AI in Digital Forensics and Incident Response (DFIR)

Digital Forensics and Incident Response (DFIR) has always been a field rooted in precision, technical rigor, and the ability to reconstruct complex attack narratives from fragmented evidence. Traditionally, success in DFIR has depended on the skill and intuition of highly trained specialists navigating terabytes of data such as logs, binaries, disk images, memory captures, and network traffic, to name a few, to answer very difficult questions and most times under tight time constraints.

However, as cyber threats grow in complexity and the volume of digital evidence increases exponentially, traditional DFIR methods are certainly reaching their limits. A single incident can generate millions of data points across multiple endpoints, cloud environments, and third-party tool integrations. In such cases, the bottleneck is no longer access to data, but the ability to extract timely, actionable intelligence from it.

Artificial Intelligence (AI), and particularly Large Language Models (LLMs), are emerging as a transformative force within DFIR. While AI will not replace human analysts, at least not in the near future, it can significantly augment their capabilities by surfacing patterns hidden within unstructured data and helping contextualize complex findings. In this article, we examine the evolving role of AI in DFIR, distinguishing it from traditional automation, and highlighting its applications in DFIR.

One of the most common misconceptions in cybersecurity is equating AI with automation. While both are valuable, they are applied to solve different problems.

Automation in DFIR involves rule-based workflows designed to reduce manual effort. Examples include ingesting and parsing logs, correlating IOCs, running predefined YARA rules, or automatically enriching data using various threat intelligence platforms. These processes follow predetermined paths: if X happens, do Y. Automation is predictable, reliable, and efficient, but it is constrained by the logic and rules built into it.

AI, particularly machine learning and LLMs, introduces a new concept. Rather than following explicit rules, AI models learn patterns from data and apply probabilistic reasoning to tasks. For DFIR professionals, this means AI can support deeper analytical tasks such as:

• prioritizing alerts based on historical behavior and risk indicators;
• identifying outliers or novel threat techniques;
• summarizing complex forensic artifacts into human-readable insights;
• interpreting ambiguous evidence in context-sensitive ways.

In essence, automation helps reduce our workload, while AI helps us to improve decision-making, especially when time is of the essence, which in most DFIR investigations is usually the case. Both are necessary to address the persistent gap in cybersecurity talent and the growing volume of cyber incidents, but their roles should not be mistaken.

As we integrate AI into DFIR operations, it becomes obvious that we are not merely using another tool, we are introducing a new kind of team member. This new ‘colleague’ of ours has unique characteristics:

• Speed: AI can process thousands of logs or command-line entries in seconds, clustering similar behaviors and identifying anomalies that would otherwise require hours of manual effort.
• Breadth of Knowledge: Pre-trained LLMs have absorbed vast amounts of documentation, threat reports, and attack patterns, allowing them to recognize obscure commands, uncommon file types, or indicators of compromise with high accuracy. No single person on our team could even come close to having all this in their head.
• Consistency: Unlike human analysts who may get tired, miss details, or interpret data inconsistently under stress, AI maintains a consistent analytical approach across datasets.

That said, AI is not some secret superpower and does come with limitations. It may support context interpretation and assist in evaluating potential outcomes, but it often lacks the nuanced judgment required to fully understand organizational culture, assess broader business impact, or engage stakeholders effectively. AI can be a powerful tool in the decision-making process, but it ultimately complements, rather than replaces human expertise and oversight.

Modern DFIR investigations often begin with overwhelming complexity. A single compromised endpoint may lead to tens of thousands of log entries. A targeted campaign affecting a multinational environment could involve data from dozens of systems across multiple countries. This scale creates significant investigative friction.

The traditional model, where analysts manually triage, investigate, and correlate, simply does not scale. Not in real time. Not with the current workforce gap. This is where AI has begun to demonstrate tangible value.

We recently conducted an experiment with test data where we applied clustering and embedding techniques to group command-line executions from event logs. Instead of manually reviewing 1,500 entries, the AI condensed them into behavioral clusters, highlighting anomalies such as privilege escalation attempts, lateral movement, or persistence mechanisms. What would have taken a lot of time to execute by hand was reduced to a focused set of hypotheses for validation, freeing up analysts to concentrate on higher-value, strategic analysis.

But clustering alone is not enough. The next step is understanding what each cluster means, and this is where LLMs play a key role.

By feeding sample entries into an LLM, we generated contextual summaries for each cluster. These summaries not only described what happened, but also offered plausible threat tactics (e.g., MITRE ATT&CK techniques), suggested mitigation actions, and raised additional questions for follow-up investigation.

With the aid of AI, we were able to transform a noisy dataset into a more coherent narrative, supporting our shift from log readers to storytellers.

Incident response is fundamentally a race against time. Every minute of an attacker being able to roam about freely increases the risk of data exfiltration, lateral movement, and service disruption. Historically, analysts relied on signatures, rule-based detections, and manual correlation to respond to incidents.

AI introduces a new layer of responsiveness:

• Dynamic baselining: AI models can learn what ‘normal’ looks like for a given environment and alert on deviations in behavior—not just static rules.
• Entity linking: AI can associate seemingly unrelated events across systems to uncover an attacker’s lateral movement path.
• Contextual enrichment: LLMs can analyze alert content and generate summaries, explain tactics, and provide first-pass recommendations.

In practice, this means faster triage, fewer false positives, and more actionable alerts. Analysts can prioritize high-risk cases and communicate with stakeholders more effectively, reducing the overall incident response lifecycle.

We are still in the early stages of AI adoption in DFIR. While use cases such as clustering, summarization, and enrichment are maturing, the long-term horizon includes:

• real-time forensic analysis during live response;
• AI-driven malware classification and behavior prediction;
• adaptive playbooks that evolve based on attacker behavior;
• predictive threat hunting using anomaly detection at scale.

These advancements will not make human analysts obsolete. On the contrary, they will make human judgment even more central by clearing the noise and elevating the signal.

At KPMG, we are actively looking at ways of integrating AI into our DFIR workflows, not just as a technology enabler, but as a strategic partner in delivering faster, smarter, and more resilient incident response services. The aim is to increase efficiency, reduce investigation time, and enhance the quality of insights provided to clients during critical incidents.