05 Aug How AI can detect cyber security threats faster

What Is AI Threat Hunting?

Threat hunting is the proactive search for suspicious activity that may not yet have triggered a confirmed security incident. Rather than relying entirely on automatic alerts, an analyst forms a question or hypothesis and searches security data for evidence.

For example:

• Has an employee account logged in from an unusual location?
• Have any devices started running unexpected PowerShell commands?
• Has the same suspicious file appeared elsewhere on the network?
• Are endpoints communicating with a newly identified malicious address?
• Is there unusual activity that could suggest stolen credentials or lateral movement?

AI threat hunting applies artificial intelligence to this process. It can help analyse large volumes of security data, turn everyday questions into structured searches, identify relationships between events and guide the analyst towards the next useful enquiry.

The aim is not to let a generic chatbot make uncontrolled security decisions. Effective AI-powered threat hunting uses AI within a dedicated security platform, with access to relevant organisational data, defined controls and human oversight.

Why Is Traditional Threat Hunting Difficult for SMEs?

Large enterprises may employ dedicated security analysts who spend their time investigating alerts and searching for hidden threats. Many SMEs rely on an IT manager, a small technical team or an outsourced provider that also handles user support, Microsoft 365, devices, networks and business systems.

Traditional threat hunting can therefore be difficult for several reasons:

• Specialist query knowledge: analysts may need to understand complex search languages and data structures.
• Limited time: internal IT teams are already balancing security with day-to-day support and infrastructure management.
• Too much data: endpoints, cloud services and identity platforms can generate more events than a small team can review manually.
• Fragmented tools: relevant evidence may be spread across different consoles and formats.
• Unclear next steps: finding one suspicious event does not automatically reveal the source, scope or correct response.

This is why searches such as “AI cyber security for small businesses”, “AI threat detection for SMEs” and “how can AI help detect cyber threats?” are becoming relevant. IT managers are looking for ways to gain stronger protection without trying to recreate a large enterprise security team internally.

How Does AI Improve Threat Hunting?

It Makes Security Data Easier to Search

Natural-language search allows an IT manager or analyst to ask a question in plain English instead of manually building every query. The AI translates the request into the appropriate search and presents the results.

It Connects Related Security Events

A suspicious login, unknown process and unusual network connection may look unimportant when viewed separately. AI can help correlate related events and build a clearer picture of what happened across users, devices and services.

It Explains Why Activity Matters

Raw alerts can be highly technical. AI can summarise the activity, explain the potential risk and identify the systems or users involved, helping the team decide which incidents need urgent attention.

It Guides the Investigation

Once something suspicious is found, AI can suggest follow-up questions. This helps an analyst investigate where the activity came from, whether it spread and what else may have been affected.

It Automates Repetitive Investigation Work

Agentic AI can progress through multiple investigation stages, gather supporting evidence and produce findings for human review. This reduces the manual workload between an alert being raised and an analyst understanding the likely threat.

It Improves Documentation

AI can help record searches, evidence, summaries and decisions. Clear documentation supports internal handovers, managed security collaboration and post-incident review.

How Does SentinelOne Purple AI Support AI Threat Hunting?

SentinelOne Purple AI is an agentic AI security analyst embedded within the Singularity Platform. It is designed to support triage, investigation and threat hunting using security context from SentinelOne telemetry and supported third-party data sources.

Natural-Language Security Queries

Users can ask Purple AI questions such as “Show me devices running unusual scripts today” or “Has this user connected to any suspicious IP addresses?” Purple AI converts the request into a structured hunt and returns the relevant results.

How this helps an IT manager: advanced searches can be launched without memorising a specialist query language, reducing the barrier to proactive threat hunting.

Intelligent Alert Summaries

Purple AI can summarise complex activity and explain the potential risk in clearer language. This gives the user a faster understanding of what happened, why the event may be suspicious and which assets are involved.

How this helps an SME: a smaller team can prioritise important alerts more quickly and communicate the issue clearly to decision-makers.

Suggested Follow-Up Hunts

An initial search may identify a suspicious process but not reveal its full impact. Purple AI can suggest further questions that explore related users, files, devices and connections.

How this helps: the investigation is less likely to stop at the first result, and less-experienced users receive guidance on what to examine next.

Agentic Investigation

Purple AI can progress beyond a single question by gathering evidence across the available security data, analysing relationships between events and constructing an investigation for review. Its agentic capabilities are intended to reduce repetitive triage while analysts retain visibility and control.

How this helps: the time between detection and a meaningful verdict can be reduced, allowing the team to focus on the response rather than manually assembling every piece of evidence.

Cross-Platform Security Context

Purple AI reasons across data normalised through the Open Cybersecurity Schema Framework, known as OCSF. This can include native SentinelOne information and supported third-party sources available within the platform.

How this helps: analysts can investigate related activity through a more consistent view instead of repeatedly switching tools and query formats.

Threat-Hunting Quick Starts

Guided starting points help users launch common threat hunts without beginning with a blank search box. The results can then be explored through natural-language follow-up questions.

How this helps: IT teams can hunt proactively for suspicious behaviour, even when they do not begin with a confirmed security alert.

Investigation Notebooks and Reporting

Purple AI can preserve searches, results and summaries within collaborative investigation notebooks. This creates a clearer record of what was investigated, which evidence was found and how the decision was reached.

How this helps: findings can be handed to a colleague or managed security analyst without the investigation having to be recreated from the beginning.

How AI Threat Hunting Helps IT Managers and SMEs

How Can an SME Integrate AI into Its Cyber Security?

Integrating AI into cyber security should be treated as a controlled improvement to an existing security strategy, not as a replacement for every tool or process.

1. Assess the current risks. Identify the devices, accounts, cloud services and business systems that need protection.
2. Establish reliable endpoint visibility. AI threat hunting is most useful when the underlying security platform receives relevant, high-quality telemetry.
3. Define practical hunting goals. Begin with questions linked to genuine risks, such as unusual account access, malicious scripts, suspicious files or potential ransomware behaviour.
4. Use AI to accelerate investigation. Allow the platform to search, correlate, summarise and recommend the next steps.
5. Retain human control. Important containment or remediation decisions should consider the wider business impact.
6. Create an escalation process. Decide who will respond when a high-risk incident is discovered, including evenings, weekends and staff absence.
7. Review and improve. Use investigation findings to strengthen policies, controls, staff awareness and future threat hunts.

For an SME without its own security team, a 24/7 Managed Detection and Response service can provide the human monitoring and incident expertise needed alongside AI-assisted technology.

AI Threat Hunting in Practice: A Suspicious PowerShell Attack

Imagine an employee opens an email attachment and their laptop begins running an unusual PowerShell command. Endpoint protection detects suspicious behaviour, but the IT manager still needs to understand what happened and whether other devices are affected.

Using Purple AI, the investigation could include:

1. Ask Purple AI to summarise the alert and explain why the command is suspicious.
2. Identify the user, device, parent process, files and network connections involved.
3. Search every protected endpoint for the same file, command or behaviour.
4. Check whether any affected devices contacted suspicious external addresses.
5. Use suggested follow-up hunts to investigate possible credential theft or lateral movement.
6. Review a compiled investigation and recommended response actions.
7. Record the findings and escalate the incident to the internal or managed security team.

This is the practical value of integrating AI into threat hunting. The AI does not merely label an alert; it helps the user investigate the wider activity and reach a more informed decision faster.

Does AI Replace an IT Manager or Security Analyst?

No. AI can analyse data, accelerate searches, summarise findings and recommend actions, but it does not remove the need for human judgement.

An automated recommendation to isolate a server may be technically appropriate but could also interrupt a critical business service. An experienced person must understand that context, validate the evidence and decide how to respond safely.

AI threat hunting, Endpoint Detection and Response and Managed Detection and Response also perform different roles:

• Endpoint Detection and Response monitors devices and detects or responds to suspicious behaviour.
• AI threat hunting with Purple AI helps teams search, investigate, understand and document security activity.
• Managed Detection and Response supplies human analysts who continuously monitor, investigate and respond on the organisation's behalf.

Used together, they provide automated protection, faster AI-assisted investigations and experienced human oversight. Our guide to EDR vs MDR explains the difference in more detail.

What Should an IT Manager Look for in an AI Cyber Security Solution?

Before choosing an AI-powered security platform, consider:

• Does the AI work with genuine security telemetry from your environment?
• Can it explain findings and show the supporting evidence?
• Can users search in natural language and inspect the underlying query?
• Can it investigate across endpoints, identity, cloud and supported third-party data?
• Are important response actions governed by suitable human controls?
• How is business and security data protected?
• Who monitors and responds when the internal IT team is unavailable?
• Can the solution be managed and supported as the organisation grows?

The most effective solution is not necessarily the one that uses the most AI terminology. It is the one that turns security data into faster, clearer and safer decisions.

AI-Powered Cyber Security with Digital Office Group

Digital Office Group helps UK SMEs and larger organisations strengthen cyber security using SentinelOne, proactive IT management and managed IT services.

We can assess your current protection, help deploy the appropriate SentinelOne solution and combine Purple AI threat hunting with 24/7 Managed Detection and Response. This gives your organisation AI-assisted analysis backed by experienced security professionals who can investigate and respond when a genuine threat is identified.