An effective post-mortem can turn a security breach into a blueprint for lasting resilience. But too often, in the stress of an incident, documenting what happened takes a back seat to containment and recovery. 

The resulting analysis relies heavily on memory, scattered notes, and competing narratives. Valuable context gets lost, timelines blur, and lessons that could strengthen defenses never become institutional knowledge. 

By selectively leveraging AI alongside human expertise, it’s possible to learn from incidents rather than just survive them. Now, teams have a way to capture, collate, and analyze incident details in real time, without detracting from the critical work of containment and recovery.

What should a security post-mortem accomplish?

A post-mortem should inform updates to processes, playbooks, and tooling, ensuring that each incident strengthens organizational resilience. In an ideal world, teams walk away from a security incident knowing:

• What caused it, and how to prevent similar events in the future
• Where response processes failed or bottlenecked
• How communications were handled across leadership, customers, and technical staff

The challenge is that traditional post-mortem processes aren’t designed to handle the complexity of real-world security incidents.

What’s wrong with our current approach?

Security incidents aren’t typically linear in the way that, for example, a database failure might be. Resolutions require multiple concurrent workstreams that unfold over days or weeks. 

An incident might start with a single malware alert on a laptop. Quickly, it becomes clear that there are 12 affected devices. Endpoint protection tools may quarantine ten of them automatically, but two need additional interventions to contain the threat. Now, response teams have to deal with parallel investigations, different evidence streams, and shifting timelines.

A full investigation of the incident may take place over multiple shifts and teams, with documentation scattered across logs, Slack threads, Zoom calls, and Google Docs. Without a post-mortem process built for this complexity, the story of the incident will remain incomplete, and the organization will struggle to prevent the next one.

Incident post-mortems and audit risks

When post-mortems miss key details, context is lost, and organizations are no better positioned to respond to future incidents than they were before.

For organizations in regulated industries, there’s an additional risk: Incomplete or inconsistent post-mortem documentation introduces the potential for compliance violations. The General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the Payment Card Industry Data Security Standard (PCI DSS) require organizations to maintain detailed records of incident response plans and security incidents and publicly report when data breaches occur.

Add to these ISO standards, SLAs, SOC 2 certification, and NIST standards, and it’s clear that accurate, actionable post-mortems are business-critical, regardless of your industry. Organizations that fail to meet documentation and reporting requirements can face penalties, reputational damage, liability, and other consequences. 

How AI enables incident capture

By automatically collecting and consolidating logs, alerts, meeting notes, and conversation transcripts, AI creates a centralized, searchable record that brings together all relevant information in one place. That way, it’s not scattered across multiple disconnected tools. PagerDuty’s Scribe Agent, for example, captures conversations, system alerts, and meeting notes in real time, giving teams a unified view of an incident as it unfolds.

This approach closes one of the biggest gaps in traditional post-mortems: documenting decisions made on the fly, across multiple platforms. Responders can quickly reference a single, comprehensive source of truth to understand what happened, why it happened, and who made key decisions.

A complement to human expertise

AI tools don’t replace human judgment. Rather, they amplify it. AI can pull together the logs, timelines, and statistics. But humans stay in charge of the post-mortem process, bringing in context and making nuanced judgment calls AI can’t provide.

PagerDuty’s Scribe Agent transcribes and summarizes conversations, alerts, and decisions in real time, while PagerDuty’s Post Incident Analysis tool, Jeli ingests those artifacts into its post-incident analysis engine to surface patterns, causal threads, and improvement opportunities. Together, they form a tight feedback loop: AI automates data capture and correlation, and humans apply judgment, context, and learning to turn that insight into better defenses.

Over time, the balance may shift. With richer runbooks, better documentation, and structured data, AI may be able to automatically remediate security incidents and conduct its own post-mortems. But for now, it serves as a powerful assistant, freeing responders to focus on strategy and decision-making rather than transcription and record-keeping.

Learning in real time

Security post-mortems should be a driver of resilience, not a box-checking exercise bogged down by incomplete records and lost context.

By combining real-time AI assistance with human expertise, organizations can finally move beyond flawed retrospectives. They can capture what really happened while it happens, and carry those lessons forward without detracting from the speed or quality of incident response.

PagerDuty’s AI tools are designed to help teams do just that: centralize documentation, streamline collaboration, and generate actionable insights that shorten containment times and strengthen defenses.

Ready to make your post-mortems smarter and your organization more resilient? Explore PagerDuty’s AI capabilities or sign up for free.