Seventy-eight per cent of organisations had not taken meaningful steps towards EU AI Act compliance as of April 2026, according to research cited by Responsible AI Labs. More than half also lacked a basic inventory of the AI systems used across their organisation.
These gaps point to a broader security challenge. Many businesses are adopting generative AI faster than they can establish the governance, visibility and monitoring needed to manage it safely. Employees may use unauthorised AI tools, applications may connect to external models through APIs, and sensitive information may enter prompts without security teams being aware.
At the same time, threat actors can use AI to make phishing, social engineering and malicious automation faster and more convincing. AI applications themselves can also be targeted through techniques such as prompt injection, model abuse and attempts to extract sensitive information.
Microsoft Sentinel AI threat detection can help organisations bring relevant security data together, identify suspicious behaviour and investigate activity across identities, applications, cloud services and AI environments. However, Sentinel is not a standalone solution for every AI risk. Effective monitoring depends on collecting the right telemetry, establishing normal behaviour and developing detection logic that reflects how the organisation uses AI.
This guide explains how Microsoft Sentinel can support AI security monitoring, where its capabilities fit and why expert oversight remains important.
Key Takeaways
- AI-related security monitoring requires visibility across identities, applications, APIs, cloud services and approved or unauthorised AI tools.
- Microsoft Sentinel can analyse relevant telemetry and help security teams identify suspicious behaviour associated with AI applications and their supporting infrastructure.
- User and Entity Behaviour Analytics can highlight deviations from established activity patterns.
- Prompt injection detection depends on access to meaningful application or gateway logs and carefully developed detection logic.
- Managed detection and response can help organisations address skills gaps, improve detection quality and maintain continuous oversight.
- Microsoft Sentinel can contribute evidence and monitoring capabilities to an AI governance programme, but it does not establish regulatory compliance by itself.
The Changing AI Threat Landscape
Cloud adoption, remote access, software-as-a-service applications and generative AI have expanded the environment that security teams need to monitor. The traditional network perimeter is no longer the only boundary that matters. Identities, APIs, data flows, cloud workloads and AI services are now equally important parts of the security picture.
AI introduces two related categories of risk.
The first is the use of AI by threat actors. Generative AI can support more persuasive phishing messages, automate reconnaissance and accelerate the creation or modification of malicious content. Traditional security rules remain valuable, but fixed indicators may not provide enough context to identify activity that changes frequently or resembles legitimate behaviour.
The second category covers attacks against AI applications and the resources supporting them. These risks can include prompt injection, abuse of model permissions, unauthorised access to AI services, exposure of sensitive prompt data and manipulation of application workflows.
AI threat detection therefore requires more than searching for a single malicious file or known network address. Security teams need to correlate activity across users, devices, applications, workloads and data sources to understand whether apparently separate events form part of a wider incident.
Microsoft Sentinel supports this approach by centralising relevant security telemetry and helping analysts investigate relationships between events. Its effectiveness still depends on data quality, appropriate configuration and a clear understanding of the organisation’s AI architecture.
Generative AI Risks and Shadow AI
Shadow AI occurs when employees use AI tools or services without appropriate organisational approval or oversight. This may include placing company information into public generative AI services, creating unofficial AI workflows or connecting business data to third-party models.
The main problem is limited visibility. Security and governance teams cannot manage tools they do not know are being used. Unauthorised AI activity can lead to:
- Sensitive information being submitted through prompts
- Intellectual property being processed outside approved systems
- Inconsistent retention and access controls
- AI-generated content being used without suitable review
- Unapproved applications connecting to organisational data
- Limited evidence for audits or incident investigations
Identity and cloud application telemetry can help organisations identify which users and services are accessing AI platforms. Microsoft Entra and Microsoft Defender for Cloud Apps may form part of this visibility where the relevant activities and applications are supported and configured.
This information can then be brought into Microsoft Sentinel alongside other security data. Analysts can investigate unusual access patterns, activity involving privileged accounts or connections that differ from the organisation’s normal use of approved technology.
Visibility should be accompanied by clear governance. CyberOne’s Managed Data Security Services can help organisations understand where sensitive data resides and apply controls that support responsible AI adoption.
How Microsoft Sentinel Supports AI Threat Detection
Microsoft Sentinel is a cloud-native security information and event management platform. It can collect and analyse security data from Microsoft services, cloud environments and supported third-party sources.
For AI security monitoring, relevant data may come from:
- Identity and authentication platforms
- AI application gateways
- API management services
- Cloud workloads
- Web application firewalls
- AI application audit logs
- Data security products
- Endpoint and network security tools
- Custom application logs
The specific data available will depend on how the AI service has been built and whether the service, gateway or application exposes suitable diagnostic information.
Microsoft Sentinel can help security teams search this data, create analytics rules and investigate related events. Normalising records into consistent formats can also make it easier to analyse information from multiple sources.
The Advanced Security Information Model provides standard schemas that can be used to normalise supported security data. Where appropriate parsers and mappings are available, this can reduce the effort required to compare events generated by different technologies.
However, normalisation does not automatically make an AI application secure. Teams must first determine which events matter, confirm that the required logs are available and design detection scenarios based on realistic risks.
Using Microsoft Sentinel UEBA and Incident Correlation
Microsoft Sentinel UEBA, or User and Entity Behaviour Analytics, uses activity data to help establish patterns associated with users, hosts and other entities. It can then highlight activity that differs from those established patterns.
In an AI environment, potentially relevant changes might include:
- An employee accessing an AI service from an unusual location
- A service identity making an unexpected number of API requests
- A normally low-privilege account attempting to reach sensitive resources
- An AI application accessing information outside its expected scope
- An account changing its behaviour after a suspicious sign-in
- A sudden change in the frequency or timing of AI-related activity
A deviation is not automatically malicious. It provides context that analysts can use alongside other evidence.
Sentinel’s incident correlation capabilities can also bring related alerts into a common investigation. Instead of reviewing every event separately, analysts can examine the identities, devices, applications and resources involved in the incident.
Microsoft Security Copilot may further support investigations by helping analysts interpret security information and work with natural-language summaries. Security teams should still validate outputs and make response decisions using the underlying evidence.
For organisations that need continuous monitoring, Managed Microsoft Sentinel can provide the operational expertise needed to refine detection logic, investigate alerts and connect technical findings to business risk.
Detecting Prompt Injection and Model Abuse
Prompt injection is an attempt to influence an AI application through instructions that conflict with its intended behaviour. An attacker may try to override system instructions, manipulate connected tools, retrieve restricted information or cause the application to perform an unauthorised action.
Prompt injection detection is difficult because prompts are written in natural language and legitimate requests can vary significantly. A fixed list of words will not identify every attack. Pattern matching may detect known indicators, but it needs to form part of a broader monitoring strategy.
Organisations should first identify where useful telemetry can be collected. Depending on the architecture, this might include logs from:
- AI application gateways
- API management layers
- Orchestration services
- Application audit systems
- Identity platforms
- Data access controls
- Content safety tools
- Model or service diagnostic logs
Microsoft Sentinel can ingest custom logs where a suitable collection method is available. Analysts can then use Kusto Query Language to search for suspicious combinations of activity.
Relevant indicators could include repeated policy violations, unusual request structures, unexpected tool calls, abnormal access to sensitive information or activity associated with a compromised identity.
Regular expressions can assist with pattern matching, but they should not be presented as a complete prompt injection control. Context, identity, application behaviour and resulting system actions are often more useful than individual words in a prompt.
CyberOne’s AssureAI framework can help organisations assess AI risks, improve governance and align security monitoring with the way AI is used across the business.
Monitoring AI Interactions and Data Exfiltration
Data exfiltration through an AI application may not appear as a single large transfer. An attacker could attempt to retrieve information gradually or use requests that resemble normal activity.
Effective AI security monitoring should therefore consider both volume and behaviour. Security teams may need to investigate:
- Repeated requests for restricted information
- AI identities accessing unusual repositories
- Changes in the sensitivity of data being retrieved
- Requests outside the application’s expected business purpose
- Activity performed at unusual times
- Attempts to bypass established application controls
- Unexpected changes to permissions or API credentials
- Data access followed by external transmission
Automated response playbooks can support containment where a scenario has been carefully tested. Depending on the available integrations, an approved response might disable an account, revoke a session, restrict access or notify the security team.
Automation should be proportional to the evidence. Automatically isolating a service based on a weak indicator could interrupt a legitimate business process. High-impact actions should therefore include appropriate safeguards and human oversight.
Supporting Cyber Maturity and Compliance Readiness
Security monitoring can support AI governance and compliance by providing evidence of access, activity, incidents and response actions. It may also help organisations demonstrate that they are monitoring certain technical and operational risks.
However, Microsoft Sentinel does not establish EU AI Act compliance on its own.
The EU AI Act includes broader requirements affecting areas such as risk management, data governance, technical documentation, record-keeping, transparency, human oversight, accuracy, robustness and cybersecurity. The requirements that apply will depend on the organisation’s role and how an AI system is classified.
Organisations should maintain an inventory of AI systems, determine which obligations apply and ensure that security controls form part of a wider governance programme.
CyberOne’s AssureMap framework can help organisations assess their current security posture and plan improvements according to recognised priorities.
UK organisations should also monitor the development of the Cyber Security and Resilience legislation rather than describe it as an established compliance standard before the legislative process is complete.
The aim is to connect technical controls with clear business outcomes: stronger governance, better incident readiness, more reliable evidence and greater confidence in AI adoption.
Securing Your AI Roadmap
The rapid adoption of generative AI creates new opportunities, but it also increases the number of identities, applications, data flows and external services that organisations must understand.
Microsoft Sentinel AI threat detection can support this challenge by centralising relevant telemetry, identifying behavioural changes and helping analysts investigate related security events. Its value depends on visibility, suitable logging, strong detection engineering and an operating model capable of acting on the findings.
Organisations should begin by identifying the AI systems in use, understanding how those systems access data and confirming which security events can be monitored. From there, they can develop realistic detection scenarios for compromised identities, suspicious AI activity, prompt injection, data exposure and abuse of connected services.
CyberOne brings together Microsoft security expertise, AI assurance and continuous managed detection to help organisations move from fragmented visibility towards a stronger security posture.
Frequently Asked Questions About AI Threat Detection
Can Microsoft Sentinel detect prompt injection attacks?
Microsoft Sentinel can identify potential prompt injection when suitable application or gateway logs are available. Analysts can monitor suspicious requests, policy violations, unusual tool use and unexpected data access as part of a layered security approach.
Can Microsoft Sentinel monitor third-party AI tools?
Yes, if the AI service or its supporting identity, gateway or API layer provides suitable logs. Visibility depends on the telemetry and integration options available for each service.
How does Microsoft Sentinel UEBA support AI security?
Microsoft Sentinel UEBA highlights unusual behaviour across users and entities, such as unexpected access or API activity. These deviations help analysts prioritise investigations but do not confirm an attack on their own.
What affects the cost of logging AI interactions?
Costs depend on data volume, data type, pricing model, retention and storage choices. Organisations should prioritise security-relevant telemetry and check current Microsoft pricing for their region.
Does Microsoft Sentinel require a data scientist?
Not necessarily. Effective AI threat monitoring is more likely to require security analysts, detection engineers and application specialists who understand the organisation’s AI systems and telemetry.
How does CyberOne support UK organisations with AI threat detection?
CyberOne combines managed Microsoft security, MXDR and AI assurance to improve visibility, refine detections and investigate suspicious activity. Services are tailored to each organisation’s AI environment, risks and governance requirements.
