It's 4:48 PM on a Thursday.
The Security Operations Center (SOC) team flags login attempts from six countries. Internal files are moving, and a forgotten server just went live again. Your team is already responding, but the pace is unnerving.
Moments like this raise a difficult question: Could we have caught this earlier?
Artificial Intelligence is often positioned as the answer to faster detection and response. But the truth is more complex. For security leaders navigating real risk, not hypothetical return, it’s time for a grounded look at AI in incident response.
Incident response is the structured process an organization follows to detect, contain, investigate, and recover from cybersecurity incidents. It is how teams limit damage when data is compromised, systems are disrupted, or malicious activity enters the network.
AI can support security teams, but only in specific ways. It is not a replacement for skilled analysts or a shortcut to being fully prepared.
AI models can detect deviations from normal user or system behavior. This may help:
Limitation: False positives are common. Without proper tuning, AI may create too many unnecessary alerts, making it harder to identify real threats.
Some tools enable AI-driven containment, such as:
Limitation: Over-automation may lead to accidental lockouts or business disruption. Human oversight is still required.
AI may assist in:
Limitation: Value depends on clean, consistent, and complete data.
If playbooks, roles, and tooling are still being defined, AI could add complexity instead of clarity.
AI tools require ongoing tuning, retraining, and monitoring. If your team is at capacity, this becomes another burden.
AI works best when used to address a clear gap, such as:
|
Condition |
AI Might Help |
|
Stable workflows |
Yes |
|
Clear goals (e.g. reduce alert fatigue) |
Yes |
|
No tuning capacity or context |
No |
|
Lacking fundamentals |
No |
AI is not a cybersecurity strategy. It is a tactic. Use it to support mature processes, not to replace them.
Responsible adoption matters. Introducing AI into your incident response toolkit should be done with clear purpose, oversight, and accountability. When AI is deployed without defined roles, governance, or training, it can expose new vulnerabilities rather than mitigate existing ones. Leaders should evaluate not just whether AI is possible, but whether it is practical, sustainable, and aligned with their team’s capabilities and risk posture.
Galson Research helps you evaluate where AI fits in your cybersecurity ecosystem and where it does not.
We provide you with:
You do not need hype or flashy language. You need clarity, context, and a team that makes tech make sense.
AI in incident response refers to using machine learning or automated logic to support detection, containment, or analysis of cybersecurity incidents.
No. AI can support specific tasks, but judgment, oversight, and decision-making remain human-led.
It depends on how well it is configured, monitored, and integrated. AI is only effective with clear thresholds and supporting processes.
Some capabilities can be added to existing platforms. Others require dedicated AI-enabled products. Start by identifying your needs before looking at technology.