In a world where cyber threats evolve at an alarming pace, Dominic Jainy stands at the forefront of the defense, specializing in the strategic use of observability platforms to combat advanced, AI-driven attacks. His expertise in artificial intelligence and machine learning provides a unique lens on the future of proactive threat hunting. In this conversation, we explore how modern security teams are moving from a state of reactive firefighting to predictive defense. We’ll delve into how integrated observability is clearing the fog of fragmented data, empowering analysts to unmask stealthy threats in complex cloud environments, and discuss the profound cultural shifts required to build a truly resilient security posture.
Many threat hunters operate with fragmented data, creating significant visibility gaps. How do modern observability tools unify logs, metrics, and traces to solve this, and what tangible impact does this have on an analyst’s daily workflow? Please share a specific, real-world example.
It’s a perfect analogy to say hunters are navigating a pitch-black room with only sporadic flashes of light. That’s the reality when you’re dealing with siloed tools and data. An analyst might get a log from one system, a performance metric from another, and a network trace from a third, but they spend most of their time just trying to stitch those disparate pieces together into a coherent narrative. Modern observability platforms completely change this dynamic. They integrate those three pillars—logs, metrics, and traces—into a single, unified view across every layer of the IT environment. Suddenly, that pitch-black room is fully illuminated. The daily workflow transforms from a frantic, time-consuming puzzle-solving exercise into a decisive, intelligence-driven hunt.
A stark, real-world example is the detection of a supply-chain compromise. In several recent incidents, organizations with fragmented visibility didn’t realize they were breached until the damage was extensive. With a unified observability platform, an analyst can correlate a strange log-in pattern from an endpoint (a log), with an unusual spike in data exfiltration (a metric), and follow the exact path that data took through their microservices (a trace). This allows them to see the full attack timeline, identify the root cause, and act immediately, rather than discovering the breach weeks later.
With AI-driven attacks mimicking legitimate user activity and shrinking response times, how does an integrated observability platform help security teams distinguish malicious behavior from normal system noise? Could you describe the process of detecting such a sophisticated threat using these tools?
This is precisely where observability becomes a foundational element of modern defense. AI-driven attacks are designed to be subtle; they don’t trip the loud, obvious alarms of traditional malware. They might abuse valid credentials or use legitimate system tools, making them blend in with thousands of normal daily activities. An integrated platform provides the deep context needed to spot these deviations. It establishes a comprehensive baseline of what “normal” looks like across the entire system—every user, every application, every network flow. It’s like knowing the rhythm of a city so well that you can feel the one tiny vibration that’s out of place.
To detect a sophisticated threat, a hunter would start by leveraging the platform’s AI-powered anomaly detection. Let’s say an alert flags an unusual pattern of access for a specific user account. Instead of just seeing an isolated log-in event, the hunter can instantly pivot. They can use end-to-end tracing to see which services that account accessed, what data it touched, and how it communicated with other parts of the system. They might see that while the user’s actions looked legitimate individually, the sequence and timing were highly anomalous when contextualized against their established behavior. This ability to correlate events across the stack turns a sea of noise into a high-fidelity signal, revealing the attacker’s subtle footprint.
Organizations often focus on reducing Mean Time to Detection (MTTD). How specifically does a comprehensive observability strategy help lower this critical metric, and what steps are involved in shifting a team from a reactive posture to a more proactive, hypothesis-driven hunting model?
Lowering MTTD is a direct and powerful outcome of a strong observability strategy. It achieves this by eliminating the wasted time spent sifting through noise and manually correlating data from disconnected tools. When an anomaly is detected, a hunter has a detailed, contextualized timeline of the attack at their fingertips. They can see the root cause, the scope of the compromise, and the path the attacker took without having to painstakingly piece it all together. This shaves hours, and sometimes days, off the detection and investigation process.
Shifting the team’s posture is a journey. It begins with instrumentation—ensuring you can see and understand every layer of your environment. Once you have that visibility, you can move from just responding to alerts to actively forming hypotheses. A hunter might ask, “What would it look like if an attacker gained access to our Kubernetes cluster and was moving laterally?” With a comprehensive observability tool, they can then query the data, build real-time dashboards using tools like OpenSearch, and actively search for those specific indicators. This fosters a culture where the security team is no longer just waiting for an alarm to go off; they are actively and continuously scouring their environment for threats that have slipped past traditional defenses.
As enterprises adopt complex multi-cloud architectures, monitoring for threats like lateral movement becomes difficult. How does end-to-end tracing in observability platforms address this challenge in distributed systems, and can you walk us through how a hunter might use this capability to uncover a hidden threat?
Multi-cloud and microservices architectures are a nightmare for security teams without the right visibility. An attacker can make small, quiet hops between containers or across different cloud providers, and each hop looks like a normal, isolated transaction if you’re only looking at individual logs. This is how attackers achieve long dwell times. End-to-end tracing is the perfect antidote to this. It essentially stitches together the entire journey of a request or a data flow as it moves through your distributed systems, regardless of where those systems reside. It provides a complete, uninterrupted narrative.
Imagine a threat hunter has a suspicion about a compromised pod in a Kubernetes cluster. Without tracing, they’d be stuck looking at logs on that one pod. With tracing, they can see every single upstream and downstream service that pod communicated with. They might notice it made an unusual call to a database it never interacts with, then relayed a small packet of data to an external-facing API. Each step might be permissible on its own, but the trace reveals the malicious sequence—the lateral movement. The hunter can follow this thread, uncovering the entire kill chain, from initial entry to the point of exfiltration, all within a single, contextualized view.
Beyond technology, implementing true observability often requires a cultural shift toward DevSecOps. What are the key operational hurdles to embedding monitoring early in thedevelopment lifecycle, and how can security leaders champion an “assume-breach” mindset across their organizations?
The technological part is often the easier lift; the cultural change is the real mountain to climb. The biggest operational hurdle is breaking down the traditional silos between Development, Security, and Operations. Developers are often focused on shipping features quickly, and security can be seen as a roadblock. To embed monitoring early, security can’t just throw requirements over the wall. They need to provide developers with the tools and frameworks to build traceability and instrumentation directly into their code from day one. It’s about making security an enabler, not a gatekeeper.
Championing an “assume-breach” mindset is critical for security leaders, and it starts at the top. This means communicating that a breach is not a matter of if but when. The goal shifts from building an impenetrable fortress to achieving rapid resilience. Leaders can do this by investing in 24/7 observability, conducting regular and realistic breach simulation exercises, and celebrating the teams that detect and respond to threats quickly. When the entire organization understands that proactive detection and swift recovery are the true measures of success, security becomes a shared responsibility, deeply embedded in the company’s DNA.
What is your forecast for the evolution of threat hunting as AI continues to transform both offensive and defensive cybersecurity capabilities over the next five years?
The next five years will see a dramatic escalation in the cat-and-mouse game between AI-powered offense and AI-powered defense, with observability as the crucial battlefield. My forecast is that threat hunting will become almost entirely predictive. By 2026, we won’t just be hunting for existing indicators of compromise; we’ll be using machine learning models, fueled by rich observability data, to anticipate an adversary’s next move and preemptively harden targets. Hunting will be less about finding a needle in a haystack and more about using AI to predict where the needle is going to be dropped.
Autonomous systems providing 24/7 detection and response will become the standard, but the human hunter’s role will become even more strategic. They will be the ones training the AI, validating its findings, and handling the most sophisticated, novel threats that models haven’t seen before. We’ll see a fusion where AI handles the machine-speed exploits, freeing up human experts to focus on creative, hypothesis-driven hunting informed by a depth of system visibility we can only dream of today. The organizations that master this synergy of human expertise and AI-augmented observability will be the ones who remain resilient in the face of ever-smarter threats.