AI is changing how organisations work, but it is also changing the way attackers operate. Generative AI can support more convincing social engineering, while autonomous agents and AI-enabled applications create new identities, data flows and API connections that security teams need to govern.
This does not mean that every organisation needs a separate AI security platform. It does mean that existing security operations need better visibility of how AI systems are accessed, what data they use and whether their behaviour changes over time.
Microsoft Sentinel can support this work by bringing security data together, correlating activity across identities, endpoints, applications and cloud services, and triggering response workflows when configured signals indicate suspicious behaviour. The platform is most effective when it forms part of a wider operating model that includes identity controls, data governance, secure application design and clear incident response procedures.
For organisations without the resources to build and operate this capability internally, a managed service can provide the specialist monitoring, detection engineering and response support needed to maintain coverage.
Key Takeaways
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Master the 2026 threat landscape by identifying how autonomous agents and polymorphic malware bypass traditional signature-based security measures.
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Optimise Microsoft Sentinel for AI threats by using machine learning and the CyberOne AssureAI service to protect model integrity against sophisticated incursions.
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Establish a proactive framework for securing the AI supply chain through rigorous monitoring of internal agents and external API communications.
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Reduce response times and ensure compliance by partnering with specialists who provide the technical resolution and data security expertise essential for AI resilience
The Changing AI Threat Landscape
AI is increasing the scale and speed of activity across the digital environment. Organisations are adopting generative AI tools, embedding models into applications and connecting agents to business systems. Each new connection can introduce additional identities, permissions, data flows and dependencies that need to be understood and governed.
Attackers can also use AI to improve phishing content, automate reconnaissance and adapt their activity to the environment they are targeting. These techniques do not make existing security controls irrelevant, but they increase the importance of behavioural detection, identity protection and consistent monitoring.
The risks are not limited to malware. Organisations also need to consider:
- Prompt injection that attempts to manipulate an AI system’s instructions
- Excessive permissions granted to agents or service accounts
- Sensitive information being sent to an external model or API
- Unauthorised changes to model configurations or connected applications
- Poorly governed third-party AI services
- Inadequate logging that prevents effective investigation
- Data poisoning or manipulation within the model development lifecycle
These risks cross traditional security boundaries. They involve security operations, identity, data protection, application security, procurement and governance. A useful response therefore needs to connect technical monitoring with clear ownership and business risk.
Where Microsoft Sentinel Fits
Microsoft Sentine provides a central platform for collecting and correlating security data across cloud services, identities, endpoints, applications and other connected sources. This can help security teams identify relationships that are difficult to see when each signal is reviewed in isolation.
For AI-related activity, the value of Sentinel depends on the quality and scope of the telemetry available. Relevant sources may include:
- Authentication and access records
- Privileged identity activity
- API gateway and application logs
- Cloud workload and container telemetry
- Data access events
- Microsoft Defender alerts
- Activity from AI applications and connected agents
- Logs from approved third-party services
Sentinel does not replace secure AI design, identity governance or data protection. It provides a way to monitor and investigate activity across those areas when the right data is connected and the detection content is configured appropriately.
Microsoft Entra ID can support this model by helping organisations ta , applications and service principals. Defender products can provide protection and detection capabilities across endpoints, identities, cloud applications and other parts of the environment. Sentinel can then correlate relevant signals and support investigation and response.
Detecting Suspicious AI Activity
Detection should focus on observable behaviour rather than assumptions about whether activity was produced by a human or an AI agent. Useful indicators may include:
- A service account accessing data outside its normal scope
- A sudden increase in API requests or token consumption
- Authentication from an unusual location or device
- New permissions being assigned to an AI-enabled application
- A model or agent accessing sensitive data without a clear business reason
- Repeated requests that attempt to bypass application controls
- Data being sent to an unapproved external endpoint
- A change in behaviour after a model, prompt or integration is updated
Kusto Query Language (KQL) can help analysts investigate these patterns across connected data sources. Detection rules should be tested against expected business activity so that automated systems do not create excessive false positives.
User and Entity Behaviour Analytics can also help identify deviations from established patterns. This requires sensible baselines and regular review. AI applications may legitimately generate high volumes of activity, so a high request count alone should not be treated as evidence of an attack. The context of the request, the identity involved, the data accessed and the destination are equally important.
Responding To Prompt Injection And Agentic Risks
Prompt injection is an application security and governance issue as well as a monitoring issue. A SIEM can help identify suspicious activity around an AI system, but it cannot guarantee that every malicious instruction will be detected from the prompt text alone.
Organisations should combine monitoring with preventative controls, including:
- Restricting the data and actions available to each agent
- Applying least-privilege access to identities and service accounts
- Separating trusted instructions from untrusted user or retrieved content
- Validating inputs and outputs within AI-enabled applications
- Controlling connections to external APIs and model providers
- Recording prompts, responses and tool calls where appropriate and lawful
- Testing agent behaviour before production deployment
- Defining clear escalation and shutdown procedures
Automation can support response when there is sufficient confidence in the detection. A playbook might revoke a token, disable an account, block an endpoint or raise an incident for analyst review. The appropriate action depends on the risk, the asset involved and the potential impact of disrupting a legitimate business process.
Protecting Data, Identities And Integrations
AI security depends heavily on data governance. An organisation should know which systems its AI applications can access, what information is sent to external providers and how long prompts, responses and associated logs are retained.
Microsoft Purview may support elements of this work through information protection, data governance and compliance capabilities. The precise controls available will depend on the Microsoft services deployed and the organisation’s licensing and configuration.
The following questions should be answered for every material AI use case:
- What data can the application access?
- Which identities can invoke it?
- Which actions can an agent perform?
- Which external services receive prompts or outputs?
- What activity is logged?
- Who reviews that activity?
- How is misuse detected and contained?
- What happens if the application or provider becomes unavailable?
These questions help connect AI security to operational resilience. They also provide evidence for governance and risk decisions without assuming that compliance alone demonstrates effective security.
When Managed Detection And Response Adds Value
Microsoft Sentinel can be operated internally, but effective operation requires more than deploying the platform. Teams need the skills and capacity to connect data sources, develop detection content, investigate alerts, maintain playbooks and review the environment as business use changes.
A managed Sentinel or managed extended detection and response service may be useful when an organisation:
- Does not have sufficient security operations coverage
- Needs support outside normal working hours
- Lacks specialist detection engineering skills
- Wants to reduce alert noise
- Needs help integrating Microsoft security services
- Requires clearer reporting for senior leaders
- Is developing its AI governance and monitoring model
CyberOne can be positioned here as a Microsoft security partner that helps organisations implement, optimise and operate Microsoft security capabilities. Any specific claim about CyberOne’s service coverage, assurance, certifications or AI security offerings should be supported by approved and current service information.
The commercial value should be expressed in practical terms. A well-run service can help improve visibility, reduce the time spent triaging low-value alerts, support faster investigation and provide more consistent evidence of security performance. It should not be presented as a guarantee that an organisation will prevent every AI-related incident.
6 Practical Steps For Building AI Resilience
Organisations can begin with a focused assessment rather than attempting to monitor every AI interaction immediately.
1. Create An Inventory
Identify approved AI applications, model providers, agents, service accounts, integrations and data sources. Include experimental or departmental use where possible.
2. Assess Access And Permissions
Review which identities can access AI systems and what actions those systems can perform. Remove unnecessary permissions and apply stronger controls to privileged access.
3. Connect Relevant Telemetry
Prioritise logs from identity providers, cloud platforms, applications, APIs, data stores and security products. Confirm that the information is detailed enough to support investigation.
4. Define High-Value Detections
Start with a small number of meaningful use cases, such as unusual data access, unauthorised API connections, privilege changes and suspicious authentication activity.
5. Test Response Procedures
Use controlled scenarios to test whether the organisation can investigate and contain an AI-related incident. Include technical teams, risk owners and business stakeholders.
6. Report Progress In Business Terms
Measure improvements in visibility, investigation time, response consistency, coverage and unresolved risk. Report these outcomes in a format that supports leadership decisions.
Conclusion
AI security is not achieved by adding one more monitoring tool. It requires visibility of identities, applications, data, integrations and the actions that AI systems can take.
Microsoft Sentinel can provide an important monitoring and response layer when it is connected to the right data and supported by effective identity, data and application controls. For organisations that lack the capacity to operate this model internally, managed Microsoft security services can provide additional expertise and coverage.
CyberOne can support organisations that want to improve their use of Microsoft security technologies, strengthen monitoring and build a measurable path from risk to resilience. The most useful next step is to assess current AI use, identify the highest-risk connections and define the telemetry and response processes needed to manage them.
Frequently Asked Questions
Can Microsoft Sentinel Detect Prompt Injection Attacks In Real Time?
Microsoft Sentinel can support the monitoring and investigation of activity associated with AI applications when relevant logs and telemetry are available. It can correlate identity, API, application and data access signals and trigger response workflows based on configured rules.
It should not be described as a standalone prompt injection detection system. Effective protection also requires secure application design, input and output controls, least-privilege access and testing of the AI application itself.
How Does Microsoft Sentinel Handle Logs Generated By AI Agents?
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Sentinel can ingest and analyse security data from connected sources, subject to the capabilities of those sources and the organisation’s configuration. Organisations should design a logging strategy that captures useful security events without collecting unnecessary sensitive content.
Cost, retention and investigation requirements should be considered when deciding which logs to keep in analytics tiers and which to retain for longer-term reference. The most useful approach is to prioritise events that support detection, investigation, governance and incident response.
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Is A Managed Service Necessary For Microsoft Sentinel?
No. Some organisations operate Sentinel internally. A managed service may be appropriate when internal teams lack the capacity, specialist skills or out-of-hours coverage required to maintain the platform effectively.
The decision should consider the organisation’s risk profile, existing Microsoft capability, operating model, budget and reporting requirements.
What Is The Difference Between Microsoft Sentinel And Microsoft Defender?
Microsoft Sentinel is primarily a SIEM and security orchestration, automation and response platform. Microsoft Defender products provide security capabilities for specific areas such as endpoints, identities, email, cloud applications and cloud workloads.
The services can work together. Defender can generate alerts and protection signals while Sentinel can help correlate data across the wider environment, support investigation and trigger response workflows.
How Should UK Organisations Approach AI Security Regulation?
Organisations should monitor applicable UK legislation, regulatory guidance and sector requirements rather than assuming that one emerging law applies in the same way to every AI use case.
A sensible starting point is to document AI systems, assign ownership, assess data and access risks, retain appropriate evidence and test incident response. Regulatory and legal claims should be checked against current guidance before publication
