Karen Kim is the CEO of Human Managed, an ASEAN cloud-native data and AI service platform for cyber, digital and risk decisions.
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 Gartner researchers, 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.
• Real-Time Threat Detection: DIPs continuously monitor the cloud environment, providing early warnings and allowing teams to reroute or take preventive action before damage occurs.
• Predictive Analytics: DIPs anticipate potential vulnerabilities by analyzing historical patterns and emerging trends, shifting from reactive to proactive security management.
• Explicit And Transparent Decisions: DIPs combine data analytics and AI techniques to generate explicit and auditable decisions.
• Continuous Learning: 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:
• Organizational Resistance: 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.
• Integration With Existing Systems: Organizations often have complex security stacks that include firewalls, SIEMs, threat intelligence feeds and compliance tools.
• Data Privacy Concerns: DIPs rely on large-scale data collection and analysis, raising concerns about data privacy, sovereignty and compliance, slowing down platform deployment.
• Alert Overload: 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.
A Case Study From The Finance Industry
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.
A Case Study From The Auto Industry
Modern cars have become software-defined vehicles or SDVs, projected to include up to 300 million lines of code by 2030. An IBM and Microsoft research paper 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.
Collaborative Partnerships Hold The Key
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.
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