Alteryx, the agentic analytics and automation company, today announced new AI capabilities across Alteryx One that connect enterprise-grade business logic directly to the AI agents’ teams already use. By extending governed workflows and datasets to external AI tools, organizations can “build once and govern once,” eliminating the need to recreate complex business logic from scratch. This approach allows enterprises to scale AI action with confidence, helping to reduce both security risks and runaway token costs.

Key Findings

• 71 percent of IT leaders report that AI initiatives are most successful when IT and business teams collaborate closely to bridge the gap between AI agents and enterprise business logic.
• NextWave achieved a 20x reduction in LLM token consumption using an Alteryx workflow during a complex Office of the CFO reconciliation between front-office and back-office data.
• Up to 93 percent reduction in token consumption and up to 85 percent increase in speed on tasks involving raw, ungrounded data, when combining an LLM with an existing, trusted Alteryx workflow.
• Up to 83 percent reduction in token costs and up to 65 percent increase in speed on tasks involving clean, grounded data.
• 65 percent of analysts confirm that AI delivers the most value when business logic is managed at the business level.

“Generative AI is brilliant at brainstorming, but it often struggles with the precision required for enterprise execution. Organizations don’t need agents that guess at business rules and burn through tokens; they need AI that operates on the same trusted business logic and governance that underpin the rest of the business,” said Ben Canning, Chief Product Officer at Alteryx. “By connecting existing tools to a governed business logic layer, we are allowing enterprises to stop the ‘re-work’ tax of rebuilding business rules for every new agent, ensuring that every AI-driven action is as reliable as the calculations they already trust.”

New Capabilities

The latest capabilities include:

Ask Alteryx: With this release, Ask Alteryx evolves from an embedded assistant into the primary way users interact with Alteryx One, guiding new users step-by-step through their first workflow in Designer and giving everyone a natural-language front door to their data through Ask Alteryx for Live Query, with connections to Snowflake, Big Query, and Databricks for reading and writing data directly. Ask Alteryx checks existing workflows and data first, delivering a governed answer when one exists or building a new workflow when it doesn’t, with every output remaining inspectable, editable, reusable, and schedulable within Alteryx One.

Agent Studio: Enables business users to turn existing, already-governed datasets into conversational agents without rebuilding anything. Analytics teams maintain full control over which datasets and KPIs power agent responses, while finance and operations departments can instantly scope an agent to their reconciliation dataset or KPI dashboard data, so stakeholders can ask trend, root-cause, and variance questions in plain language and get governed, explainable answers back.

Alteryx Insights for OpenAI: Available through the ChatGPT Plugin Directory, this capability allows business users to generate answers based on analyst-approved data, calculations, and workflows. Employees can investigate revenue variances or resolve reconciliation issues directly, accessing trusted business logic without opening the platform or requiring an Alteryx seat. Alteryx will be expanding this surface integration strategy to bring governed logic to where teams already collaborate, including upcoming support for Claude, Gemini, Slack, and Microsoft Teams.

Alteryx MCP Server: The governed way to use Alteryx from whatever AI platform or agent you already work in. It lets AI agents interact with Alteryx as easily as a human would, finding the right data, building multi-step solutions, and turning them into governed, repeatable workflows. Agents can build, run, schedule, and discover Alteryx assets directly, with external AI requests inheriting Alteryx’s authentication, workspace context, role-based access controls, and permissions automatically, so every interaction carries the same security model and audit trail as if a person had done it. More capabilities, including connection creation and expanded scheduling, are expected to roll out later this year on the same governed connection.

Alteryx Skills: A GitHub install that teaches third-party agentic interfaces, OpenAI Codex, Microsoft Copilot, Claude Code, Gemini CLI, and more, how to build Alteryx assets the right way, closer to how Ask Alteryx already builds them. Rather than each tool guessing at Alteryx’s patterns on its own, Skills gives them Ask Alteryx’s own workflow-building know-how, so a financial calculation or reconciliation workflow gets built correctly the first time, without rebuilding logic or permissions separately for every tool your team uses.

Cloudera, the only company bringing AI to data anywhere, and Mistral, a global frontier AI lab, today announced a landmark strategic partnership to bring secure, sovereign AI directly to enterprise data wherever it resides.

Cloudera and Mistral will combine Cloudera’s hybrid data and AI platform with Mistral’s sovereign AI models to give enterprises a secure path to build, customize, and run AI using their own private data. The collaboration will enable organizations to bring intelligence directly to their data across cloud, on-premises, edge, sovereign, and air-gapped environments—without requiring sensitive information to leave their control.

For enterprises managing the world’s largest and most sensitive data estates, this approach provides greater control, choice, and flexibility over how and where AI runs. Organizations can run inference privately, customize AI using proprietary data, and deploy AI applications within their existing security and governance boundaries, while gaining greater control over the economics of AI at scale.

“Enterprise AI is entering a new phase where organizations need more than access to powerful models, they need the freedom to unlock specialized intelligence using their data, on their terms,” said Abhas Ricky, Chief Business Officer & GM, Applied AI at Cloudera. “Together with Mistral, we are giving enterprises the ability to run AI where their data lives, customize it with their own intellectual property,  and maintain control over their data, infrastructure, and economics. That combination of intelligence and control is essential to moving AI from experimentation into production.”

Bringing Intelligence to the Data

For enterprises operating in highly regulated and data-intensive industries, moving sensitive or proprietary information to external AI services can introduce regulatory, security, and operational challenges.

Cloudera and Mistral are addressing these challenges by bringing secure, sovereign AI directly to the enterprise data perimeter.

Mistral’s suite of frontier models and tools will be integrated with Cloudera’s hybrid data and AI platform, enabling customers to run their own custom AI models and applications across 30 exabytes of data. Enterprises will have the deployment flexibility across public, private, on-premises, and fully air-gapped environments, while maintaining consistent governance and control over their context.

The partnership will span Mistral’s broad portfolio of frontier AI models and tools, including reasoning, chat, coding, document intelligence, and voice, giving Cloudera customers greater choice in how they apply AI to their enterprise data.

By enabling organizations to run inference within their own environments, the collaboration also gives customers greater flexibility over the economics of AI. Enterprises can choose the deployment model and infrastructure that best fit their workloads rather than depending exclusively on public API consumption models as AI usage scales.

From Private Inference to Proprietary Intelligence

Through the integration of Mistral Forge, a system for enterprises to build frontier-grade AI models grounded in their proprietary knowledge, organizations utilizing Cloudera’s platform will be able to customize and train models against large volumes of private enterprise data within controlled environments. For enterprises with petabytes of proprietary information, this creates an opportunity to transform decades of institutional data and domain expertise into differentiated AI while maintaining ownership and sovereignty over both the data and resulting intelligence.

Developers and practitioners will also be able to securely build AI-powered experiences using private enterprise data within local environments—from conversational access to governed data to AI-assisted software development and agentic workflows—without exposing sensitive business context or intellectual property to external environments.

Cloudera and Mistral also intend to collaborate on the next generation of AI at the edge, bringing increasingly capable inference closer to where enterprise data is created. This will enable organizations to explore intelligent applications across disconnected, latency-sensitive and resource-constrained environments where relying on centralized cloud infrastructure is not practical.

“Our mission is to make frontier AI available to enterprises without requiring them to give up control over their data, infrastructure, or intellectual property,” said Kamal Brar,  SVP of Partnerships and Alliances at Mistral. “Cloudera manages some of the world’s most valuable enterprise data estates, making this partnership a powerful opportunity to bring our technology directly to where that data lives. Together, we can give customers the ability to build AI that reflects their own data and expertise and deploy it securely wherever their business requires.”

Expanding Choice for Enterprise AI

The partnership also expands the Cloudera Enterprise AI Ecosystem, through which Cloudera works with leading AI models, infrastructure, application, and technology providers to give customers flexibility in how they build and deploy enterprise AI.

Mistral adds a significant new dimension to that ecosystem by enabling customers to access and customize its models and AI capabilities directly alongside their governed enterprise data. The partnership reinforces both Cloudera’s and Mistral’s commitment to an open approach to enterprise AI, giving customers choice across models, infrastructure, and deployment environments rather than locking their data or AI strategy into a single technology stack.

The joint Cloudera and Mistral solutions will be available through Cloudera’s enterprise sales team and partner ecosystem, with additional integrations and capabilities rolling out over time.

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.”

Article by Prasanna Rajendran, Vice President – EMEA, Kissflow

Across the Middle East, governments and enterprises are investing heavily in cloud infrastructure to support national digitization agendas, from Vision 2030 in Saudi Arabia to the UAE’s push toward AI-driven government services. Gartner forecasts that IT spending across the Middle East and North Africa will reach $169 billion in 2026, an 8.9 percent increase over 2025, with software spending alone growing 13.9 percent.

Infrastructure as code (IaC) is the practice of defining and provisioning computing infrastructure, including servers, networks, databases, and load balancers, using machine-readable configuration files rather than manual processes or interactive consoles. Rather than logging into a console to click through setup wizards, teams describe their entire infrastructure in version-controlled code that can be reviewed, tested, and deployed like any other software artifact.

For CIOs and IT leaders, this matters because IaC has become the operational standard for any organization running workloads at scale. Grand View Research valued the global IaC market at $1.2 billion in 2025 and projects it to reach $6.1 billion by 2033, a compound annual growth rate of 22.3 percent. That trajectory reflects a clear shift: enterprises are moving from manual, ticket-driven infrastructure management to automated, code-driven provisioning.

What is infrastructure as code?

At its core, IaC means defining resources such as virtual machines, storage volumes, network configurations, security policies, and access controls in declarative or imperative code files. Those files become the authoritative record of what your infrastructure looks like at any moment.

IaC generally follows one of two approaches, depending on whether teams want to define an outcome or prescribe the route to it. Declarative IaC describes the desired end state: you specify what you want, such as three servers, a load balancer, and a database cluster, and the tool works out how to get there. Terraform, AWS CloudFormation, and Azure Bicep all use this method. Imperative IaC instead specifies the exact steps to reach an outcome. You write procedural instructions: create this server, then attach this disk, then configure this network. Ansible and Chef follow that model more closely.

The declarative approach dominates enterprise adoption today because it is easier to maintain and less error-prone. You describe the outcome rather than the procedure, which keeps the code readable even as infrastructure complexity grows.

What separates IaC from traditional infrastructure management is version control. Every change is tracked in Git, reviewed through pull requests, and deployed through automated pipelines. This is the mechanism Gartner points to when it describes IaC as the route to cloud governance and self-service at scale.

Why infrastructure as code matters for enterprise IT

Manual infrastructure management does not scale. When an operations team provisions servers through tickets and console clicks, every environment differs slightly, every deployment carries risk, and every audit turns painful. IaC removes these problems systematically.

Consistency and reproducibility

IaC guarantees that the development, staging, and production environments are consistent. Configuration drift, the slow divergence of environments over time, disappears because every deployment is generated from the same code. When an incident occurs, you can rebuild an environment from scratch in minutes.

Speed and agility

Organizations using IaC provision entire environments in minutes rather than weeks. When business conditions change, whether through a product launch, a capacity spike, or a compliance deadline, IaC lets you respond at the speed of code.

Security and compliance

With IaC, security policies are embedded directly in infrastructure templates. Guardrails apply automatically. Compliance checks run in the CI/CD pipeline before any change reaches production. Security stops being a gate at the end of the process and becomes part of how infrastructure gets built.

Cost efficiency

IaC gives you precise control over resource provisioning. Idle capacity gets identified and decommissioned through code rather than through quarterly manual audits. Cost discipline has grown into a standing function for this reason: 59 percent of the 759 organizations Flexera surveyed for its 2025 State of the Cloud Report now run a dedicated FinOps team, up from 51 percent the year before.

Key infrastructure as code tools for the enterprise

Several tools now anchor enterprise IaC strategy, each suited to a different environment. Terraform and its open-source fork, OpenTofu, remain the dominant choice for cross-cloud work, offering declarative provisioning across multiple clouds using HCL. Organizations standardized on a single cloud often turn to native alternatives instead: AWS CloudFormation for AWS-centric environments, using JSON or YAML, and Azure Bicep for Azure-native deployments. Ansible takes an imperative, YAML-based approach and excels at configuration management and application deployment rather than pure provisioning. Pulumi appeals to developer-led teams by letting them define declarative infrastructure in familiar languages such as Python, TypeScript, or Go.

Common challenges when adopting infrastructure as code

Adopting IaC is not without friction. The most immediate obstacle is usually a skills gap, because IaC asks infrastructure teams to work the way developers do, with version control, code reviews, and CI/CD pipelines. That shift is cultural as much as it is technical, and it requires deliberate investment in training.

State management adds complexity of its own. Declarative tools maintain state files that track current infrastructure, and multi-team environments need remote state backends, locking, and workspace isolation from day one to avoid conflicts.

Legacy system integration is another common obstacle, since not everything can be expressed in code immediately. Most organizations start with new cloud workloads and progressively extend IaC to existing systems through API wrappers.

Governance and drift detection require ongoing discipline. IaC only delivers its full value once it becomes the sole path for infrastructure changes, which makes continuous drift detection and sustained cultural enforcement critical rather than optional.

Where workflow automation fits in an IaC-driven enterprise

Infrastructure as code solves the provisioning problem. Enterprise IT complexity does not stop there. The layer above IaC, covering the processes, approvals, and operational logic that run on top of provisioned infrastructure, is where most organizations still depend on fragmented tools, manual handoffs, and spreadsheet-based tracking. That gap is especially visible across the Middle East, where cloud adoption and ambitious national targets often outpace the operational processes needed to govern them.

Regulatory pressure widens the gap further. Gartner forecasts worldwide sovereign cloud IaaS spending at $80 billion in 2026, a 35.6 percent rise over 2025, with governments as the main buyers. Provisioning infrastructure inside a national boundary is one requirement. Proving that every approval, exception, and access grant on that infrastructure followed a governed path is another, and code alone does not answer it.

This is where workflow automation platforms operate as a digital backbone for enterprise operations. IaC automates the infrastructure layer. A no-code or low-code workflow platform automates the process layer: IT service requests, change management approvals, vendor onboarding, compliance workflows, and the hundreds of cross-functional processes that connect people, systems, and decisions across the enterprise.

For IT leaders across the region pursuing IaC adoption, particularly those operating under strict data residency and regulatory requirements, this kind of platform complements the strategy by giving business teams a way to build and manage operational workflows without adding to the IT backlog. IaC handles your infrastructure. Workflow automation handles everything that runs on it.

See how Kissflow governs the change approvals, access requests, and compliance workflows that sit on top of your cloud infrastructure in a 30-minute demo.

The company’s discussions at GISEC will center on Autonomous SOC with Governed AI, Agentic AI, Actionable Threat Intelligence and Unified Security Data Lake capabilities that are changing the manner in which security teams investigate threats, manage workflows and make decisions.

Samer Jadallah, Vice President, Middle East & Africa at Anomali, will represent the company at GISEC and will focus on the growing impact of AI on both attackers and defenders, emerging shifts in the threat landscape, including the need to counter CEO impersonation attacks as well as key challenges facing modern security teams. He will also highlight AI’s role is helping organizations respond more effectively to evolving threats, Anomali’s commitment to the region and ongoing product innovation and plans to expand adoption of the Anomali platform across global enterprises and government organizations.

A key focus at this year’s event will be the changing nature of cyberattacks. As threat actors promptly adopt AI to scale campaigns and further accelerate attacks, security operations centers (SOC) are under growing pressure to process rising volumes of alerts with limited resources. To help with this, Anomali will demonstrate how AI can support analysts in multiple ways like surfacing higher- value insights, reducing manual effort and enabling quicker, informed responses.

Visitors can find Anomali at Booth E156 and Booth A80.

• Event: GISEC Global 2026
• Booths: E156 and A80
• Location: Dubai Exhibition Centre (DEC), Expo City
• Dates: 16 th to 18 September 2026
• Time: 9:00 am to 5:00 pm GST

HONOR, a global leading AI device ecosystem company, concluded its participation at LEAP 2026, bringing its “ALPHA PLAN: From Vision to Reality” to life through the world’s first Robot Phone, advancements in robotics, cinematic imaging and connected AI experiences. The participation also marked HONOR’s renewed “Dare to Be” brand direction reflecting its continued transformation from a company predominantly known for smartphones into a broader global AI device ecosystem company.

Commenting on the participation, Lei Kunming, CMO HONOR Middle East & Africa said, “LEAP 2026 showed how HONOR’s ALPHA PLAN is moving from vision to reality. From the world’s first Robot Phone and Robotics to our connected ecosystem, we demonstrated how AI can work more naturally around people.” He added “Our ‘Dare to Be’ direction gives that transformation a stronger identity, with Saudi Arabia remaining central to our growth in AI and digital innovation.”

Bringing the HONOR Booth Experience to Life

Visitors explored HONOR’s expanding ecosystem through interactive experiences spanning sports, gaming, smart office, learning and everyday scenarios.

The booth demonstrated how smartphones, PCs, tablets, wearables and other smart devices can work together to deliver more seamlessly, across work, entertainment, sports and health, reflecting the ALPHA PLAN’s ambition to create a more intelligent, connected and human-centric ecosystem around the user.

Introducing the World’s First Robot Phone

At the centre of HONOR’s showcase was the world’s first Robot Phone, a defining proof point of the ALPHA PLAN and a new category combining AI, robotics and cinematic imaging.

Its integrated robotic gimbal can move, track subjects and respond to its surroundings, enabling more intuitive and proactive content creation. LEAP also marked the Robot Phone’s reveal for the Middle East and Africa, making the region the first overseas market to experience the new category.

HONOR also highlighted its collaboration with ARRI, bringing more than 100 years of professional cinema and image science expertise together with HONOR’s AI and robotics capabilities to make cinematic content creation more accessible to everyday users.

Advancing a Future Built on Human and AI Collaboration

Beyond the Robot Phone, HONOR showcased its ambitions in robotics through HONOR Robotics D1, reinforcing its vision of a future shaped not by humans competing against AI, but by humans working with AI to achieve more. The company also explored the future of AI devices and robotics through its LEAP keynote and panel discussions.

Entering a New “Dare to Be” Era

Reflecting this evolution, HONOR’s renewed“Dare to Be” direction reflects its evolution beyond individual devices towards an open AI ecosystem connecting technologies, industries and lifestyles. Supported by innovations including Agentic OS and AiMAGE, HONOR’s journey progresses from an intelligent phone to an intelligent ecosystem and, ultimately, an intelligent world.

Driving Growth Across MEA

HONOR entered LEAP with strong regional momentum. In H1 2026, the company grew by 35% while the Middle East smartphone market declined by 13%, making HONOR the only leading smartphone brand in the region to grow and the second-largest smartphone brand in the Middle East. This followed 73% year-on-year growth in Q1 2026.

Within this regional growth, Saudi Arabia remains a particularly important market for HONOR, supporting the company’s broader ambitions across AI devices and its connected ecosystem.

Strengthening HONOR’s AIoT Presence in Saudi Arabia

As part of this momentum, Saudi Arabia remains a strategic market for HONOR and an important part of the ALPHA PLAN. The Kingdom is now HONOR’s number one tablet market by share, while its AIoT business has grown by more than 500% and HONOR Earbuds Clip has surpassed 100,000 units sold locally.

Building on this strong foundation, HONOR continues to strengthen its presence through retail, after-sales, local partnerships and deeper engagement with the Kingdom’s technology ecosystem.

greytHR, a leading full-suite Human Resource Management System (HRMS) platform, launched greytHR NAVOS across the Middle East region, bringing agentic AI competencies directly into its platform. The new capability enables HR teams  to find information, navigate workflows, while completing authorised HR activities through simple, natural-language commands.

The Middle East rollout comes as agentic AI begins to reshape HR functions globally. According to McKinsey’s August 2026 research on the agentic era of HR, roughly two-thirds of today’s HR activities, including recruitment, payroll, onboarding and benefits administration, could be largely automated by 2030.

Unlike conventional AI assistants that primarily focus on answering questions, greytHR NAVOS follows an execution-first approach, helping users move from finding information to initiating and completing supported HR actions. Its three core functions – Navigate, Operate, and Support – enable information and workflow discovery, authorised action execution and contextual guidance.

Girish Rowjee, Co-founder & CEO of greytHR, said: “We have evolved from a system that you work on, to a platform that works with you. As a full-suite HRMS, greytHR already connects the entire hire-to-retire lifecycle across key HR operations. With the launch of greytHR NAVOS, we are adding an agentic AI layer across this connected ecosystem, enabling users to manage everyday HR activities through conversational commands rather than searching through multiple menus or disconnected tools. Furthermore, greytHR NAVOS works within each organisation’s existing security and access controls, reducing administrative friction and accelerating everyday HR execution.”

greytHR NAVOS supports a wide range of workflows across core HR, leave and attendance, workforce management, performance management, recruitment, platform navigation and support. HR teams can use the capability to update employee information, generate and publish letters, access attendance records, manage leave and regularisations, support performance reviews, search for job openings and candidates, handle recruitment approvals and generate hiring content.

Based on early greytHR NAVOS usage in comparable markets,  HR administrators have reported productivity savings of approximately five hours per week by replacing repetitive manual workflows with conversational requests. In one practical example, an attendance-muster export that previously required around five separate steps can be completed through a natural-language prompt in approximately 10 seconds. By simplifying routine processes and reducing time spent on software interfaces, greytHR NAVOS enables HR professionals to devote more time to employee engagement, workforce planning and strategic decision-making.

greytHR NAVOS operates within each organisation’s existing role-based access control (RBAC) system, ensuring that access to employee information and platform actions remains aligned with individual user roles and permissions. Moreover, sensitive or irreversible actions require explicit human confirmation before execution. The platform also maintains a traceable audit record of AI-assisted activity, including the initiating user, timestamp, prompt and action details. Meanwhile, greytHR’s established privacy, security, and governance framework extends to greytHR NAVOS, including alignment with SOC 2 Type II, ISO 27001:2022, and GDPR frameworks, supported by human oversight.

The new capability is available across all paid greytHR plans, including essential, growth, and premium, with no per-seat AI fees, usage caps or separate purchase required.

The launch showcases the evolution of HRMS platforms from traditional systems of record towards systems of action, where technology can understand user intent, identify the relevant workflow and support the execution of the next authorised step.

greytHR currently supports over 34,000 organisations and 3.5 million users across over 30 countries, providing a strong foundation for expanding its agentic AI capabilities across the Middle East region. With greytHR NAVOS, the company is helping regional businesses simplify HR administration and narrow the gap between intent and execution.

The greatest challenge of our time is not merely that people believe false information. It is that the very boundary between truth and opinion, fact and emotion, credibility and visibility, has become increasingly vague.

This shift signals a transformation in symbolic authority itself. Trust has not simply declined – it has been displaced. Traditional institutions no longer monopolize credibility, while digital platforms have multiplied voices without necessarily strengthening legitimacy.

In societies such as the UAE – built on coexistence, institutional trust, and the delicate management of cultural diversity, this challenge carries particular strategic weight. This is where digital citizenship ceases to be an educational slogan and becomes a matter of national importance.

Beyond Media Literacy

At its core, digital citizenship is a contemporary form of civic responsibility. It deals with how individuals participate in the digital public sphere, how they interpret information, and how they contribute – consciously or unconsciously, to the production of collective trust.

⁽¹⁾As Manuel Castells once stated, power in network societies increasingly operates through control over communication flows. The question is no longer simply who speaks, but whose voice becomes visible, amplified, and believed.

Trust as Social Infrastructure

In the UAE, misinformation is not merely a media concern – it is also a matter of social architecture. The country’s model of stability rests on institutional credibility, intercultural coexistence, and high levels of public trust.

This explains why the UAE has invested heavily, not only in digital transformation, but also in institutional clarity and communication governance. ⁽²⁾ Federal Decree-Law No. 34 of 2021 on combating rumours and cybercrime reflects an important principle: digital stability is inseparable from social stability. The objective is not merely punitive regulation, but the protection of public confidence itself.

Youth, Families and the Transformation of Authority

Young people are not passive consumers of information; they are producers of narratives, identity, legitimacy, and influence. They shape public conversations long before institutions respond to them.

In previous generations, legitimacy flowed vertically: from institutions, schools, family structures, and recognised expertise. Today, authority is increasingly negotiated horizontally- through peers, influencers, networks, and algorithmic visibility.

In addition, families act as the first school of civic trust. Long before formal media literacy programs, individuals learn how to relate to truth, disagreement, and legitimacy inside the home.

Why Social Sciences Matter

The response to misinformation cannot be reduced to fact-checking mechanisms or technical media literacy alone. What is required is a deeper intellectual infrastructure – one that social sciences are uniquely positioned to provide.

Sociology, communication studies, political science, and anthropology do not merely teach individuals how to verify information; they teach them how power operates, how legitimacy is constructed, how public opinion is shaped, and how collective trust is sustained or eroded.

A National Priority

The UAE has positioned itself as a global leader in artificial intelligence, digital governance, and future-oriented policy. This ambition is both necessary and admirable.

In this scenario, digital citizenship is not a secondary educational concern. It is part of national security, social sustainability, and the long-term legitimacy of institutions.

The UAE is not only managing digital transformation; it is helping to define what responsible digital modernity should look like.

Because in the end, the future of social cohesion will not be decided by technology itself, but by who is trusted to interpret reality in the digital age.

Delinea, the identity security platform that governs what humans, machines and AI agents do once they have access, today announced its participation at GISEC Global 2026. Under the theme, “From Access to Action: Governing AI at Runtime,” the company will spotlight the growing need for organisations to govern the actions autonomous AI agents take after access has been granted.

Following its release of new runtime authorisation capabilities for AI agents this summer, Delinea will use GISEC to advance the conversation around runtime authorisation, an approach designed to enforce policy on an agent’s actions within a session, at the time those actions are executed.

As AI agents take on increasingly autonomous roles across databases, cloud environments, infrastructure and business systems, the security challenge is shifting beyond authentication. While an agent may have legitimate access to a system, it can still take an action that exceeds its intended task or creates operational risk. Delinea’s runtime authorisation capabilities enable organisations to apply policy at the point of action — allowing, blocking or escalating activity before it executes.

“AI adoption is moving the identity-security conversation beyond the moment of authentication,” said Mortada Ayad, VP Sales at Delinea. “The question for organisations is no longer simply whether an AI agent can access a critical system. It is whether they can govern what that agent does once it is inside. At GISEC, we want to put that challenge, and the need for runtime control, at the centre of the regional conversation.”

Delinea has strategically adopted a channel-led presence at this year’s show, reflecting its focus on bringing these capabilities to market through the regional partners enterprises rely on to deploy, manage and operationalise complex security programmes. Through its presence at the stands of regional distributor, Shifra, and integrator partner, Defa3, the company will deepen engagement with existing partners, recruit new partners and build the specialist capabilities needed to support secure AI adoption across the region.

The approach builds on the Delinea Partner Advantage Program, launched in July 2026. The program provides partners with clearer rules of engagement, deal protection, tiered incentives and AI-driven enablement, helping them build profitable identity-security practices, while delivering stronger outcomes for customers. It is designed to support selling partners today, with expanding roles planned for managed service providers, global systems integrators, advisory partners and technology partners.

“For a capability this consequential, technology alone is not enough,” Ayad added. “Customers need partners who understand their environment, their risk and how to translate security policy into practical control. Our focus at GISEC is to equip and grow that ecosystem, so organisations can move forward with AI, knowing that authorised access does not become unchecked action.”

Delinea’s runtime authorisation for AI agents enables organisations to govern activity across the systems agents use, including databases, SSH hosts, Kubernetes clusters and cloud consoles. Through just-in-time, task-scoped access, and real-time policy enforcement, organisations can reduce reliance on standing credentials, while maintaining a defensible record of AI-driven activity.

Enterprise AI has entered a new phase. The conversation is no longer centred on whether organisations should invest in artificial intelligence, but on how they can transform that investment into measurable business value.

By: Mohammed Hilili – General Manager, Lenovo Gulf

Across the GCC, enterprises are moving beyond experimentation. Pilot projects are giving way to enterprise-wide deployments as organisations seek to integrate AI into customer experiences, business operations, software development, cybersecurity and decision-making. Yet despite growing investment, many AI initiatives continue to struggle to deliver the outcomes leadership teams expect.

In my experience, the reason is rarely the technology itself. More often, organisations begin with the wrong conversation.

Too many AI discussions start with infrastructure specifications, GPU availability or the latest foundation models. These are undoubtedly important decisions, but they are not the first ones organisations should make.

The first question is much simpler.

What business problem are we trying to solve?

Without a clear answer, AI initiatives often remain isolated demonstrations of technical capability rather than platforms capable of delivering sustainable business value.

From AI Pilots to Enterprise Platforms

Across industries, organisations have spent the past two years experimenting with generative AI. Many have successfully launched departmental pilots that demonstrate what AI can achieve within a controlled environment. The greater challenge now lies in scaling those experiments across the enterprise.

That transition requires far more than additional computing power. It demands clear governance, high-quality data, well-defined business objectives and an architecture capable of supporting continuous growth. Successful AI adoption is increasingly becoming an organisational transformation exercise rather than simply another technology deployment.

Business Strategy Before Infrastructure

I recently worked with a leading regional financial institution looking to strengthen its research and development capabilities through AI. The ambition was clear, but many practical questions remained unanswered.

How much computing capacity would the organisation require? Which GPU architecture would support both current and future workloads? How could the environment remain scalable as AI adoption expanded across the business?

These may appear to be technology questions. In reality, they are strategic business decisions with long-term operational consequences.

Instead of beginning with hardware selection, we started by understanding the organisation’s objectives. Together with the leadership team, we assessed AI readiness, identified priority business outcomes and defined what success would look like before discussing infrastructure.

Only after establishing that foundation did we determine the appropriate compute resources, architectural approach and deployment model required to support long-term growth.

The result was not simply a successful implementation but an AI platform capable of evolving alongside the organisation’s ambitions.

AI Readiness Extends Beyond Technology

Many organisations still view AI readiness primarily through the lens of infrastructure. In reality, readiness begins much earlier.

Leadership alignment, data quality, governance frameworks, cybersecurity, skills development and measurable business outcomes all influence whether an AI initiative succeeds or stalls. Infrastructure remains essential, but it should support strategy rather than define it.

The organisations achieving the strongest results are those treating AI as a long-term business capability rather than a series of disconnected technology projects.

Building for a Hybrid AI Future

Enterprise AI environments are also becoming increasingly hybrid. Certain workloads will remain on-premises to address latency, compliance or data sovereignty requirements, while others will leverage the scalability of public cloud environments.

This makes architectural flexibility increasingly important. Organisations need infrastructure strategies capable of supporting multiple deployment models while allowing AI workloads to evolve alongside changing business priorities.

Selecting technology is therefore no longer simply about purchasing hardware. It is about building an adaptable foundation capable of supporting continuous innovation over many years.

The GCC Opportunity

The GCC is uniquely positioned to accelerate enterprise AI adoption. Governments across the region continue investing heavily in digital transformation, sovereign AI capabilities and next-generation cloud infrastructure while strengthening regulatory frameworks around data governance and cybersecurity.

These investments provide organisations with an increasingly mature environment in which to deploy AI at scale. However, long-term success will depend less on access to technology than on the ability to align AI investments with clear operational priorities and measurable business outcomes.

As AI becomes embedded within core enterprise operations, leadership decisions made today will determine competitive advantage for years to come.

Why Clarity Still Comes Before Compute

Technology will continue evolving at remarkable speed. New AI models, specialised processors and deployment approaches will continue reshaping the enterprise landscape.

What will remain constant is the importance of making the right decisions before investing.

At Lenovo, this philosophy shapes how we work with customers. We believe AI is not simply a product to deploy, but an organisational capability that develops over time. By combining advisory expertise with infrastructure, lifecycle services and long-term planning, organisations can reduce uncertainty, optimise investment and build AI platforms that continue creating value as business needs evolve.

The organisations that lead in the AI era will not necessarily be those with the largest AI budgets or the most powerful infrastructure. They will be those that begin with business clarity, build the right foundations and scale with purpose.

Because in enterprise AI, infrastructure enables transformation—but clarity makes it possible.