Reconstruct what actually happened.

Every investigation starts the same way: something fired, and now someone has to explain it. The classic first hours are a hunt. Query one tool, export the results, pivot to the next tool, and stitch the pieces together by hand in a spreadsheet or a war-room channel. The craft involved is real, and we document much of it in our security log analysis guide, but the craft exists to compensate for an architecture in which no single system holds the whole story.

The cost is measured in hours during exactly the period when hours matter most. In a ransomware case the gap between detection and containment decides the blast radius, a dynamic our ransomware incident response guide walks through stage by stage. Responders lose those hours to data assembly, not to judgment. The investigation is slow because the memory it needs is fragmented across tools that were never designed to agree with each other.

On Bloo the assembly step disappears. Telemetry from endpoint, cloud, identity, network, and application sources is already captured into one substrate by Datafabric, so the investigation starts at the question, not at the export queue.

The second failure mode of legacy investigation is quieter: the evidence was collected, and then it aged out. Retention windows sized by ingestion cost mean the precursor activity, the initial access, and the early staging are often gone from hot storage weeks before anyone knows there is a case. Our analysis of security log retention covers how routinely this happens and what it costs.

Real campaigns exploit exactly this gap. The tradecraft we track in campaigns like APT36 and Mustang Panda unfolds over months, and the tooling documented in our malware research library is built to stay quiet longer than a 90-day window. An investigation that cannot see past the window is not reconstructing the incident, it is reconstructing the last chapter of it.

Datafabric retains full-fidelity telemetry hot for years, with economics that scale with time rather than volume, so the slow-burn precursor is still there to find. Nothing ages out mid-case, and no restore job stands between the responder and the early history.

With the record intact, the investigation itself changes shape. Instead of searching forward through everything that happened, reconstruction walks backward from the anomaly. Each step asks one question: what earlier event caused this one? The answer links a parent process, an authentication, a session, or a configuration change, and attaches the record that proves it. The interactive timeline above this article shows the walk; the product behind it is SynthAI's investigation capability, which assembles the chain by reasoning over the substrate.

The output is a causal history, not a pile of matching log lines. It reads in order, from origin to anomaly, which is the same structure defenders use to think about attacks in the first place. The cyber kill chain describes an intrusion as stages; a reconstruction recovers those stages from the record instead of inferring them from experience. Where a manual investigation produces a narrative that lives in the lead responder's head, a reconstruction produces an artifact anyone can open, replay, and extend.

The hard part of any timeline is the stitching. A host appears in endpoint telemetry by hostname, in cloud logs by instance ID, and in identity logs by the user who touched it. Legacy workflows reconcile those identities by hand, one join at a time, using the techniques cataloged in our log correlation guide. Every join is a place to make a mistake, and every mistake ships in the final report.

Because all of it lands in one substrate, Datafabric maintains the entity thread as data rather than as analyst effort. A host, an identity, a process, and a network flow are followed across sources as one story, queryable through hot search across the whole record. This is the property we mean when we say telemetry becomes organizational memory: the fabric does not just store events, it maintains the histories of the entities those events describe. Advanced host-level techniques, like the memory address pattern hunting we describe in this Sysmon deep dive, slot into the same timeline as the cloud and identity events around them.

A conclusion that cannot be defended is not a conclusion, it is an opinion with a timeline attached. Investigations increasingly end in front of auditors, insurers, regulators, and sometimes courts, and what those audiences require is provenance: which records support each step, where they came from, and whether they were altered.

Reconstruction on Bloo carries that provenance by construction. Every node in the causal chain cites the full-fidelity record it came from, and the record itself never left your own cloud, so custody is simple to establish. The same retained history that powers the investigation also answers the compliance questions that follow it, which is the subject of our companion use case on compliance and audit history and the risk and compliance solution. One record serves both masters, because both need the same thing: completeness that does not require an apology.

The reconstruction mechanism does not care whether the consumer is a human or an agent. As response timelines compress, the first pass of an investigation is increasingly run by machine: an agent walks the chain, assembles the timeline, and hands the responder a reconstruction to verify rather than a blank query box. We make the case for this shift in AI-native incident response needs full-fidelity history.

The precondition is memory. An agent reasoning over a 90-day window inherits every blind spot of that window, with none of the intuition a veteran analyst uses to route around gaps. Grounded in the full record, the same agent produces conclusions that resolve to specific citable events, the property we explore in AI-driven decisions. Machine-speed forensics is not a model feature, it is a data property.

Two products carry this use case. Datafabric captures and retains full-fidelity telemetry inside your cloud through its capture pipeline, so the history exists to reconstruct. SynthAI reasons over it to assemble the causal chain, and keeps no second copy of your data.

Because the reconstruction runs on the same record everyone else trusts, its output is reproducible by anyone who reads it. That is the deeper argument of the system of record for enterprise telemetry: when one substrate holds the primary record, detection, investigation, audit, and automation stop disagreeing about what happened. Security teams run this directly, and MSSP and MDR providers run the same reconstruction across every customer-owned Datafabric they serve, without pooling anyone's data.