Episode 36 — Validate every pivot without chasing ghosts
In Episode 36 — Validate every pivot without chasing ghosts, we are focusing on a discipline that separates productive investigations from exhausting ones. Pivoting is powerful because it lets you move from one indicator to a broader picture, but pivoting can also lure you into chasing connections that are technically true yet operationally meaningless. A shared attribute does not always equal a shared attacker, and a pattern can be an illusion created by common infrastructure. Validation is the skill that keeps your curiosity from turning into drift. It gives you a way to pause, test, and confirm that each new step is anchored to something real. When you validate consistently, your investigations stay lean, your conclusions stay defensible, and your team spends its time on leads that can actually reduce risk.
Validation means making sure the connections you find during pivoting are relevant and real, not just coincidental overlaps. In threat intelligence work, many artifacts are shared because modern computing is shared. Cloud platforms, managed services, and global infrastructure providers create environments where unrelated entities can look connected if you only examine one attribute at a time. Validation requires you to ask whether a linkage is meaningful in the context of malicious operations. You are not trying to prove everything beyond doubt before taking a next step, but you are trying to prevent your investigation from being built on a weak foundation. A validated pivot is one where the relationship between two artifacts has a plausible mechanism, corroborating evidence, or repeated behavior that makes coincidence less likely. Without that grounding, you risk building a map that looks impressive and explains nothing.
A practical example is checking whether a shared IP address belongs to a large Content Delivery Network (C D N) provider. Many domains resolve to the same C D N addresses because the provider is doing exactly what it was designed to do, which is to serve content at scale. If you treat shared C D N infrastructure as evidence of a shared attacker, you will quickly create false clusters and confuse your own analysis. The disciplined move is to identify when you are seeing shared infrastructure and then adjust your pivot strategy accordingly. Instead of pivoting on the IP, you look for other attributes that are less likely to be shared by chance, such as unique hostnames, certificate details, registration artifacts, or behavior observed in telemetry. Recognizing common infrastructure early is like noticing that many cars share the same highway. It tells you something about the environment, but not about who is driving.
Another common trap is following every lead without first assessing the likelihood that it represents malicious data. Investigations can feel like momentum, where each pivot produces new artifacts, and new artifacts create the illusion of progress. The danger is that you can spend hours exploring a path that was low probability from the start, simply because it kept producing new things to look at. Validation introduces a decision point, where you ask whether the next step is justified based on what you have so far. This is not about being lazy or overly cautious. It is about being honest about opportunity cost. Every minute spent on a weak lead is a minute not spent on a strong lead. A disciplined team treats pivoting like a sequence of small investments and expects each investment to earn its way forward.
Using multiple sources to confirm that a specific indicator is truly linked to an attacker is one of the best defenses against chasing ghosts. A single dataset can be incomplete, outdated, or biased by how it was collected. When you corroborate an indicator across independent sources, you reduce the chance that you are following an artifact created by noise, misclassification, or shared infrastructure. Independence matters here, because two sources can repeat the same claim, creating the illusion of corroboration when it is really duplication. Validation asks you to find confirmation that is derived from different observation points, such as separate telemetry sources, independent reporting, or direct artifacts from your own environment. When the same linkage appears from different angles, confidence rises naturally. When it does not, you keep the linkage tentative and avoid building too much on it.
Imagine the moment when you pause your investigation to verify that a domain is not a false positive before you escalate it. That pause is not wasted time. It is quality control that prevents your team from running an avoidable detour. False positives can come from benign testing domains, security vendor infrastructure, internal scanning activity, or domains that were previously malicious but have been reclaimed. The disciplined move is to examine context that can confirm whether the domain is behaving maliciously now, not merely whether it has ever been suspicious. You might consider when it was first seen, whether it appears in internal telemetry, whether it is associated with specific behaviors, and whether it aligns with the timeline of your case. Validation at this stage protects your credibility, because calling something malicious when it is not is a fast way to lose trust with both technical teams and leadership.
A helpful way to think about validation is as a filter that keeps you from falling down a rabbit hole. Pivoting tends to amplify complexity, because each step exposes you to more data. Without a filter, complexity grows faster than understanding. The filter is your set of checks that decide whether a lead is worth expanding or should be set aside. Good filters are based on uniqueness, corroboration, and relevance to requirements, not on personal excitement. They also adapt as the investigation evolves, because what counts as relevant can change when a new hypothesis emerges. The key is that the filter is applied consistently, not only when you feel uncertain. In practice, you often feel most certain right before you make an untested assumption, so the filter needs to operate even when the path feels obvious.
One of the fastest ways to improve validation is learning to identify indicators that are too common or generic to be useful for high confidence pivoting. Shared ports, widely used file names, public resolver addresses, and common cloud endpoints can all produce false linkages if treated as unique. Generic indicators are not useless, but they require additional context to become meaningful. A generic indicator can be a supporting detail, but it should rarely be the backbone of a pivot. When you recognize generic indicators early, you prevent your graph from being built on weak connectors. You also reduce noise, because you stop generating clusters that are really just reflections of popular services. High confidence pivoting depends on choosing pivot points that have low chance of accidental overlap.
This validation process saves your team time by concentrating effort on the most promising and verified leads. Time saved is not just operational efficiency, it is analytic clarity. When you remove weak leads early, the remaining data becomes easier to interpret, and patterns become more visible. This also reduces the emotional drain of investigations, because chasing ghosts is demoralizing. Teams lose momentum when they realize they have spent hours on something that never mattered. Validation prevents that by creating small, frequent checkpoints. It is far easier to correct course early than to unwind a large map built on shaky assumptions. Over time, teams that validate well become faster, not slower, because they avoid rework and dead ends.
Validation also involves looking for conflicting data that might disprove your current theory about a threat actor or campaign. This is an uncomfortable step because it forces you to challenge your own narrative, but it is essential for accuracy. Conflicting data might include timestamps that do not align, infrastructure that has legitimate ownership, or behaviors that do not match the actor profile you are considering. The presence of conflict does not automatically invalidate your theory, but it does require explanation. Sometimes the conflict reveals a separate actor, a parallel benign process, or a misinterpretation of an artifact. Sometimes it reveals a coverage gap that makes your conclusion less certain. A disciplined analyst treats conflict as a signal to refine the hypothesis rather than as an inconvenience to ignore.
Documentation is what turns validation from a personal habit into a team capability. When you document the evidence used to validate each step of your pivoting process, you make your reasoning visible and reviewable. This protects you when questions arise later, and it helps other analysts learn your approach. Documentation also prevents memory drift, where you forget which links were validated and which were tentative. In complex investigations, it is easy to confuse early assumptions with confirmed findings if you do not write them down. Clear notes that separate confirmed from suspected relationships are a form of analytic hygiene. They keep your final product clean and allow the team to revisit decisions as new evidence appears.
Peer review is another practical validation tool, because a second mind is less likely to be anchored to your initial framing. Asking a peer to review your logic is not about doubting your competence. It is about acknowledging that assumptions are easiest to see from the outside. A peer can spot where you treated a shared service as unique, where you inferred intent without evidence, or where you skipped a validation step because the story felt smooth. This review is most effective when you provide the peer with your chain of reasoning and the evidence you used, not just your conclusion. The peer should be able to challenge the reasoning step by step. When teams normalize this kind of review, validation becomes a cultural norm rather than an occasional correction.
High confidence pivoting requires a disciplined approach to checking every new piece of information before it becomes part of the story you tell. This does not mean every pivot must be proven perfectly before you proceed. It means you classify pivots by confidence and you do not let low confidence links carry the weight of major conclusions. High confidence pivots are supported by multiple independent observations, unique attributes, and coherent alignment with the case timeline. Medium confidence pivots may be plausible but still need corroboration. Low confidence pivots are treated as leads, not as facts. This discipline keeps your products defensible because the strongest claims rest on the strongest links. It also keeps your investigations efficient because you invest most deeply where the evidence is strongest.
Over time, validation reshapes your investigative rhythm. You become comfortable pausing, checking, and continuing, rather than sprinting down every path that opens. You also become more selective about pivot points, choosing those that are unique and meaningful rather than those that are merely available. This improves analysis quality and reduces conflict with stakeholders, because your findings are less likely to swing wildly as weak leads collapse. Validation is not glamorous, but it is foundational. It is what keeps your intelligence work grounded in reality instead of in a network graph that looks convincing but rests on coincidence.
Conclusion: Validation prevents mistakes so verify your most recent pivot with a second source. When you treat each pivot as a claim that must be supported, you protect your team from chasing ghosts and you strengthen the integrity of your conclusions. Use validation to recognize shared infrastructure, filter out generic indicators, confirm key links with independent sources, and document the evidence that supports each step. Invite a peer to challenge your logic so assumptions are surfaced early rather than discovered late. By adopting this disciplined rhythm, you will pivot faster in the long run because you will spend less time undoing false paths. Take the last pivot you made in a recent investigation and find corroboration from a separate source, because that simple check is how confidence becomes earned rather than assumed.
