Where to draw the line on AI: Lessons from digital forensics | Daily Reports Online
Artificial intelligence is becoming part of almost every workflow. Developers are using it to write and test code, customer support is turning to it to handle routine queries, and security teams are using it to analyze alerts and support investigations. Digital forensics and incident response (DFIR) is no exception.
DFIR Curriculum Lead and Head of Faculty at SANS Institute.
But as digital forensics work has evolved over the last few decades, one lesson remains consistent. Tools can support investigations, but investigators remain responsible for the quality of the conclusions they reach. As AI becomes increasingly integrated into workflows across industries, the challenge is understanding where AI tools can assist and where human expertise remains essential – and importantly, where organizations should draw the line.
Where AI adds value
For digital forensics and incident response teams, some of AI’s most promising applications are also its most practical.
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With 90 percent of criminal investigations and prosecutions now involving a digital element, DFIR teams are being asked to process enormous volumes of data. As of February of this year, more than 20,000 devices remain in the digital forensics backlog in England and Wales alone.
Data is spread across endpoints, cloud environments, communications platforms and user accounts, and AI can help investigators triage. It can process that information more efficiently and help to identify potential leads, spot connections between datasets, and highlight other sources of evidence that may require further examination.
For example, it can help investigators correlate relevant activity across multiple devices, accounts and data sources, supporting the reconstruction of events timelines that would otherwise take days to piece together manually. It can also help practitioners organize information and structure reports, allowing them to spend less time on administrative work and more time on analysis.
Used appropriately, AI can reduce the time spent on manual, repetitive work and allow skilled practitioners to spend more time on the analysis and decision-making that moves their work forward.
The danger of treating AI as an answer
The challenge is that AI often appears so authoritative that people become overly reliant on its outputs.
One of the most important lessons from digital forensics is that every tool has limitations. Experienced investigators understand that forensic tools can be wrong. AI is no different. The greatest risk of AI is using it without having the foundational knowledge needed to recognize when it is wrong.
AI is like your junior analyst on their best day. It can identify patterns, highlight areas of interest and help move work forward. But no organization would allow a junior analyst to make the final decision in a sensitive investigation without oversight and accountability. AI should be approached in much the same way.
Problems arise when organizations treat AI-generated outputs as answers rather than something that needs to be validated by someone with the expertise to challenge them. AI can help you reach a conclusion, but it shouldn’t be the conclusion in and of itself.
Knowing where to draw the line
There are many areas where AI can accelerate and enhance work, but there are some areas where AI should never be used. Organizations need to pay attention to where those boundaries lie.
In digital forensics, AI can help identify information that warrants further examination. It can support analysis and help investigators work more efficiently. But it shouldn’t be blindly trusted to reach investigative conclusions or determine innocence or guilt.
Those decisions carry legal, professional and life-changing consequences, making human judgement and accountability non-negotiable.
The same principle extends beyond digital forensics. Organizations in every sector are now deciding how much responsibility should be handed to AI. The answer will vary depending on the use case, but the underlying question remains the same – what safeguards are in place to prevent people becoming overly reliant on AI outputs, and how will they know when those outputs are wrong?
Building frameworks for responsible adoption
These are exactly the questions currently being addressed by the new practical frameworks for the use of AI in digital forensics and incident response. They encourage organizations to consider how much risk a task carries, how outputs will be reviewed, and where human oversight should remain mandatory.
Using AI in DFIR requires strict governance because the stakes are so high. Mistakes are taken seriously when someone’s life is on the line, or a wrongful conviction could take place. However, it shouldn’t take consequences this severe for organizations to work towards protecting the integrity of their decision-making and ensuring humans remain in the driving seat.
Rather than racing to embed AI into every process, organizations should establish governance frameworks first – including what AI should never be in charge of – then start with low-risk use cases where outputs can be easily reviewed or reversed. From there, they can expand capability and explore how AI can help their teams do their jobs better, acting as a force-multiplier rather than the final arbiter.
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