One of the major business trends from the past decade is the growing digitalisation of customer interactions. With all industries looking at ways to take a more digital and integrated approach to how they work, there is a significant opportunity to improve the customer experience. At the same time, digitalisation and further integration of systems presents a challenge as it opens up an organisation to a more diverse and threatening set of risks.
Theoretically, anything digital can be exploited by cyber criminals, cyber terrorists or malicious insiders. If we look at an emerging example, the majority of the healthcare industry was not connected to the network 10 years ago, but now you can control healthcare devices remotely. Being able to do this has advantages, but it also represents a real opportunity for those with malicious intent to cause serious harm.
In an environment of advanced threats - often to human life - changing business demands and an increasingly extensive technology infrastructure, a traditional perimeter focused approach to IT security is no longer effective. In my opinion a totally new approach to digital security is required. Organisations need to adopt a data driven approach to digital security if they are to stay ahead of the threats whilst experiencing the growth opportunities presented by Data & Analytics technologies.
The evidence of an attack exists in machine data within an organisation, so security teams need to gain insight from that data to properly detect, analyse and respond. Attackers will attempt to use all possible mechanisms to compromise an organisation, which may involve use of identity, endpoints, servers, business apps, web and email servers, as well as non-traditional systems. The evidence of these activities is often captured in the data from these systems, making analysis even more relevant.
By continuously monitoring this data across your entire infrastructure you can detect malicious activity as early as possible. This could involve spotting anomalies, recognising unusual activity or identifying indicators of compromised systems. As soon as you identify an issue, you can understand the scope and impact of a threat before taking steps to nullify it and ensure it doesn’t happen again. If, on the other hand, you aren’t able to see what’s happening, you can’t protect yourself”.
Many organisations are adopting technologies such as Splunk to get answers out of digital data sources. “For these organisations it’s critical that in a dynamic digital landscape they can apply Data & Analytics technology to quickly get answers to their questions in near to real time. This means they can react as soon as they detect anything that might give them – or their customers – cause for concern. With the threat landscape continuing to evolve, it’s clear that machine data will take its place as the first line of defence for organisations in all industries.
If you would like to find out more about how Data & Analytics could help you make the most out of your current infrastructure while enabling you to open your digital horizons, do give us a call at +44 (0)203 475 7980 or email us at Salesforce@coforge.com
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KEY TAKEAWAYS
- Digitalisation expands customer experience opportunities but significantly widens the cyber‑risk landscape.
- Traditional perimeter-based security is no longer effective in complex, integrated digital environments.
- Machine data is the most reliable evidence source for detecting, analysing, and responding to cyber threats.
- Continuous monitoring of machine data enables early detection of anomalies and compromised systems.
- Data-driven security platforms like Splunk help organisations make near real‑time decisions based on machine data insights.
FAQS
Q1. What is machine data in the context of cybersecurity?
Machine data includes logs, events, performance metrics, and automated outputs from systems, apps, devices, networks, and nontraditional digital assets. It is often the first indicator of cyber threats.
Q2. Why is perimeter-based security no longer sufficient?
Because organisations now operate highly integrated digital ecosystems with remote devices, cloud systems, and connected applications—leaving no fixed perimeter to defend.
Q3. How does continuous monitoring help in threat detection?
It identifies anomalies and indicators of compromise early, allowing teams to assess impact and contain threats quickly.
Q4. What role do platforms like Splunk play?
They help ingest, analyse, and visualise machine data in near real time, enabling security teams to identify risks as they emerge.
Best Practices for Implementing DataDriven Security
- Establish full visibility across endpoints, identities, servers, business apps, and nontraditional systems.
- Implement real time monitoring of machine data to detect early indicators of compromise.
- Automate anomaly detection using analytics and rule-based alerts.
- Regularly review and optimise dashboards to ensure relevant, actionable insights.
- Integrate analytics outputs into incident response workflows for faster resolution.
Common Pitfalls to Avoid
- Relying solely on perimeter defences, which are outdated for modern digital ecosystems.
- Ignoring nontraditional systems like IoT devices and connected medical equipment.
- Underestimating data volume, leading to poor visibility or incomplete analysis.
- Delayed monitoring, causing threats to go unnoticed until damage occurs.
Glossary of Key Terms
- Machine Data: Automatically generated logs and records from devices, apps, and systems.
- Indicators of Compromise (IoC): Signs that a system or device may have been breached.
- Anomaly Detection: Identifying unusual patterns that may indicate threats.
- DataDriven Security: Security practices centred on analysing digital data to detect and respond to threats.
- Security Analytics: Tools and techniques used to interpret machine data for security insights.
BLOCK QUOTE
“The evidence of an attack exists in machine data within an organisation, so security teams need to gain insight from that data to properly detect, analyse and respond.”
“If you aren’t able to see what’s happening, you can’t protect yourself.”
Traditional Security vs Data Driven Security
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Aspect
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Traditional Perimeter Security
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Data Driven Security
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Primary Focus
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Network boundary
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Machine data & all internal systems
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Threat Visibility
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Limited
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Complete, real-time
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Response Speed
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Reactive
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Proactive & near real-time
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Suitability
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Static environments
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Highly digitised, integrated ecosystems
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