Summary
- Cybanetix says its Managed AI Service covers employee AI use, AI governance, and embedded AI agents.
- The service combines technology from NOMA, SentinelOne, Microsoft, and Exabeam with Cybanetix consultancy and SOC operations.
- The launch reflects growing demand for managed AI security where organisations need inventory, policy, monitoring, and response capability.
Cybanetix has launched a managed AI security service intended to help enterprises control employee AI use, AI governance, and embedded AI systems through a combination of technology platforms, consultancy, and security operations monitoring.
The Managed AI Service combines technology from NOMA, SentinelOne, Microsoft, and Exabeam with Cybanetix consulting, managed service delivery, and 24/7 SOC monitoring. The service is designed to respond to AI security alerts in under 15 minutes.
The offer is built around three domains of enterprise AI risk. The first is user behaviour, including employees uploading data into public large language models or using unsanctioned tools. The second is governance, including model provenance, AI bills of materials, and ownership of AI assets across the business. The third is embedded AI, where agents and tools are connected into business processes, sometimes with excessive privileges and incomplete inventory.
SentinelOne Prompt Security and Microsoft Purview for AI handle user-level controls, while NOMA supports AI discovery, access control, red teaming, detection, and response, and Exabeam provides agent behaviour analytics. The service also includes observability, exposure mapping, behavioural monitoring, runtime protection, synthetic testing, adversarial testing, AI posture management, and continuous reporting.
The broader risk category is now moving quickly. Enterprises are shifting AI from experimentation into business workflows, while many still lack a reliable inventory of which tools, models, copilots, agents, plugins, or low-code automations are in use. Without that inventory, governance and monitoring depend on assumptions rather than evidence.
Several exposures can develop at once. Employees may paste sensitive data into unmanaged services. Internal teams may connect AI agents to SaaS platforms with broad permissions. Developers may use coding agents that can access repositories, secrets, build systems, or production-adjacent tooling. Business units may deploy AI workflows without logging, monitoring, or named ownership. The organisation may then struggle to prove to regulators, customers, or auditors how AI systems are governed.
The managed service model responds to a capacity problem. AI security requires skills from application security, data protection, identity, cloud security, model risk, legal, procurement, and security operations. Most organisations will not hire specialist teams for every aspect of AI governance and runtime monitoring, so managed providers are packaging technology stacks into operating models that can be consumed through existing SOC or MDR relationships.
Enterprise buyers will need to assess the service at the level of operations rather than branding. Useful capability depends on whether the provider can discover AI assets across sanctioned and unsanctioned environments, map agent permissions, detect prompt abuse, identify data exfiltration, recognise model manipulation, separate abnormal behaviour from legitimate business activity, and provide evidence mapped to frameworks such as ISO 42001, the EU AI Act, NIST AI RMF, NIS2, Cyber Essentials Plus, and ISO 27001.
The SOC element also depends on integration depth. AI alerts can be noisy if they are not tied to identity, endpoint, SaaS, data, and network context. A managed service needs to correlate AI activity with existing identity and endpoint telemetry, decide whether activity is authorised, and support containment without breaking business workflows unnecessarily.
Cybanetix’s launch reflects where the AI security market is heading. Standalone AI governance policies are unlikely to hold once agents and copilots are embedded in operations. Inventory, access control, runtime monitoring, adversarial testing, incident response, and evidence will become the working layer beneath AI adoption.
