This question comes up constantly, in both directions — some people dismiss it entirely, others are convinced their career is about to disappear. The honest answer is more nuanced than either extreme.
AI genuinely speeds up repetitive, time-consuming work — summarizing logs, doing a first pass on evidence, flagging likely-relevant findings out of a large volume of raw data. That's a real, meaningful shift in how the work gets done, not a hypothetical future change.
For professionals already in the field, this mostly shows up as a productivity multiplier on tasks that used to consume disproportionate time, freeing up capacity for the higher-judgment parts of the work.
Judgment calls under ambiguity, understanding business context, and verifying that an AI-flagged finding is actually real and actually matters — that still requires a person with real expertise. Treating AI output as a conclusion rather than a starting point is a genuine risk, not just an abstract concern.
Client and stakeholder trust also still runs through people, not tools. A report or recommendation carries more weight, and gets acted on with more confidence, when it comes with a named, accountable specialist standing behind it.
The professionals most at risk are those doing purely repetitive, low-judgment work with no adjacent skills being developed alongside it. Building genuine judgment and hands-on depth, not just tool familiarity, is what keeps a career resilient as the specific tools in use continue to change.
Practically, this means treating AI tools as something to learn and use effectively, rather than something to avoid or fear — professionals who integrate these tools well tend to become more valuable, not less, because they can move faster on the routine parts of the job.
Not necessarily — repetitive work is often how junior professionals build the pattern recognition that leads to senior judgment. The risk is staying in purely repetitive work indefinitely, not starting there.
Learning to use these tools effectively tends to make professionals more valuable, not less — it's ignoring them, not adopting them, that carries the bigger long-term career risk.
Roles requiring significant judgment under ambiguity, client relationship management, or physical/hands-on work (like some penetration testing scenarios) tend to be less immediately affected than purely data-processing-heavy tasks.