Ruslan Rakhmetov, Security Vision
In modern cybersecurity, hindsight is not just a technical technique, but a fundamental shift in strategic thinking. It marks a shift away from the traditional paradigm of perimeter defense and real-time threat prevention to a model based on the assumption that a breach is inevitable (similar to the business continuity planning processes in the BCM module). In this new paradigm, the main goal is to minimize the time an attacker spends in a compromised infrastructure and reduce the attack surface (similar to hardening in the SPC module). In this article, we will look at the general principles, stages of hindsight, and how it works in practical security.
The underlying mechanism of this process is how new threat data is applied to older, already collected data. In practice, in the incident management (SOAR) and threat intelligence (TIP ) modules, when new indicators of compromise (IOCs), updated detection rules, or behavioral signatures, the security system can "rewind time" and re-analyze the archived data with this fresh information. It’s like rereading the same book at different points in your life: you can always notice something new in the text. Imagine that information security specialists have a "time machine" that allows them to look at assets enterprises and their past performance to identify threats that were invisible to existing defenses at the time they emerged.
This approach accepts that real-time detection systems are inherently imperfect. And instead of aiming for the unachievable ideal of 100% prevention, the strategy shifts toward creating a self-correcting security system over time. The value is now expressed in the ability to quickly detect and eliminate the consequences of incidents that have already occurred, thereby increasing the overall resilience of the organization to cyberattacks. This is why the "post-incident" or post - mortem incident handling stage is so important.
Retrospective analysis is not a chaotic search through archives, but a structured, multi-stage discipline, a consistent transformation of raw historical data into actionable information that allows you to make informed decisions on enhancing security. And we can break this process down into clear stages:
1) Data collection and normalization
The process begins with centralized data collection from the widest possible range of sources in the corporate infrastructure. Since the quality and completeness of the collected data are the foundation of a successful retrospective investigation, it is worth paying attention to such automation systems (if you plan to automate this process), which include simple tools for integration with any third-party solution (such as the Security Vision Platform connector designer).
Imagine you are a detective investigating a mysterious incident in a large house: someone ate the entire cake intended for the celebration. You begin the investigation, and first you need to collect all the possible evidence in the house: CCTV footage (analogous to network data, PCAP) showing who went where; fingerprints on dishes and doorknobs and utensils (analogous to endpoint data, EDR) showing who touched what; and witness statements (analogous to system and application logs) about who saw, heard, and did what. So you have disparate information on hand: video recordings, photographs of fingerprints, oral histories. To work with this, you normalize the data - you enter everything into a single investigation notebook, bringing it to a common format.
In practical security, the key data sources at this stage can be presented in more detail as follows:
- Full network packet records and network flow data (NetFlow/IPFIX) are immutable and capture all network activity, including lateral movement and command and control (C2) communications, making them extremely difficult for attackers to hide or erase;
- Logs collected by EDR agents (including as part of SOAR) contain detailed telemetry about process launches, file modifications, registry changes, and user actions;
- Authentication logs (Active Directory, LDAP, VPN), system logs of servers and workstations, firewall logs, as well as data from specialized security tools (for example, DLP) enrich the picture.
Once collected, raw data from disparate sources is parsed and converted into a single, structured format, such as JSON. This process, called normalization, is critical because it enables correlation of events from different sources and perform effective search queries within a SIEM system or data lake.
2) Applying new knowledge to old data
At this point, the normalized historical data is reanalyzed using the newly acquired threat intelligence. You review your evidence notebook, but the picture doesn’t add up. Then one of the triggers occurs: new data (analogous to a new indicator of compromise) emerges, as one guest remembers that another guest recently won a cake-eating contest. You notice cake frosting on the floor that shouldn’t be there (analogous to an alert), prompting you to review all your kitchen-related records. The disparate facts now fit together.
Triggers for launching analysis in practical information security:
- Publication by a vendor, community or internal research team of a new indicator of compromise (file hash, IP address, domain name), a new Yara rule or a new behavioral signature;
- An alert is triggered in the monitoring system, which can be examined to understand the background of the attack and its full scope;
- Proactive Threat Hunting, where the analyst formulates a hypothesis (e.g., "Has a new technique used by the APT-X group been used in our network in the last 6 months?") and uses historical data to test it.
Analysis involves finding new indicators in a historical data set, correlating events from different sources to build an attack timeline, and identifying anomalies. The value lies not just in storing logs, but in the platform’s ability to connect disparate events into a single, coherent story.
3) From results to response
The final stage involves interpreting the results obtained, drawing conclusions and developing practical recommendations for action.
You have collected all the evidence together and come to conclusions: the fact of the "crime" is confirmed, the attack vector is that the "criminal" took advantage of the moment when the guests were busy, the scale of the damage is one cake.
- How was this possible? The cake was left in a prominent place unattended. This is the "vulnerability" in your security system.
- What to do to eliminate the consequences? Conduct an educational conversation with the troublemaker.
- What to do in the future? Store all cakes at parties in a special container with a combination lock (new security rule, for example, using the encryption algorithm) and install a motion sensor that will send you a notification on your phone if someone approaches the refrigerator at an inopportune time (improving the monitoring system, setting up correlation rules and triggers for notifications).
Even with automation, the need for skilled analysts will not disappear. AI can detect anomalies, but humans are still needed to provide context, understand intent, and make strategic decisions. The analyst’s role will shift from manually sifting through data to generating hypotheses, validating the output of automated systems, and planning strategic responses.
The hindsight process is a continuous improvement cycle, not a one-time linear process. Successful hindsight makes future real-time detection more effective, which in turn reduces the need to retrospectively search for the same threat. In this way, hindsight becomes an essential element for moving forward more safely.
