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

Early cloud adopters often lifted and shifted data without a long-term plan, leading to inefficiencies, security risks and skyrocketing costs. Reviewing data classification, life cycle policies and access governance ensures storage aligns with business needs. A modern approach optimizes performance, cost and compliance while unlocking AI-driven insights. - 

18. Opportunities To Leverage Managed Cloud Services

Early cloud adopters built a lot of services and infrastructure, which public cloud providers ended up offering as managed services. While some of these managed services were not good at launch, a lot of them improved over time and are now better, cheaper and more reliable than their self-managed versions. Early cloud adopters should assess cloud infrastructure that competes with their internal services. - 

In the analytics space, there are a great number of early adopters with bespoke data lake implementations. Such systems require significant system engineering and maintenance, and a large majority are candidates for cloud data warehousing platforms. Those that do fit the data lake pattern can benefit from modern tooling and frameworks, as well as “data lake as a service” offerings. - 

Many businesses “lifted and shifted” on-premises workloads to the cloud, merely transferring existing inefficiencies. Companies should evaluate whether they’ve embraced cloud-native technologies, like serverless computing, purpose-built databases and automated infrastructure management, to convert technical debt into opportunities for operational efficiency. - 

originally published on Forbes Technology Council

Time For A Cloud Check-Up? 20 Key Areas To Review

The Human Managed Intelligence Decision Action (I.DE.A.) platform improves decision efficiency in the age of Data and AI

Decision Dilemma From Information Overload

Most modern digital businesses are united in one aspect: they want to leverage data to drive innovation, fuel growth and improve service. All business leaders also need to accept and plan for one trend: whatever the future of AI, there will be more data, not less.

The volume, velocity, and variety of data generated and stored are growing exponentially, estimated to reach more than 2,000 zettabytes by 2035. Oracle cites that more data leads to a 10x increase in the number and complexity of decisions business leaders and employees make every day. Alert fatigue, manual analysis, and indecision all contribute to operational inefficiency.

Decision Efficiency From Data-Driven I.DE.A.s

Leveraging data and AI to drive business value must be approached from both data and process levels. All businesses have unique data and processes, so the approach to data-driven outcomes must be contextualised for every business.

Human Managed is a cloud-native service platform for 24/7 cybersecurity, risk, and data operations with a mission to improve decision efficiency. Our platform processes data and generates custom intelligence, decisions, and actions (I.DE.A.) to protect and manage their digital assets. Some of our top solutions cover Cloud Security, SecOps Center (SOC), Fraud Management, and Data Cataloging.

To get started customers simply need to select the cyber, risk and digital outcomes they want, and install the agent for the Human Managed Foundational Platform (hm.works) to receive or collect data for chosen outcomes. Customers can bring their own data source (BYOD) or go with standard software in the Human Managed platform.

Our Product Stack structures the data and runs cycles of analysis to generate personalised intelligence for selected use cases in Cyber, Risk, and Digital Workbenches. The Workbench platforms are domain expert systems that generate and serve customized insights for selected use cases.

Finally, our Intelligence Decision Action (I.DE.A.) platform delivers the outputs as a 24/7 service through custom report, notification, and dispatch. Customers can also explore and search their processed and contextualised data.

A more intelligent & secure approach to I.DE.A.s

As a leader in comprehensive data management, we seek to help businesses unlock smarter and more secure decision-making. By controlling threats, managing risks and scaling opportunities our modular platform transforms complex data into actionable insights.

Through this we make data accessible to all, providing companies with the intelligent insights they need to achieve greater progress and unlock their full potential. Our platform helps organisations reduce downtime by automating processes that would otherwise be manual, enhancing workflow effectiveness and efficiency.

Through continuous monitoring and rapid incident response, we ensure businesses are protected against emerging threats, giving them the ability to stay focused on growth while their data security needs are expertly managed.

With in-depth insights and transparent reporting, companies can maintain better governance and comply with regulations more effortlessly, ensuring both business continuity and long-term sustainability.

Our suite of microservices cover everything from compliance management to threat detection to enhancing your cybersecurity posture, leading to significant gains in operational efficiency.

By leveraging advanced analytics and machine learning, companies of all sizes can uncover hidden opportunities, enhance productivity, and gain a competitive edge in their respective industries.

Our platform has been built to boast high adaptability, enabling businesses to tailor solutions to fit their unique needs and empowering them to stay competitive in a rapidly evolving digital landscape.

To read the full Aboitiz Group report, click 

Empowering Decision-Making with Data Intelligence

Picture this: You’re driving through a sprawling, unfamiliar city at night, with roads constantly shifting beneath your wheels and signs disappearing as soon as they appear. That’s what managing enterprise cybersecurity can often feel like.

Cloud assets can be vast, and a lack of visibility can create blind spots where threats lurk. Many businesses operate in multicloud environments, each with its own architecture, rules and configurations. Without a unified approach, inconsistencies can arise, leaving gaps in defense. Add to this the problem of siloed operations. Often, security, IT and DevOps teams work in isolation, leading to inefficient communication, slow response times and increased vulnerability to risks.

IBM’s Cost of a Data Breach Report 2024

 found that 40% of data breaches affected data residing in multiple environments. The report also indicated that breaches involving data in public cloud environments had the highest average cost, reaching $5.17 million. Organizations are increasingly applying AI and automation to reduce the cost of such breaches. However, businesses are still grappling with technological possibilities and actual decision-making.

Decision Intelligence Platforms: A GPS For Better Outcomes

Here's where decision intelligence platforms (DIP) can help. DIPs provide a real-time, unified view of all your data and insights transformed into explicit decisions and actions to be taken to reach a certain business outcome. Think of it as a context-aware GPS taking you to a required destination, and once that destination is achieved, the system learns from the path taken for subsequent improvements.

Although still an emerging trend, the adoption of decision intelligence (DI) tools and techniques has been growing to deal with the increasing complexity of decision making. According to 

, by 2026, market convergence will prompt 50% of organizations to evaluate analytics and business intelligence (ABI) and data science and machine learning (DSML) platforms as a single platform.

In the context of cybersecurity, DIPs provide the following:

• Contextualized And Unified Visibility:

 DIPs map out all assets across cloud platforms, track changes as they occur and maintain up-to-date business 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 analyzing 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.

Potential Roadblocks Of Decision Intelligence Platforms

However, multicloud complexity also needs technical problem-solving, stakeholder management and strategic decision making to navigate obstacles, including:

 Many security analysts and IT teams have been using manual processes or legacy tools for years and may view DIPs as disruptive rather than beneficial.

 Organizations often have complex security stacks that include firewalls, SIEMs, threat intelligence feeds and compliance tools.

 DIPs rely on large-scale data collection and analysis, raising concerns about data privacy, sovereignty and compliance, slowing down platform deployment.

 Without proper tuning, DIPs can flood teams with threat intelligence alerts, many of which are false positives.

Transforming Data Into Decision Intelligence: Two Case Studies

No matter how advanced a navigational system's features are, a journey can't begin without a well-defined destination. In the context of decision intelligence platforms, it's crucial to have a clear understanding of the organization's goals and the needs of its stakeholders.

When a major ASEAN bank approached Human Managed, they faced significant challenges: a lack of visibility over their assets, complex environments with data silos and legacy architecture. Their goal was clear—protect distributed digital assets and enhance the maturity of their security operations.

To reach this goal, relevant data sources and use cases were curated. Their digital assets and security controls were cataloged with their context and relationships. The data catalogs and graphs formed the knowledge base to generate decision intelligence for various cyber and risk use cases, including cloud security, SecOps and fraud management. Over time, the bank's decision navigation system was built to support the bank's strategic, tactical and operational decisions.

Some outcomes were transformative. The bank saw 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.

 or SDVs, projected to include up to 300 million lines of code by 2030. An IBM and Microsoft 

 shares the case study of an auto manufacturer that was looking to go fully electric and expand its base of connected vehicles. A sample of critical IT assets revealed that assessed risk could result in annual losses of over $1 billion. Leveraging its investment in Microsoft Azure platform services, the company worked with IBM to embed a “secure by design” methodology across the entire supply chain. Gaining visibility over and closing security gaps from suppliers to backend services was a result of leveraging decision intelligence infrastructure.

The world of decision intelligence is still new territory, and any journey into the unfamiliar can be daunting. The service providers who are building the nuts and bolts of decision intelligence platforms and the early adopters who are willing to test these initial versions are true collaborative partners. They're building a future where human and machine interactions will run the gamut of decision support, decision augmentation and decision automation. It’s an exciting future, but at present, many unknowns still must be worked through. Collaborative partnership thus holds the key to the future of decision intelligence.

Navigating Cybersecurity With Decision Intelligence Platforms

How Charmaine Valmonte is steering a century-old firm’s evolution from traditional infrastructure to AI-powered tech enterprise while maintaining security

The transformation of century-old enterprises into digital businesses has become a defining challenge of corporate strategy. In Southeast Asia, where traditional conglomerates maintain significant economic influence, this evolution carries implications for national infrastructure and development. The Aboitiz Group, with 44 business units spanning power generation, banking, food, land, and infrastructure, highlights this shift.

From its beginnings in Philippine agriculture, through shipping and maritime operations, to its current position managing critical infrastructure, Aboitiz has adapted to technological change. Today, the company positions itself as the Philippines’ first “techglomerate” – integrating AI and data science across sectors that range from renewable energy to digital banking.

Securing this transformation falls to Charmaine Valmonte, Chief Information Security Officer at Aboitiz Equity Ventures. “We're using technology to accelerate and enhance innovation through our people,” she explains, describing the company's shift from traditional operations to technology-enabled growth.

Operating at the scale of protecting systems that touch millions of lives across Southeast Asia, Charmaine brings military precision to this corporate mission. “I spent 21 years in the US Army as a signal officer, providing telecommunications, data and voice to our troops wherever we deployed, whether in the middle of the desert, Europe or Asia,” she says. This background in military communications evolved into cybersecurity leadership as technology shifted. “We started in information security around 2001-2004. Back then it wasn't known as cybersecurity.”

The stakes extend beyond corporate strategy. With interests from renewable energy to digital banking, the security of Aboitiz’s systems impacts national infrastructure. The company aims for a 50-50 split between renewable and thermal power capacity by 2030, while its financial services handle critical banking data. This combination of traditional infrastructure and digital transformation creates distinct security challenges, requiring new approaches to system protection.

The stakes of this transformation extend beyond corporate strategy. With interests from renewable energy to digital banking, the security of Aboitiz’s systems impacts national infrastructure. The company aims for a 50-50 split between renewable and thermal power capacity by 2030, while its financial services handle critical banking data. “We drive change for a better world by advancing business and communities,” Charmaine says.

Aboitiz’s path from agriculture to technology spans more than a century of Philippine economic development. The company began in farming, moved into shipping and maritime operations, and now operates across 44 business units. Yet its current transformation marks the most significant shift in its operating model.

“We have legacy businesses – power, banking, brick and mortar operations – but we're pivoting to digitisation,” says Charmaine. This pivot manifests in concrete changes across sectors. The banking arm exemplifies this evolution, moving from traditional branches to cloud-enabled digital banking, powered by data science and AI.

The company’s manufacturing operations now stretch across five Southeast Asian markets, while its infrastructure division manages multiple airports. Each sector incorporates technology not as an add-on, but as a fundamental operating principle. This creates what Aboitiz terms the ‘techglomerate premium’ – value generated through cross-sector technology implementation.

“We can develop different products, create synergies between the businesses, and learn from one another using technology and innovation to add more value,” Charmaine highlights. This approach requires significant cultural change. “Our CEO has said that to transform, it has to start with all of us as humans – we are the builders and beneficiaries.”

The transformation extends beyond internal operations. Aboitiz’s data science and AI arm develops models for government, healthcare and financial services. In power generation, the company deploys AI to expand renewable energy operations, targeting equal distribution between renewable and thermal capacity within six years.

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