The SOC Skills Teams Need When AI Takes the First Pass

An analyst opens an alert and finds that AI has already prepared a timeline, summarised the evidence, and recommended escalation. 

The summary looks convincing, but the analyst still needs to establish whether the evidence supports it and whether the recommendation makes sense. 

As AI handles more of the initial investigation, those skills become increasingly important. For SOC leaders, the priority is developing those capabilities through practical work that reflects what analysts encounter during investigations. 

Which SOC Skills Matter Most When AI Assists Investigations?

AI can help analysts gather evidence and prepare an initial assessment. 

To use that assistance effectively, practitioners need a strong understanding of the activity behind an alert and the confidence to challenge unsupported conclusions. 

The following five skills provide a practical foundation. 

1. Verify Findings Against Source Evidence

AI can summarise logs and connect events, but analysts must be able to trace important claims back to the original records. 

Consider a generated summary stating that an attacker moved laterally between two hosts.

The analyst needs to examine the relevant authentication, endpoint, and network activity to determine whether that conclusion is supported. A successful login may confirm access to another system, but further evidence may be needed to establish what happened during the session. 

That requires practical knowledge of:

• Log analysis: Understanding event fields, timestamps, and relevant sources.
• SIEM queries: Retrieving supporting evidence and investigating related activity.
• Telemetry gaps: Recognising when missing data prevents a reliable conclusion. 

2. Understand the Behaviour Behind an Alert

Finding a suspicious event is only part of an investigation. Analysts also need to understand what the activity could mean. 

For example, an unusual WMI (wmic.exe) process might indicate malicious execution or a legitimate administrative task. 

The analyst should examine the command, its parent process, the affected user, and related activity before deciding how to classify it.

This requires a working understanding of operating systems, authentication, networking, and common attacker techniques. 

Those foundations also help detection engineers evaluate AI-generated rules. A rule might be syntactically correct but miss an important attack variation or generate too many false positives. 

3. Challenge AI Recommendations With Confidence

AI can produce convincing explanations even when parts of the answer are inaccurate.

NIST’s Generative AI Profile identifies risks including automation bias, over-reliance, and confidently presented incorrect information. 

Analysts need a consistent approach to reviewing recommendations rather than relying on how convincing the explanation sounds.

Before accepting a finding, they should establish:

• What evidence supports the conclusion?
• Which assumptions could change the assessment?
• Is there enough information to recommend the next action?

For example, an AI recommendation to close an alert may assume that the activity was authorised. If that assumption has not been verified, the analyst needs to investigate further. 

4. Assess the Consequences of Response Actions

When an investigation leads to containment or remediation, analysts need to understand the potential operational impact. 

Isolating an employee workstation carries different consequences from isolating a shared management server. Disabling a compromised administrator account may also interrupt legitimate incident response activities.

Responders should understand the affected asset, its dependencies, and the organisation’s approval requirements before recommending action.

Give practitioners scenarios where several containment options are available. Ask them to explain their preferred approach, identify the risks, and determine whether additional authorisation is required.

Then change the scenario. For example, reveal that the affected endpoint supports a critical business service and observe whether the responder adjusts their decision.

This develops the judgment needed to balance containment objectives with operational risk.

5. Communicate Findings and Uncertainty Clearly

An investigation can lose value when the next responder receives an incomplete handover.

AI-generated summaries may help with documentation, but analysts still need to communicate which findings are confirmed, what remains uncertain, and what should happen next.

For example, an investigation might establish malicious activity on one endpoint without confirming whether other systems were affected. 

A useful escalation should make that distinction clear and identify the next evidence needed to establish the incident's scope. 

Test this skill by giving a completed case to another analyst without a verbal explanation. Can they understand the findings, continue the investigation, and identify the next decision?

How to Build These Skills Through Practical Training

Start With Real SOC Workflows

Review the investigation and escalation tasks your team handles most often. Identify where analysts struggle, which decisions frequently require correction, and where AI is already assisting the process.

Then match each gap to a practical exercise. 

This helps managers evaluate practical ability rather than relying solely on course completion or tool familiarity. 

Give Junior Analysts Time to Investigate Independently

Junior analysts still need to learn how investigations are built from the underlying evidence. If AI always prepares the initial account, they may have fewer opportunities to develop that process themselves.

Use selected training cases where analysts investigate the logs and reach a conclusion before reviewing the AI-generated summary. 

Compare the two assessments and discuss the differences. This gives practitioners experience with the underlying investigation while helping them understand where AI assistance is useful.

Senior analysts can support that development by reviewing the decisions behind a case, including which evidence changed the conclusion and what remained uncertain. 

Practise With Realistic Variations

Repeatedly solving the same familiar scenario may improve recall without demonstrating that a skill transfers to new incidents.

Use exercises that introduce different conditions, such as incomplete telemetry, conflicting evidence, legitimate activity resembling an attack, or a response decision involving a critical asset.

Include both AI-assisted and independent investigations where appropriate.

Practitioners should be able to explain their reasoning in either setting, including when the available evidence is insufficient to reach a firm conclusion.

For team exercises, include shift handovers and escalation decisions to see how well individual skills translate into coordinated work. 

How to Measure Whether Your Team Is Improving

Training should give SOC leaders a clearer picture of what analysts can do and where further development is needed.

Start by assessing performance on representative cases before introducing targeted training. Repeat the assessment with different scenarios to establish whether the improvement carries over.

Focus on Decision Quality

A practical measurement framework could include: 

Measure speed alongside accuracy. A quick decision that overlooks a compromised account or misidentifies an asset creates additional work and risk.

Review accepted AI recommendations as well as rejected ones. A low correction rate may indicate accurate output, but it could also mean analysts are missing errors.

Consider case difficulty when comparing results. Complex investigations may require more time even when an analyst handles them well. 

Use the Findings to Guide Further Practice

Review performance regularly and identify the decisions that cause the most difficulty. 

For example, if analysts consistently identify suspicious endpoint activity but struggle to establish the wider incident scope, focus the next training cycle on investigation across multiple evidence sources.

If individual investigations are accurate but handovers are incomplete, introduce team exercises that test communication and escalation.

NIST's incident response guidance supports the use of tests and exercises to improve response capabilities.

This approach helps teams build skills around observed gaps and gives managers evidence of progress.

Build Stronger SOC Analysts With TryHackMe

As AI takes on more investigative work, your SOC needs analysts who can verify its findings, recognise genuine threats, and make informed decisions.

TryHackMe SOC Simulator gives your team repeated practice with realistic alerts and investigations, helping practitioners strengthen the skills they use during everyday SOC operations.

With performance feedback, you can identify where analysts struggle, target training more effectively, and track their progress over time. Give your analysts more practice with the decisions that matter.