Introducing CyberMSI AI Trust: Get the confidence that your AI usage is known, secure, continuously monitored, and governed.
Somewhere in your tenant, an employee pasted sensitive customer data into a chatbot that your security team never approved. A business unit deployed an agent with a workload identity that nobody reviewed for least agency. A copilot inherited access to a document library that was shared far more broadly than anyone intended.
None of this required an attacker. It is simply what AI adoption looks like when it moves faster than security. And it is the reason that, for most organizations, the most urgent question in cybersecurity today is no longer “Are we using AI?“, but “Do we know where our AI is, what it can touch, and whether it is secure?“
CyberMSI is answering that question with the launch of CyberMSI AI Trust, a fully managed service that secures, monitors, and governs your organization’s AI usage around the clock.
AI does not add one new risk. It adds risks at every layer.
Legacy security was built to protect systems that behave predictably. AI systems do not. Each layer of the AI stack introduces its own class of threat vectors:
- AI chatbots expose sensitive information leakage and shadow AI that grows where security teams cannot see it.
- AI agents and applications are vulnerable to intent breaking, memory and context poisoning, indirect prompt injection, exploited tools, data oversharing, and insecure MCP servers.
- Open weights models invite data poisoning, guardrail removal, jailbreaking, and denial of wallet attacks, in which the attacker’s goal is to consume your tokens and your budget.
- Governance and compliance gaps appear around agent identity and access, AI policy management, and AI supply chain attacks.
The common thread is uncomfortable. You cannot patch these weaknesses because the behavior of the AI is the new attack surface. And a manipulated agent that holds valid credentials looks exactly like authorized activity because technically it is.
The data confirms that this is not theoretical. In its 2025 Cost of a Data Breach Report, IBM found that 13 percent of organizations had already reported a breach of an AI model or application, and that 97 percent of those organizations lacked proper AI access controls.
The four pain points that keep security leaders up at night.
When we speak with CISOs, IT directors, and security managers, the concerns about AI consistently fall into four categories.
- Visibility: Shadow AI, unapproved apps, and agents that were deployed without a conversation with security. You cannot protect what you cannot see, and most organizations cannot yet produce a reliable inventory of the AI operating in their environment.
- Identity and access: Agents run on identities, and those identities frequently hold far more permission than their task requires. Worse, abandoned agents often keep their credentials long after anyone stopped using them. The principle of least privilege has to become a principle of least agency.
- Data exposure: Sensitive data flows into AI applications, is overshared through permissions that were never tightened, and leaves through connectors that nobody approved. Data loss prevention, labeling, and access policies built for a pre-AI world often do not hold up.
- Operations:. Disparate detections, incomplete logging, and automated playbooks that never executed. Even when an AI-related signal exists, it frequently never reaches the people and tools that could act on it.
Notice the pattern. Most tools address one of these four problems. Attackers move across all four in a single attack.
Consider a realistic chain of events. A poisoned document reaches an AI agent. The agent holds a data permission. The data leaves through an approved connector. Siloed tools see three unrelated, low-severity alerts. A correlated view sees one incident, and sees it in time to stop it.
Why buying another product will not solve this.
Many organizations respond to AI risk by purchasing a point solution. That instinct is understandable, but it misses the nature of the problem. AI risk spans exposure management, security operations, governance, and compliance, and it lasts for the entire lifecycle of every AI application and agent, from design and deployment through monitoring, retirement, and everything between.
Organizations need the following capabilities for trusted AI usage:
- Governance sets ownership and policy.
- Exposure management finds attack paths and configuration risks.
- Compliance produces the evidence.
- Security operations investigates, contains, and recovers.
Miss any one of the four, and the other three are working with a blind spot.
This is why AI Trust is deliberately defined by what it is not. It is not about security products alone. It is not limited to AI model or agentic security. And it is not a one-time implementation.
Introducing CyberMSI AI Trust.
CyberMSI AI Trust is a managed service built on Microsoft security, powered by AI agents, and operated by security analysts 24x7x365. It assures that your AI usage is:
- Known and secure, with every AI app, copilot, agent, and third-party tool discovered, inventoried, and protected.
- Continuously monitored, with AI telemetry feeding a single detection and response pipeline.
- Governed and compliant, with policy management and continuous compliance monitoring against standards such as GDPR, HIPAA, and PCI-DSS.
Underneath the service sits the Microsoft Integrated Security Operations Center (ISOC), and a single pipeline that collects signals, correlates incidents, prioritizes exposure, investigates, contains, remediates, and measures outcomes.
That pipeline spans six attack surfaces: AI copilots and agents, identity, applications, data, cloud, and endpoints. One pipeline matters because an AI attack does not stay in its lane, and your defense should not either.
Within that framework, CyberMSI AI Trust delivers:
- Prompt injection defense, agent hijacking prevention, and exfiltration protection
- Shadow AI identification, governance gaps and policy violations
- Unified cyber risk visibility and proactive attack path modeling
- Continuous compliance monitoring and automated evidence collection
- 24×7 response to both legacy and AI-specific threats
- Fully managed, with analyst accountability built in
AI Trust inherits the operating model that dozens of CyberMSI customers already rely on for managed detection and response (MDR): AI performs the volume work while humans own the decision. Our “analyst-on-the-loop” approach pairs AI-scale detection with human accountability, so automation remains both intelligent and responsible.
The results behind that model are measurable:
- 21 minutes mean time to remediation. Remediation, not merely response. Acknowledging an alert is easy; resolving the incident is the metric that matters.
- 30 to 75 percent fewer security incidents after onboarding. The range is wide because every tenant starts from a different place.
- 60+ incident response actions backed by a 30-minute SLA, so incidents are resolved rather than simply escalated.
- 24×7, one-click access via Microsoft Teams to your assigned SOC analyst team.
- Real-time visibility into SOC operations, supported by weekly in-person sessions.
As one biotech CIO and CyberMSI customer put it: “CyberMSI resolves incidents fast due to their deep Microsoft security expertise.“
Start with a free AI Risk Assessment.
You do not need a large time investment to learn where you stand regarding AI security risks. The recommended starting point is a six-part assessment that produces a scored inventory of your AI footprint and a prioritized list of gaps:
- AI apps and agent discovery: Inventory AI apps, copilots, agents, third-party tools, and shadow AI.
- Identity and access risk: Evaluate AI agent identities, workload identities, and permissions in Microsoft Entra ID.
- Data exposure and governance: Identify sensitive data flowing into AI applications and assess DLP, labeling, and access policies.
- AI threat surface evaluation: Analyze prompt injection, agent hijacking, and exfiltration risks across model endpoints.
- AI security posture score and gaps: Benchmark against Secure-AI best practices and flag misconfigurations.
- Monitoring and visibility: Confirm that AI telemetry actually reaches your security tools. If it does not, detection and response have nothing to work with.
One question you need to be prepared to answer for your stakeholders: What are our AI risks?
If you cannot answer this question with quantifiable evidence, you need to get it before the next major AI agent incident hits.
The organizations that earn trust in the era of AI will be the ones that can demonstrate that their AI usage is known, monitored, and governed.
CyberMSI AI Trust exists to make it possible.
Get your free AI Risk Assessment → https://cybermsi.com/services/ai-security/
About CyberMSI. CyberMSI delivers 24×7 managed detection and response built on Microsoft Integrated Security Operations Center (ISOC). Our AI and analyst-on-the-loop SOC model pairs AI-scale detection with human accountability, delivering accountable and intelligent automation that resolves incidents rather than simply alerting on them.