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AI Automation Index
Will AI Replace Digital Forensics Analysts?
Task-level automation exposure for Digital Forensics Analysts: 20 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
65%
weighted task index
20 of 20 tasks scored
Median Salary
$116,580 / year
BLS OEWS, May 2025 — this exact occupation
Employment Level
Very High
435,370 people hold this job — size, not growth
Projected 2025–35
+5.1%
employment change
27,000 openings a year · BLS line item
Pay by state
BLS OEWS publishes 54 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.
$60,470 – $156,590
Highest medians
District of Columbia $156,590
Maryland $144,680
Colorado $139,580
Virginia $139,030
Delaware $137,470
Lowest medians
Puerto Rico $60,470
Tennessee $76,470
Guam $76,770
Louisiana $77,000
North Dakota $78,330
State figures come from the 15-1299 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.
What the score is made of
Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.
Automation potential 65%
Importance-weighted mean of the scored tasks. This is the headline index.
Physical work 5%
Share of weighted tasks in hands-on or machine-operating work activities.
Judgment & influence 38%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 29%
Share in documenting, recording, and administrative activities.
Rewritten by O*NET 0%
Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.
Consequence of error 20/100
Low
O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.
Constituent Task Mix
20 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.
High exposure (≥ 60% of the task is automatable) 62%
13 tasks
Medium exposure (≥ 35%) 33%
6 tasks
Low exposure (below 35%) 5%
1 tasks
How would you survive?
Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.
Simulate Upskilling →Constituent Tasks
#21792 · Core · 35% automatable
Duplicate digital evidence to use for data recovery and analysis procedures.
visual inspection — Capturing relevant images in field is human act
Record images needed to address work issues.
#21799 · Core · 85% automatable
Preserve and maintain digital forensic evidence for analysis.
data analysis — Maintain files and records in structured systems
Maintain records, documents, or other files.
#21786 · Core · 85% automatable
Adhere to legal policies and procedures related to handling digital media.
data analysis — AI monitors digital security logs and alerts
Monitor the security of digital information.
#21804 · Core · 44% automatable
Write reports, sign affidavits, or give depositions for legal proceedings.
text generation — Reports/evaluations drafted; human owns judgment
Write reports or evaluations. Testify at legal or legislative proceedings.
#21797 · Core · 58% automatable
Perform forensic investigations of operating or file systems.
data analysis — Record screening aided, investigative judgment human
Examine records or other types of data to investigate criminal activities.
#21789 · Core · 82% automatable
Create system images or capture network settings from information technology environments to preserve as evidence.
text generation — Technical documentation compilation, human reviews accuracy
Compile technical information or documentation.
#21795 · Core · 80% automatable
Maintain knowledge of laws, regulations, policies or other issuances pertaining to digital forensics or information privacy.
information retrieval — AI tracks and summarizes regulations; human confirms
Maintain knowledge of laws or regulations.
#21805 · Core · 72% automatable
Write technical summaries to report findings.
text generation — Reports/evaluations drafted; human owns judgment
Write reports or evaluations.
#21796 · Core · 58% automatable
Perform file signature analysis to verify files on storage media or discover potential hidden files.
data analysis — Record screening aided, investigative judgment human
Examine records or other types of data to investigate criminal activities.
#21801 · Core · 78% automatable
Recover data or decrypt seized data.
text generation — Translation well-handled; human reviews nuance
Translate information for others.
#21790 · Core · 60% automatable
Develop plans for investigating alleged computer crimes, violations, or suspicious activity.
decision support — Sequence planning aided; production constraints human-verified
Plan production or operational procedures or sequences.
#21787 · Core · 58% automatable
Analyze log files or other digital information to identify the perpetrators of network intrusions.
data analysis — Record screening aided, investigative judgment human
Examine records or other types of data to investigate criminal activities.
#21794 · Core · 30% automatable
Maintain cyber defense software or hardware to support responses to cyber incidents.
physical manipulation — Software side AI-assisted; hardware maintenance is hands-on
Maintain computer equipment or software.
#21798 · Core · 68% automatable
Perform web service network traffic analysis or waveform analysis to detect anomalies, such as unusual events or trends.
data analysis — Traffic data analysis is computational and well-specified
Examine records or other types of data to investigate criminal activities. Analyze traffic data.
#21791 · Core · 52% automatable
Develop policies or requirements for data collection, processing, or reporting.
text generation — Drafts operational policy; accountability and approval human
Establish operational policies.
#21803 · Core · 72% automatable
Write cyber defense recommendations, reports, or white papers using research or experience.
text generation — Reports/evaluations drafted; human owns judgment
Write reports or evaluations. Recommend changes to improve computer or information systems.
#21793 · Core · 64% automatable
Identify or develop reverse-engineering tools to improve system capabilities or detect vulnerabilities.
data analysis — IT resource sizing from requirements; human approves
Identify information technology project resource requirements. Develop technical methods or processes.
#21788 · Supplemental · 70% automatable
Conduct predictive or reactive analyses on security measures to support cyber security initiatives.
data analysis — Security analysis aided; human validates findings
Analyze security of systems, network, or data.
#21802 · Supplemental · 84% automatable
Write and execute scripts to automate tasks, such as parsing large data files.
data analysis — Code entry is routine data input
Write computer programming code. Enter codes or other information into computers.
#21800 · Supplemental · 71% automatable
Recommend cyber defense software or hardware to support responses to cyber incidents.
decision support — Drafts sound recommendations; human validates against real systems
Provide recommendations to others about computer hardware. Recommend changes to improve computer or information systems.
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How this was scored
- 1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
- 2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
- 3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
- 4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.
Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.
Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast
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