WITH the rise of open banking and increasing cyber threats, real-time observability is becoming a national priority in the Philippines. Observability provides real-time visibility into how systems behave under stress, allowing IT and security teams to move from passive detection to proactive threat hunting.
While many organizations have adopted observability to strengthen their cybersecurity framework, gaps remain between adoption and implementation. According to ManageEngine’s ITOM Survey 2025, observability data revealed that there is a clear need for more intelligent tooling.
In a conversation with The Manila Times, Gowrisankar Chinnayan, director of product management at ManageEngine FSO, shared insights on the gaps in observability adoption in cybersecurity and the role of AI-driven observability for real-time infrastructure visibility and cyber resilience.
The Manila Times (TMT): What is observability with respect to cybersecurity, and how did the issue of observability arise? Why should business and industry be aware of observability, particularly in the aspect of open banking?
Gowrisankar Chinnayan (Chinnayan): Observability, when applied to cybersecurity, is about using the same telemetry that ensures performance — logs, metrics, traces, and events — to detect, investigate, and even predict security threats. The rise of observability in cybersecurity stems from the reality that digital transformation has multiplied attack surfaces. Open banking, cloud-native adoption, and API-driven ecosystems mean that transactions and data flows are spread across partners, platforms, and geographies. Traditional monitoring alone cannot keep up with that complexity.
Get the latest news
delivered to your inbox Sign up for The Manila Times newsletters By signing up with an email address, I acknowledge that I have read and agree to the Terms of Service and Privacy Policy.
This is why many Philippine enterprises are already using observability with security in mind. For the banking industry, this shift carries strategic weight. Observability should not be seen as a back-end technical enhancement but as part of its service promise to customers. An agile posture means using observability to anticipate risks, ensure uptime, and keep services resilient even under high transaction volumes or sophisticated cyberattacks. In practice, bank transactions — whether payments, fund transfers, or loan applications — will be seamless and secure.
TMT: What is the significance of AI in the implementation of observability, and what are the current AI tools available? How do these tools support BSP’s push for real-time infrastructure visibility and cyber resilience?
Advertisement
Chinnayan: When you look at the sheer volume and velocity of telemetry data far exceeding what human operators can manually process, you’ll understand that AI is no longer optional but a non-negotiable component. In security contexts, milliseconds matter: a delayed correlation or missed anomaly can mean fraud slipping through, services going offline, or a regulatory breach. AI-driven features close that gap by accelerating detection, pointing teams toward the most probable root causes, and automating parts of investigation.
This is highly relevant in the Philippines, where the BSP now requires banks and e-wallet providers to deploy real-time, automated fraud and risk management systems. Observability platforms, when powered by AI, enable exactly that; they turn raw telemetry into prioritized insights, surface patterns across distributed systems, and reduce reliance on manual triage.
AI and ML capabilities have become a top priority when organizations evaluate observability tools. The focus is increasingly on advanced features such as root cause analysis and generative AI (GenAI) to support query generation and incident summarization.
TMT: Apparently, there is a gap between high observability adoption and limited technical effectiveness. How does that gap affect the stakeholders in securing complex, fast-moving digital environments?
Advertisement
Chinnayan: It’s important to note that observability has already delivered real progress. Many Philippine enterprises are seeing major improvements in response times, with faster detection, triage, and resolution of incidents.
However, integration remains a key hurdle. Seamless data flow and workflow consistency between observability platforms and adjacent systems such as ITSM, SecOps, or fraud detection are still works in progress. The cost of scaling telemetry is another challenge, as data volumes grow faster than budgets. And AI and ML features, though promising, are still too generic in many cases, requiring heavy tuning before they deliver true value.
On top of these hurdles is the persistent skills gap. The Department of Labor and Employment (DOLE) underscored in 2025 that closing the gap in emerging and high-growth sectors is essential to unlocking the Philippines’ full economic potential — a reality also reflected in the IT space, where a lack of in-house expertise to effectively leverage observability tools remains a limiting factor.
Put together, these gaps explain why organizations can see faster response times on paper yet still feel weighed down by manual workloads and complexity.
Advertisement
TMT: What are key considerations to help local organizations move from reactive monitoring to proactive defense?
Chinnayan: The shift from reactive monitoring to proactive defense is really about changing the posture of IT teams from responding after the fact to anticipating issues before they escalate. That requires two key considerations.
First, the foundation is comprehensive visibility. You cannot predict what you cannot see. Observability equips teams to stitch together signals across infrastructure, applications, and networks so anomalies are spotted early and investigated in context.
Second, it’s about culture and design. Teams need to set up observability as part of system architecture. That means collecting relevant telemetry, enriching it with business context, and building alerting rules that focus on risk and impact. The goal is to prevent fatigue from false positives and give analysts the headroom to act decisively.
Advertisement
A third consideration is how effectively organizations can harness observability to predict and prevent issues before they escalate. By using observability platforms, IT leaders can detect anomalies early, correlate signals across complex systems, and prioritize threats in real time, strengthening the organization’s overall defense and freeing up valuable time and resources for long-term digital growth.
