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.
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:
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.
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:
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.
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:
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.
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:
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.
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:
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.
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:
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.
Organisations can begin with a focused assessment rather than attempting to monitor every AI interaction immediately.
Identify approved AI applications, model providers, agents, service accounts, integrations and data sources. Include experimental or departmental use where possible.
Review which identities can access AI systems and what actions those systems can perform. Remove unnecessary permissions and apply stronger controls to privileged access.
Prioritise logs from identity providers, cloud platforms, applications, APIs, data stores and security products. Confirm that the information is detailed enough to support investigation.
Start with a small number of meaningful use cases, such as unusual data access, unauthorised API connections, privilege changes and suspicious authentication activity.
Use controlled scenarios to test whether the organisation can investigate and contain an AI-related incident. Include technical teams, risk owners and business stakeholders.
Measure improvements in visibility, investigation time, response consistency, coverage and unresolved risk. Report these outcomes in a format that supports leadership decisions.
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.