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AI exposure report
Will AI replace Information Security Analysts?
Many of your tasks are within reach of AI. The role will change, so it pays to act now.
Task exposure to today's AI
High exposure
This is the occupation's typical task mix. Your own score can be up to 30 points higher or lower depending on how you work: routine share, people contact, judgment and how much you already use AI.
Get your personal scoreWill AI replace Information Security Analysts?
Not entirely. Today's AI tools can take on about 53% of the typical work in this occupation, mostly: document computer security and emergency measures policies, procedures, and tests. The parts that stay human include: Perform risk assessments and execute tests of data processing system to ensure functioning of data processing activities and security measures.; Monitor use of data files and regulate access to safeguard information in computer files.; Confer with users to discuss issues such as computer data access needs, security violations, and programming changes..
People who learn to use AI for the automatable parts keep the edge. The 3-phase plan below shows where to start.
Which tasks AI can take on
The largest tasks in this occupation, their share of the working week, and how much of each today's AI tools can do.
+ 5 more tasks (34% of the week) in the full test.
Your human edge
- Perform risk assessments and execute tests of data processing system to ensure functioning of data processing activities and security measures.
- Monitor use of data files and regulate access to safeguard information in computer files.
- Confer with users to discuss issues such as computer data access needs, security violations, and programming changes.
AI tools to try
Coding assistants (GitHub Copilot, Claude Code, Cursor)ChatGPT / Claude for design and debuggingAI test and documentation generatorsRelated roles
A 3-phase AI learning plan
Phase 1
Pair programming with AI
- Use a coding assistant for boilerplate, tests and refactoring.
- Give it context: project rules, examples, small steps.
- Review AI code like a colleague's pull request.
Project
Use a coding assistant to rewrite one manual log-review or access-audit script into tested Python, with unit tests and a reviewed diff, and log hours saved vs. the old script.Phase 2
AI in your systems
- Call an LLM API with structured output.
- Retrieval over your own documents or data.
- Evaluate quality, cost and latency before shipping.
Project
Build a retrieval tool over your sanitized security policies and CVE feeds that returns structured JSON risk ratings for 20 new advisories, measuring accuracy, cost and latency per query.Phase 3
Production-grade AI
- Evals and regression tests for prompts.
- Security: prompt injection, data leaks, permissions.
- Monitoring and cost control.
Project
Harden that advisory triage tool with a 30-case eval set, prompt-injection and data-leak tests, least-privilege API keys, and a token-cost dashboard; report pass rate before and after.