The Modern Cyber Landscape – From Response to Readiness
In 2025, the global cybersecurity landscape continues to evolve at breakneck speed. According to the Verizon Data Breach Investigations Report (DBIR 2025), 83% of organizations worldwide experienced at least one cybersecurity incident, and 24% of those breaches involved stolen or reused credentials. The IBM Cost of a Data Breach Report 2025 shows that the average breach cost has reached 4.44 million USD, with most attacks stemming from delayed detection and response.
Traditional reactive security the strategy of defending after an attack has been detected is no longer sufficient. It often results in data loss, extended downtime, and regulatory risks under laws like GDPR and Vietnam’s PDPL. In contrast, threat hunting a proactive cybersecurity approach empowers organizations to actively search for hidden threats before they cause damage. This strategy complements reactive defenses, strengthening Data Loss Prevention (DLP) and improving incident response readiness through early visibility and rapid containment.
A Story Every Business Knows Too Well
In 1969, psychiatrist Elisabeth Kübler-Ross described the five stages of grief that people experience when facing death: Denial, Anger, Bargaining, Depression, and Acceptance or simply DABDA.
Over time, that model has been used to explain all kinds of loss not just human loss, but emotional ones too: a failed project, a missed opportunity, or even… lost data. We’ve spent hours researching the emotional side of losing data though ironically. Still, after hearing countless stories from friends, colleagues, and clients, I’m convinced that data loss follows the same emotional pattern that Kübler-Ross observed decades ago. When data suddenly disappears, people react in similar ways first trying to fix it quickly, then realising it’s worse than expected.
Many business leaders first respond with: “It’s just temporary. We’ll reboot the device and everything will be fine.” But rebooting again and again without results only makes things worse. Unfortunately, this often delays the real recovery process. Every minute counts when data loss happens. So, the sooner we accept that a backup or recovery process may be necessary, the faster we can mobilise resources. Being in denial delays recovery and often multiplies cost.
When reality sets in, frustration erupts. This anger reflects how valuable data truly is. We might get angry at the system, your IT team, or even yourself for not having a proper backup. At that time, don’t waste time blaming anyone. Focus on solving the problem together. Having a clear incident-response plan helps everyone stay calm and productive.
At this point, people start trying different tools or calling data-recovery companies to “get something back.” The mindset is: “If I do x, maybe we’ll recover enough and be okay.”
While this shows proactivity, it often lacks strategic direction. Without a coherent plan, we risk patchwork recovery and long-term loss of control over data integrity. Some time, you can recover part of the data but without a good plan, you risk making things worse.
Therefore, We should define clearly: What data must we restore – how fast – by whom – and what residual risk do we accept?
This is the hardest stage. We start to confront what may be irrecoverable: Years of metadata, non-backed up files, sensitive customer records, or intellectual property. The mood is: “All that… gone. How will we rebuild?” This stage is real for many organisations, especially when the event drags on, costs escalate or systems remain offline. Once loss is confirmed, morale drops. Transparent communication and clear recovery steps restore focus. Leadership matters immensely here. We need Stay transparent with team. It’s important to manage the situation calmly and learn from it. Then, start building a stronger protection plan. Meanwhile, ensure your backup strategy and business-resilience plan turn from just “nice to have” to “must have”.
Eventually you reach a point of clarity. Recovery may be partial; some files may never return. But you pivot to the forward step: rebuild, clean up, rethink your strategy—and this time do it correctly. Acceptance is not defeat. It’s the moment the organisation says: “We have experienced data loss, we will learn, and we will emerge stronger.”
So, let’s use this moment to implement a robust data-protection policy backups, recovery testing, data-flow monitoring, and preventative solutions. For example, The 3-2-1-1-0 Backup Rule means keeping three copies of your data on two different media types, one copy offsite, one copy offline or air-gapped, and ensuring zero errors after backup verification to turn acceptance into resilience
What we can learn from this?
Understanding Reactive Security
Reactive security refers to cybersecurity measures designed to identify and mitigate attacks after they occur. Reactive security uses cybersecurity tools and best practices to build robust defenses against common attack approaches and cyber threats. It also enables detecting malicious activity when ill-intended third parties penetrate your network defenses and gain unauthorized access to your system. The most common reactive security features include firewalls, antivirus, and spam filters, realistic vulnerability assessment, and a disaster recovery plan. The standard Incident Response (IR) framework – Preparation, Detection, Containment, Eradication, Recovery, and Review follows guidance from NIST. While effective against known threats, reactive defense cannot stop zero-day exploits or fileless malware.
Example: A finance company relying solely on antivirus tools failed to detect credential abuse in its email system. The attacker accessed cloud storage unnoticed for three weeks, resulting in regulatory violations under GDPR.
What Is Proactive Threat Hunting and How It Works
Threat hunting is a proactive cybersecurity practice. Rather than waiting for alerts, it onitor endpoint activity, collect telemetry data, assumes that attackers may already be inside the network and continuously searches for Indicators of Attack (IoAs) behavioral signs such as abnormal login times, lateral movement, or suspicious process executions. Effective threat hunting combines machine learning, behavioral analytics, and human expertise through three main methods:
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- Anomaly-based analysis: Uses AI to detect unusual behaviors.
- Hypothesis-driven hunting: Investigates based on new intelligence.
- IoC/IoA-based detection: Correlates new threat intelligence with existing logs.
Following best practices from ENISA’s Threat Landscape Report 2025, the process includes: Preparation, Prioritization, Intelligence Integration, Trigger, Investigation, Containment and Remediation, and Post-hunt Review.
Example: A manufacturing firm used proactive threat hunting to detect “living-off-the-land” malware. Early detection prevented a ransomware deployment that could have halted operations for weeks.
Why Proactivity Matters for Data Loss Prevention (DLP)
Threat hunting detects malicious activity before it reaches to sensitive data. By analyzing endpoint logs and user behavior, analysts can spot internal accounts accessing unusually large data volumes a potential insider threat or data exfiltration attempt.
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- Threat Hunting – Hunting for data threats: Actively monitor systems to detect unusual behavior related to accessing, copying, or transmitting sensitive data. User Behavior Analytics (UBA) to detect behaviors that pose a risk of data leakage.
- Real-time analytics: Use AI/ML to analyze data streams and detect behavioral patterns that can lead to data loss. For example: Identify when an employee sends large amounts of data out at unusual times.
- Dynamic Access Control: Apply access policies based on context: location, device, time, user role. Prevent inappropriate data access or sharing as soon as the behavior occurs.
- Detect and prevent before a breach occur: Proactively check for vulnerabilities in data storage and transmission systems. Automatically alert and block risky behavior before data is leaked.
Proactive strategies also help organizations focus resources on high-value assets, reducing wasted time on false alarms. With stricter data privacy laws like GDPR and PDPL, early detection ensures faster reporting and reduces penalties. According to IBM 2025, organizations using proactive cybersecurity saved 1.49 million USD per breach through reduced recovery time and minimized data loss.
From Grief to Growth
The five stages of data loss mirror the evolution of cybersecurity maturity: from denial and reaction to acceptance and prevention. Understanding both the emotional and technical sides of data loss allows organizations to evolve from reactive defense to proactive resilience.
Data Loss Prevention (DLP) technologies detect, control, and prevent unintentional data leakage or destruction. A modern DLP framework integrates:
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- Continuous monitoring of devices, applications, and networks
- Content-aware protection that recognizes sensitive data
- Automatic policy enforcement
- Integrated backup and recovery for rapid restoration
At ITM, we help organizations move beyond reactive defense into proactive cybersecurity resilience, ensuring continuity, compliance, and confidence to grow.
Protect your data. Strengthen your resilience.
Contact ITM today to evaluate your data-protection readiness and secure your future.
