareness; most organizations have policies in place and express real confidence in them. The gap is between what’s written down and what’s enforced when an agent takes an action nobody approved. That disconnect shows up most clearly in how organizations authorize agent actions and monitor them once they’re live, and it’s the reason incidents are happening at a rate the industry hasn’t fully reckoned with.”

Shreyans Mehta, Co-founder and CTO at Cequence, said: “The number that jumped out to me is the 92% being confident in their governance frameworks. Confidence like that is a trap; it’s exactly why organizations stop looking for problems, stop investing in monitoring, and let authorization checks lapse until an incident forces the conversation. This is the exact blind spot Cequence is built to close, giving security teams real-time visibility into what AI agents are actually doing and enforcing authorization at the moment an agent acts, not after the fact.”