Digital transformation may sound like a buzzword, but it has become a business imperative. Across industries, companies are under pressure to improve efficiency, scale effectively, meet rising customer expectations and keep pace with evolving regulations—all of which demand leading-edge technology.

Yet urgency alone isn’t enough. Without cultural alignment, strong governance and a clear purpose, technology upgrades rarely deliver lasting results. Below, members of 

 share strategies and policies that can help organizations turn digital transformation into measurable business impact.

1. Align Technology Change With Cultural Change

One key factor is aligning technology change with cultural change. When leaders invest in clear communication, reskilling and stakeholder buy-in alongside new tools, digital transformation moves from being an IT project to an organizational success. - 

Digital transformation is urgent, but starting points are often unclear. Whether it’s the leadership team (CEO, CFO or CTO), deadlines, regulations or external market forces, something’s always altering plans. However, quick investments in AI for automation efficiency or fraud detection can save millions. During ERP rollouts, infrastructure modernization ensures continuity, justifying the investment despite external factors. - 

One key factor is making data the core of the transformation—not just software or infrastructure. Success comes when teams invest in foundational data work: defining schema, ontologies, metadata, features and labels. This makes downstream automation, AI and decision workflows truly scalable and adaptive. - 

4. Use AI At The Edge For Transparency And Control

AI is the ultimate digital transformation. AI at the edge isn’t just smart—it’s empowering. With real-time insights and remote control, industries gain visibility, agility and confidence like never before. AI transparency moves us from “what you don’t know can hurt you” to “what you understand can empower you.” AI at the edge provides greater transparency and control of remote sites. - 

5. Align Technology With Operational Needs

In my experience, digital transformation succeeds when technology serves a clear operational need. For example, in high-speed digital semiconductor testing, aligning engineering and operations early was critical. Cross-functional teams ensured new tools delivered real performance gains (increased throughput, higher yields and improved quality), and that drove lasting impact across the organization. - 

6. Build Trust Through Collaborative Development

The key factor for us is always collaborative testing, joint application development sessions with the various lines of business, and understanding upstream and downstream impacts that the automation brings. The successful outcomes of our latest digital transformation project had a lot to do with trust in delivering quality and in each other as a unified team. - 

Start with what customers truly need, not what you offer. Appreciate the plurality of their needs and segment them into the top three priorities. Focus on delivering outcomes that help them succeed and democratize access to value. - 

Digital transformation cannot be successful unless it’s focused on the user. Focusing on user experience, getting iterative feedback and improving are the most critical strategies in all digital transformation successes. - 

9. Secure Commitment From Executive Leadership

Serious commitment from executive leadership is essential. Successful digital transformation requires a clear and compelling vision, as well as adequate resources to drive the change needed to achieve measurable outcomes over time. - 

10. View Transformation As A Strategic Shift

One key factor in successful digital transformation is viewing it as a strategic shift, not just a tech upgrade. In the travel space, we’ve seen the biggest wins when companies combine AI and automation with real changes in how frontline teams work—that is, when teams are empowered to make faster, more confident decisions (even under pressure) and when complex, repetitive tasks are automated. - 

11. Engage Stakeholders With A Clear End Goal

Start with the motivating purpose and target end goal to engage all stakeholders who will be affected by, and can help enable, the transformation. A systems approach combined with clear communication focused on the end goal ensures coordinated, collective change management. - 

12. Fuel Change With A Genuine Desire To Improve

The biggest driver of digital transformation is a genuine desire for change. When people feel the status quo is no longer acceptable, that motive power fuels vision, capabilities and roadmaps—and creates not just growth, but also meaning in work. I have come to believe transformations launched by mandate stall; those fueled by desire endure. - 

Remember the Pareto Principle. Here’s digital transformation’s 80/20 truth: Success isn’t about 100% tech adoption, but 80% of impact coming from 20% of strategic changes. Empower the 20% of change-makers and prioritize the 20% of initiatives that truly move the needle. It’s about smart focus, not exhaustive effort. - 

In every successful digital transformation I have seen, the real differentiator was designing systems for adaptability instead of just delivery. When platforms are modular and governance-driven, they evolve, turning transformation into a lasting capability rather than a one-time project. - 

15. Establish A Governance Framework From The Outset

One key factor in delivering a successful digital transformation is establishing a clear data and technology governance framework from the outset. Transformation initiatives often fail not because of the technology itself, but because of fragmented ownership, unclear accountability and lack of trust in data. - 

Garbage in, garbage out. I’ve seen that successful digital transformation really depends on starting with clean, reliable data and a clear view of your goals. You can have the best algorithms in the world, but if you’re feeding them junk, you’ll just make bad decisions at scale. Clean up the data first, then choose the tool that meets your needs. The sequence matters. - 

Quick wins are essential. A clear vision is critical for any initiative, but great ideas die quickly in the face of daily demands in today’s lean organizational models. Maintaining focus for extended periods of time on any initiative is tough. Bite-sized, tangible successes achieved with a focused and protected team build momentum, energy and credibility, which in turn create more interest and engagement. - 

18. Connect Technology Outcomes To People Outcomes

Successful transformation happens when technology outcomes connect directly to people outcomes. I was part of a project in which redesigning data pipelines that served more than 7 million digital users not only saved $1.5 million in cloud costs, but also gave customers faster, more reliable experiences. Anchoring change in both efficiency and human impact is what makes transformation stick. - 

The most overlooked factor in digital transformation is building for complexity from day one. Too many initiatives fail because they stitch together tools that don’t scale. The most successful transformations I’ve seen have embedded automation and intelligence into core workflows—so systems don’t just digitize processes, they adapt and improve as the business evolves. - 

20. Align Data And AI Strategy With Business Priorities

Digital transformation can stall without clear priorities. Data and AI help align stakeholders by identifying friction points like invoice disputes or fulfillment delays and correlating them to root causes, such as complexity in product or pricing structures. The takeaway: Aligning your data and AI strategy with your broader digital strategy doesn’t just support transformation—it accelerates it. - 

 offer firsthand insights on tech & business.

This article was originally published on 

20 Keys To Driving Successful Digital Transformation

Proven Product Design Strategies Cybersecurity Teams Should Adopt

COUNCIL POST | Membership (fee-based)Jun 09, 2025, 08:15am EDTShareSave

Product design and cybersecurity may seem worlds apart, but they share a common goal: creating systems that people can trust, use intuitively and engage with seamlessly. Cybersecurity solutions that are too complex or obstructive often get bypassed, weakening the very defenses they aim to create.

 share design principles from the product world that cybersecurity leaders can adopt to strengthen security and reduce friction in their solutions. Follow their recommendations to build systems that users will actually embrace.

Approach cybersecurity like great product design—embed it from the start. In digital health and SaMD, security isn’t an afterthought; it’s foundational to user trust, regulatory compliance and patient safety. - 

Cybersecurity leaders can learn to empathize with users—a core product design principle. Security solutions should be user-friendly and seamlessly integrate into workflows, reducing friction and encouraging secure behavior. Usability drives adoption, making security effective without becoming a burden. - 

 is an invitation-only community for world-class CIOs, CTOs and technology executives. 

3. Avoid Rigid Controls And Complex Processes

In product design, usability is key; solutions are intuitive, accessible and aligned with how people naturally behave. Cybersecurity often errs by enforcing rigid controls or complex processes that frustrate users, leading them to bypass security measures altogether. - 

Great product managers remove every nonessential element from their products. Great cybersecurity leaders should strip every unneeded port, privilege and dependency they can. By favoring elegant, minimal configurations over sprawling rule sets, security teams cut the attack surface and maintenance toil while making audits far simpler. - 

If security controls are confusing or intrusive, users will find ways around them, weakening your defenses. Like product design, cybersecurity must focus on usability. Simplify authentication and data protection, test tools with real users and make the secure path the easiest one. Usable security is effective security. - 

Design security like great UX: invisible, intuitive and frictionless. The best product experiences remove barriers—cybersecurity should do the same. If controls slow users down, they’ll find workarounds. Bake protection into workflows so users stay secure without thinking about it. - 

7. Include Customers In The Development Process

Product design relies on its customer feedback loop. Cybersecurity leaders, if customers aren’t part of the development process, you’re flying blind. Building solutions with a customer-centric mindset shifts the focus from shipping fast to intentional design, ensuring tools are usable and effective. Users don’t reward the first to market. They reward the first to get it right. - 

In product design, we evangelize “low burden.” Cybersecurity leaders should do the same, especially when it comes to MFA. Adoption increases when security fits natural user flows (for example, biometrics over token fatigue). Like great UX, secure systems must reduce friction, anticipate behavior and encourage effortless compliance. - 

One key lesson is embracing user-centered design to reduce friction. When security workflows and solutions are designed around how people actually work—not how we wish they did—adoption goes up and risky workarounds go down. - 

The most successful products are often those that are simple and elegant to use. Of course, this doesn’t mean the product itself is simplistic. This principle can be co-opted in security through processes such as single-instance data storage in data lakes or other high-volume repositories, normalization of security data, and flexible analysis and visualization to support various stakeholders. - 

11. Make It Visible, Explainable And Easy To Do Right

Cybersecurity shouldn’t feel like a roadblock; it should feel like a well-designed product. Clarity drives behavior. If your controls confuse people, they’ll find a way around them. But if security feels natural, intuitive and purposeful, people lean in, not out. Design security like you’d design trust: visible, explainable and easy to do right the first time. - 

12. Aim For ‘Invisible Intuitiveness’

Cybersecurity leaders can learn from product design’s principle of “invisible intuitiveness”—crafting protections that feel seamless, frictionless and even delightful to use. Security shouldn’t just block threats; it should blend into workflows so elegantly that users embrace it as an enabler, not an obstacle, turning defense into experience design. - 

Product designers have one priority, which is to build a product that solves problems for users in a simple and intuitive way. Cybersecurity solutions are often complicated and confuse users. Cybersecurity leaders should adopt product designers’ mindset and create solutions that are simpler and more intuitive. Simply put, cybersecurity should be easy to learn and understand. - 

Cybersecurity leaders can borrow the lesson of design thinking from product design. Prioritize user empathy and create security solutions that are intuitive and user-centered, ensuring both strong protection and ease of use for better adoption and effectiveness. - 

Cybersecurity leaders can learn from product design the importance of prioritizing user experience and intuitive interfaces to encourage the adoption of security best practices. Making security tools and protocols user-friendly reduces friction and human error, ultimately strengthening the overall security posture by fostering a more security-conscious culture. - 

16. Design Around Human Behavior, Not Against It

Embrace user-centered security that designs around human behavior rather than against it. Like good product design, which prioritizes usability, effective security acknowledges that users will find workarounds for overly restrictive controls. By understanding user workflows and designing security that integrates naturally, leaders can achieve better protection through higher compliance and less friction. - 

17. Adopt A Continuous Product Design Mindset

Cybersecurity leaders should adopt a continuous product design mindset: Prioritize usability, measure performance metrics and iterate relentlessly. Designing for optimal end-to-end workflows across technology and organizational processes will go far beyond detection; it will improve the cybersecurity teams’ prioritization, decision-making and response. - 

Product design focuses on a great consumer experience. Providing higher security and ensuring usability don’t have to be at odds. Cybersecurity leaders have to ensure solutions are built with the principle of making them intuitive, low-friction and empowering. When users find security easy, they adopt it, transforming them from the “weakest link” into your strongest defense layer. - 

19. Borrow The Principle Of ‘Progressive Disclosure’

Cybersecurity leaders can borrow the product design principle of “progressive disclosure.” Instead of overwhelming users with alerts, options or jargon up front, reveal critical security prompts contextually and just in time. This builds trust, reduces fatigue and ensures users engage meaningfully when it matters most, making secure behavior both intuitive and impactful. - 

Cybersecurity can borrow iterative prototyping from product design, testing defenses like evolving products, not static walls. Instead of overengineering for edge cases, launch MVP protections, gather threat feedback and rapidly refine. This agile mindset helps security adapt as fast as attackers do. - 

originally published on Asia Tech Podcast

Learn How AI is Revolutionizing Business Decision-Making - EP 365 - Karen Kim

The modern supply chain is more than a cost center—it is a critical driver of competitive advantage. Yet, business leaders face a complex and volatile environment where geopolitical instability, supply disruptions and shifting consumer demands challenge traditional supply chain models. In fact, industry 

 suggest that 70% of supply chain professionals expect risks to escalate in the coming years.

Chief supply chain officers (CSCOs) must go beyond cost-cutting and logistics management. They need to design resilient supply networks, integrate real-time data for smarter decision-making and embrace intelligent technologies to navigate uncertainty. The challenge is twofold: first, balancing diversification and localization to reduce risk while maintaining efficiency; and second, overcoming data fragmentation and decision overload in an era where more data doesn’t always mean better insights.

This article explores three strategic levers—diversification, data integration and decision intelligence—that supply chain leaders should embrace to turn challenges into opportunities and build a future-ready supply chain.

1. Derisk complexity with diversification.

Businesses today must balance diversification and localization to manage risks and optimize operations. According to 

The Economist Impact Trade in Transition 2025

 report, businesses are balancing dual priorities of diversification and localization. In fact, nearly 46% of businesses are diversifying to enter new markets and hedge against disruptions, while 42% are localizing for reduced transportation costs and greater oversight. When it comes to choosing suppliers, 75% of businesses are spreading risk and increasing resilience across more partners.

Resilience can also be built by additional buffers of inventory or diversified materials to be used as inputs. Delivering enhanced customer experience could also mean customized products and channels.

, a leading electronics supplier, overly reliant on Asian manufacturing, faced shortfalls that disrupted operations. With most of its customers in the US, the company near-shored production, optimizing its supply chain. This move cut delivery times, lowered carbon emissions and reduced excess inventory, enhancing both efficiency and sustainability.

2. Integrate organizational capabilities with data.

, Gartner recommends that CSCOs consider five supply chain characteristics to deliver business value: agility, resilience, strategic alignment, regionalization and ecosystem collaboration. Achieving this requires investments in technology, sustainability, talent and business models to enhance customer satisfaction, revenue, and performance.

These investments align with four strategic paths:

 Innovates on business models and simplifies products.

 Uses advanced technologies to optimize supply chain tasks.

 Delays new capabilities to maintain stability and control costs.

 Focuses on sustainability and risk management for long-term resilience.

Technology is the key enabler, and data powers decision-making. CSCOs play a vital role in:

• Understanding supply chain data sources

• Designing feedback loops for smarter decision-making

A robust DataOps system in the supply chain refines MLOps for AI-driven operations, enabling probabilistic planning, real-time sustainability tracking and advanced analytics to balance trade-offs effectively.

, a multinational company with a significant number of vending machine outlets needed to manage brand assortment within the machines more efficiently. The company needed to balance its target sales volume and gain market share of brands. Another priority was to avoid “out of stocks” and “dead slots” within the vending machine.

Additionally, it was dealing with complexity within contractual obligations with different manufacturers. The company consolidated its data from vending machines to design a data model to get visibility on the hundreds of brand assortments and interactions between different data pools. Web-based technologies leveraged data in simulations and predicted outcomes. Benefits included getting insights into consumer buying preferences, developing targeted assortments and predicting logistic costs. Data integrated from vending machines thus helped with decision-making across different disciplines within the organization.

3. Leverage intelligence platforms for real-time decision-making.

While organizations invest in AI for supply chain efficiencies, they also need to plan for the explosion of 

, predicted to reach more than 2,000 zettabytes by 2035. One of the consequences of data overload is indecision paralysis. According to Oracle, more data means that business leaders face a 10-times increase in the number of decisions that need to be made, leading to alert fatigue, manual analysis and indecision, all contributing to operational inefficiency.

To address these issues, Gartner experts believe that supply chain planners will prioritize technologies such as decision intelligence platforms (DIPs) to deliver 

 and automated decision-making. DIPs are AI-driven systems that integrate data analytics, automation and predictive modelling to enhance decision-making. Unlike traditional business intelligence tools, DIPs enable supply chain leaders to simulate and anticipate disruptions and optimize operations dynamically.

, one techglomerate we worked with that has 44 business units across South East Asia, spanning a variety of industries from power generation, banking, food and land, is transforming its century-old traditional infrastructure to an AI-powered tech enterprise. The scale of change creates technical challenges of maintaining security and operations across legacy systems and new digital systems. Continuous monitoring of data movement, across the enterprise, identifying patterns and detecting anomalies is critical. An integrated monitoring system that flags potential security implications immediately is a must-have.

It's time to build a resilient, data-driven supply chain.

In a volatile landscape, supply chain leaders must go beyond cost-cutting to build resilient, data-driven operations. While diversification is a priority, true resilience requires balancing supplier networks, integrating data and leveraging AI-driven decision intelligence platforms to turn data overload into actionable insights.

The challenge is no longer just adapting to short-term shocks but embedding long-term resilience through smarter strategies and scalable technology adoption. Businesses that embrace this shift won’t just survive—they will thrive with a competitive edge.

Unlocking Supply Chain Value With Diversification, Data And More

According to the ‘2025 Top Strategic Technology Trends’ by Gartner, AI risk assessment will be a significant focus area for organizations due to the rise of Agentic AI (a class of AI that focuses on autonomous systems that can make decisions and perform tasks without human intervention), AI governance platforms and disinformation security.

For businesses, this means innovation is required for quicker data analysis and prediction intelligence, assessing privacy violations, guiding and tracking AI models through governance standards and looking out for fraud and phishing.

Now more than ever, businesses need to quickly overcome the challenges of data silos, inconsistent data modalities and integration issues that complicate data analysis and decision-making. Furthermore, the threat of data breaches and cyberattacks creates obstacles to data sharing and collaborative efforts among companies within the same industry.

These challenges are amplified within complex and interconnected digital supply chains. Enterprises are increasingly looking to AI to provide better intelligence that goes beyond incremental productivity gains.

So what constitutes better intelligence? Over years of working with enterprise customers, we at Human Managed have learned that intelligence that is particularly effective and trustable has three qualities – it is traceable, timely and fresh. However, to generate such intelligence consistently, a wide range of data needs to be processed, analyzed, applied to well-defined problems and distributed to the right channels in real time.

Enterprises are increasingly looking to AI to provide better intelligence that goes beyond incremental productivity gains.

How can AI lead the way in building better intelligence? Enter Federated Learning (FL) with privacy preservation techniques, a machine learning approach where multiple institutions (for example, banks, hospitals, telcos, insurers) can collaborate within their sector or across industries to train a shared model while keeping their data decentralized and secure. This approach is particularly appealing to industries such as the financial sector, where privacy and security are paramount.

So why would enterprises, especially in the Association of South-East Asian Nations (ASEAN), consider the route to collective intelligence? As our recently launched white paper, titled ‘Better Intelligence Is Collective: Unlocking The Potential Of AI With Federated Learning’ reveals, there are several reasons:

1. The demand for quality data is growing:

 Reusable, scalable and adaptable AI requires quality data and data privacy is business critical. At the same time, there is also a need to reduce large language model training costs and hence specialized technologies such as FL seem to check all the boxes.

2. Diverse application potential is attractive:

 A wide array of industries, including banking and financial services, healthcare, mobile applications and autonomous vehicles can benefit from FL and privacy preservation techniques. Successful applications for FL solutions have been used during the COVID-19 pandemic and for financial risk management and manufacturing. Academic research has further highlighted its potential for IoT, telecommunications and healthcare.

3. ASEAN digital economy is poised for growth:

 While FL is at a nascent stage in ASEAN, the application potential is strong. As one of the fastest-growing regions of the global economy, estimated to reach US$4.5 trillion by 2030, ASEAN’s digital economy could add up to US$2 trillion by 2030.

According to the ASEAN Digital Economy Framework Agreement (DEFA), the interconnectedness of the ASEAN ecosystem can deliver a multiplier effect across many layers. The priorities of the DEFA agreement highlight six significant opportunities: cross-border data flows and data protection; online safety and cybersecurity; digital ID and authentication; digital payments and e-invoicing; cross-border ecommerce; and digital trade.

What challenges, incentives and solutions could affect adoption of FL? While tech challenges can be overcome easily enough, participation incentives are key in encouraging enterprises to adopt FL. Additionally, modular technologies will be critical in scaling FL across diverse industries and regions in ASEAN.

1. Data and model convergence are key tech challenges:

 For the most part, industry players produce heterogeneous data and adopt heterogeneous models within their business context. A FL approach that attempts to converge and agree on standardized feature sets and model architecture could be costly with little buy-in.

2. Centralized agencies are best placed to drive adoption of FL in ASEAN:

 While the technology exists for FL to be actioned easily, bigger socio-economic challenges lie around participation incentives, centralized stewardship and regulatory know-how. The case for FL is strongest in highly regulated industries such as banking, insurance, telco and healthcare where government or quasi-government agencies can persuade and enable enterprises to work together.

3. Modular tech can help scale collective intelligence across ASEAN:

 One way of addressing communication, computation and data and model heterogeneity challenges is via modular architectural data platforms. At Human Managed, our Collective Intelligence platform, hm.works, delivers AI-native solutions for cyber, digital and risk problems for enterprises.

This platform is a modular collection of 14 functions and 92 microservices abstracted into infrastructure, software, data and AI stacks. It integrates data from any source and develops AI models for business context and specific use cases. Through FL and AI-powered apps, the HM collective intelligence platform can build a distributed intelligence sharing system for organizations.

Reusable, scalable and adaptable AI requires quality data and data privacy is business critical.

In conclusion, the journey to operationalizing AI is fraught with challenges, primarily revolving around the management and utilization of vast amounts of data. As organizations navigate data silos, inconsistent modalities, and integration issues, the importance of data privacy and protection becomes increasingly paramount.

The rise of FL offers a promising solution, enabling collaborative, privacy-preserving machine learning across industries. This approach not only addresses data privacy concerns but also enhances the quality of data and effectiveness of AI models.

As AI risk assessment becomes crucial for organizations, the need for better intelligence grows. Federated Learning can revolutionize industries by fostering secure, collaborative and privacy-preserving AI solutions.

How AI Can Lead the Way in Building Better Intelligence

With digital transformation accelerating across Asean, policymakers and business leaders face a dual challenge: unlocking the potential of the Digital Economic Framework Agreement while safeguarding against escalating cyber threats.

While several Asean-level cybersecurity cooperation initiatives have been launched, implementation remains a challenge due to slow progress in intelligence sharing between multiple stakeholders; both government agencies and enterprises. The key challenge is balancing privacy concerns and innovation while keeping up with evolving threats.

Here’s how emerging technologies such as decision intelligence platforms and federated learning can address these issues to promote collective intelligence across the region.

Collective cybersecurity requires collective intelligence

According to UNODC estimates, financial losses from scams targeting victims in East and South East Asia, ranged between US$18 billion and US$37 billion in 2023 alone. Industry reports suggest that the Asia Pacific region is most susceptible globally to phishing attacks targeting financial institutions.

Several initiatives have been launched by Asean states to tackle cybersecurity at a regional level, including the Asean Regional Computer Emergency Response Team and the Norms Implementation Checklist for ASEAN states, that translates the UN eleven norms of responsible state behavior in cyberspace into practical steps.

Additionally, the Asean Cybersecurity Cooperation Strategy 2026-30 is being developed, with the aim of enhancing regional cybersecurity measures, building on the foundations set by cooperation strategies of 2017-20 and 2020-25. Securing cross-border data flows, establishing robust data governance frameworks, such as the Asean Data Management Framework and Asean Model Contractual Clauses for ensuring data security, regulatory compliance, and seamless cross-border business operations, is seen as critical in the upcoming phases.

However, implementation remains the key challenge for these initiatives. To begin with, levels of national cybersecurity readiness, governance, accountability frameworks is at different levels across the region.

Crucially, experts believe that while cyber capabilities are expanding, military, intelligence and law enforcement agencies are not keen to discuss operations and strategies. Varying data protection laws and enforcement procedures raise concerns about data breaches, illegal access, and misuse of personal information.

Trust and collaboration hold the key to scaling cybersecurity efforts across the region. Just as continuous dialogus, knowledge sharing and capacity building nurtures confidence among stakeholders, technological infrastructure and use cases demonstrating data privacy and real-time analysis will encourage intelligence sharing.

Composite AI refers to the integration of multiple AI methodologies, including decision intelligence platforms (DIPs) for real-time decision automation, federated learning for privacy-preserving collaborative intelligence, and AI-driven threat modelling for cybersecurity risk assessment.

By leveraging these techniques, Asean members can strengthen cross-border threat detection while maintaining data sovereignty.

A DIP is the technical infrastructure that provides a real-time unified view of data from multiple sources and insights that are transformed into decisions and actions for specific outcomes.

Federated learning with privacy preservation techniques is a decentralised machine learning approach, enabling multiple organizations to collaboratively train algorithms without exchanging raw data. Each participant trains the model on their local data, and only the model updates are shared and aggregated to build a global model. This method preserves data privacy and complies with data sovereignty regulations.

Integrating decision intelligence for proactive cyber defence:

Decision intelligence can augment cybersecurity posture by:

: DIPs map out all assets across cloud platforms, track changes as they occur and maintain up-to-date organisational context to inform decisions.


 DIPs continuously monitor the cloud environment, providing early warnings and allowing teams to reroute or take preventive action before damage occurs.


: DIPs anticipate potential vulnerabilities by analysing historical patterns and emerging trends, shifting from reactive to proactive security management.


: DIPs combine data analytics and AI techniques to generate explicit and auditable decisions.


: DIPs evaluate outcomes from decisions and actions and consistently improve decision models based on feedback.

Benefits of federated learning in Asean's Cybersecurity Framework

Implementing federated learning within Asean's cybersecurity framework offers several advantages:

 Federated learning allows participants at a national level [government agencies and local enterprises] and regional level [ASEAN member state national agencies] to collaborate on threat intelligence without sharing raw data, addressing concerns related to data privacy and sovereignty.


 By aggregating model updates from diverse datasets across the region, federated learning can improve the accuracy and robustness of threat detection systems, enabling the identification of sophisticated cyber threats that may not be detectable by individual nations.


 Collaborative model training reduces redundancy and optimises resource utilisation, allowing member states to benefit from shared intelligence without incurring significant additional costs.

A multi-level intelligence-sharing model can facilitate secure cybersecurity collaboration in Asean. This framework integrates decision intelligence platforms for real-time security automation and federated learning for cross-border intelligence-sharing while preserving data privacy.

And in what kind of sectors could such a model work? In the whitepaper, 

Better Intelligence Is Collective: Unlocking The Potential of AI with Federated Learning

, experts share that early adopters could possibly be central authorities in highly regulated sectors such as digital trade, digital payments, ecommerce, air traffic, ports etc. that already demonstrate the highest level of digitalisation.

 – DIPs deployed within individual banks detect threats, generate automated decisions, and enhance security operations.

Use Case: Human Managed worked with a major Asean bank to protect their distributed digital assets and enhance the maturity of its security operations.

Relevant data sources were curated, digital assets and security controls were catalogued with their context and relationships. The data catalogues and graphs formed the knowledge base to generate decision intelligence for cloud security, SecOps and fraud management.

Over time, a decision navigation system was built to support the bank’s strategic, tactical and operational decisions. The results were transformative, resulting in a 90% reduction in network security violations, a 97.8% reduction in mean time to respond (MTTR) to phishing attacks and a 97.9% reduction in MTTR to DOS attacks.

 – Federated learning aggregates anonymised intelligence from multiple banks, improving fraud detection models without sharing sensitive data.

Use Case: In Singapore, banks and telcos have been assigned duties for a shared responsibility fraud framework to protect consumers from fraud. Banks need to perform real-time fraud surveillance to detect if a customer’s account is being drained due to a phishing scam and send outgoing transactions to consumers. Similarly telcos need to implement a scam filter for SMSs.

In individual banks and telcos, DIPs can be developed to trigger alert outcomes to notify customers. Additionally, banks and telcos can share annonymised intelligence to a central platform leveraging federated learning techniques for improved fraud detection training models.

 – Asean-wide federated learning hubs aggregate national intelligence flows, enabling proactive regional cybersecurity coordination under Norm 13a.

Use Case: Under Norm 13a, “Inter-State Cooperation on Cybersecurity” Norm, of the Asean Norms Implementation Checklist for responsible state behaviour in cyberspace, the technical pillar recommends building the infrastructure needed to facilitate information exchanges with other states, developing cybersecurity capabilities for endpoint protection via automatic updates, to prevent, detect or disrupt malicious ICT acts.

Regional hubs of information powered by federated learning technologies would enable sharing of intelligence while protecting privacy for stakeholders.

The Asean Norms Implementation Checklist is a regional first. Not only does it serve as a guideline for responsible national behaviour for Asean member states but also for countries beyond the region, in supporting efforts in building a more secure cyberspace.

Asean leaders are early adopters who are entering new territory as a collective and while the unfamiliar can be daunting, the collaborative intent is exciting. They are building a future where human and machine interactions can leverage decision support, decision augmentation and decision automation to enhance cybersecurity in the region.

Many unknowns need to be worked through, but with the technical solutions for balancing privacy and innovation are already available. Ultimately, it all comes down to the collective will for intelligence sharing, to secure the region on its transformative digital journey.

originally published on The Edge Singapore

Balancing innovation and privacy for collective cybersecurity in Asean

Early adopters of cloud technology were early beneficiaries of its positives: scalability, cost savings, accessibility and more. But as experts caution, a move to the cloud isn’t a “set it and forget it” decision. Over time, outdated security protocols, underutilized resources and legacy architectures can create issues that hinder business growth and even open you up to cyber risks.

If your business was an early mover to the cloud, now is a good time to review your cloud strategies and identify opportunities for optimization, automation and modernization. Below, members of 

 share key areas businesses should focus on when assessing their cloud presence and why these factors matter.

Over time, cloud waste accumulates—unused resources linger and ownership becomes unclear. Manual cleanup is tough, and outdated access roles often remain overprivileged, creating security risks. Automating discovery and remediation is key to tackling hidden waste and excessive permissions, helping organizations reduce costs, strengthen security and maintain a well-governed cloud environment. - 

For seasoned cloud pioneers, now is the moment to rethink legacy security blueprints. Designed for simpler eras, many early frameworks fall short in countering today’s advanced cyberthreats. Modernizing encryption, access controls and monitoring systems is crucial for meeting evolving compliance standards, enhancing resilience and safeguarding critical assets in an ever-changing threat landscape. - 

Companies reviewing their cloud presence should assess cost optimization. Over time, cloud usage can become inefficient due to unused resources, outdated pricing models or overprovisioning. Regular audits help identify savings, improve performance and ensure alignment with business needs, maximizing the value of cloud investments. - 

Cloud repatriation is on the rise because many are paying a premium for overprovisioned resources and vendor lock-in pricing models that appear designed to inflate costs. With AI, costs will likely go up. Take the time to list out and divide out what belongs on the cloud and what should be brought back on premises, as greater savings and security may be achieved in company-owned environments. - 

Businesses should evaluate workload portability—the ability to migrate workloads across cloud providers or repatriate them on premises. This flexibility prevents vendor lock-in, enhances cost control and strengthens negotiating power as cloud expenses rise. A portable infrastructure ensures adaptability to meet changing business needs while optimizing long-term cloud strategy and efficiency. - 

AI will unlock a new layer of intelligent cloud management, including automated cloud provisioning, intelligent cost and throughput management, advanced analytics, enhanced security features, and more. Evaluate your cloud service for value-added features that can unlock business leverage, instead of looking through a pure cost lens. - 

Companies should assess scalability challenges across physical and virtual spaces. While a holistic approach offers advantages, it adds complexity. The cloud helps, but it isn’t a one-size-fits-all solution, especially for specialized resources like GPUs for AI workloads. Technologies like Kubernetes enable scaling, but applications must be designed to leverage these tools effectively. - 

8. IVR And Contact Center Testing Strategies

Early cloud adopters should reassess their interactive voice response and contact center testing strategies. Many moved to the cloud for scalability, but they may not have full visibility into global call quality and the customer experience. Ensuring consistent, real-time monitoring and proactive issue detection can prevent silent failures that impact customer satisfaction and revenue. - 

The main thing that companies should focus on is getting executive buy-in for locking down cloud portals to read-only and “break glass” type changes. This focus, combined with automation solutions, will greatly benefit companies seeking to gain control of sprawling cloud environments. - 

Companies must review cloud security and data compliance, which many service providers offer. Data regulations have evolved, so revisiting cloud strategies and security is essential, especially as organizations integrate AI. - 

11. Cloud Economics, Automation And Interoperability

Early cloud adopters must balance AI-driven innovation with a systematic cloud strategy. As AI evolves, enterprises need resilient, more cost-efficient systems. CTOs should focus on cloud economics, automation and interoperability. Leveraging AI where impactful—while avoiding overreliance on trends that may not drive lasting business value and enterprise growth strategy—is crucial. - 

Early cloud adoption was infrastructure migration. Now the world is run on data—and cloud resources as a data model is one of the most complex. Businesses must take stock of their entire digital assets (including the cloud) and their relationships and criticality to the overall business. Getting data-driven visibility will inform decisions for cost optimization, security and risk management. - 

13. Resource Rightsizing And Optimization

Many businesses are overprovisioned, paying for underutilized instances or outdated architectures. A review should focus on rightsizing resources, leveraging spot instances and optimizing storage tiers. Cloud-native AI tools can help automate scaling, reducing waste while maintaining performance and security. That said, it only works if AI is enabled across the organization. - 

Companies should reevaluate their current cloud architecture for modernization opportunities. Many early cloud adopters still run outdated lift-and-shift deployments that fail to leverage serverless computing or AI-driven automation. Refactoring applications can enhance scalability, reduce costs and improve performance, ensuring the cloud strategy remains competitive and future-ready. - 

15. Security Protocols And Infrastructure

Companies assessing their cloud presence should take a look at their security protocols and infrastructure. Early cloud adopters often implemented security measures that may now be outdated, leaving them vulnerable to new threats. A thorough review can identify potential weaknesses and ensure alignment with current best practices for data protection and compliance. - 

Over time, many early adopters have accumulated a mix of cloud services, legacy systems and third-party tools that may not work seamlessly together. This can lead to data silos, inefficiencies, a suboptimal experience and unnecessary costs. In my opinion, a key area to evaluate would be interoperability opportunities that can create a one-stop-shop experience through a consolidated engagement layer. - 

17. Data Classification, Life Cycles And Governance

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