ArmorCode has launched AI Exposure Management (AIEM), a governance-focused system delivered through its ArmorCode Agentic AI Platform, designed to provide enterprises with centralized visibility, control, and accountability over AI usage across applications, cloud environments, and developer workflows. The platform integrates with more than 350 security and IT systems to ingest telemetry and governance signals, positioning itself as a control layer for managing AI risk at scale.
The release is framed squarely around AI governance, addressing a growing gap between the rapid adoption of generative AI and the slower evolution of enterprise oversight frameworks. As organizations deploy AI across distributed environments, many lack a consolidated inventory of where AI is in use, who approved it, and who is responsible for managing associated risks. This fragmentation is compounded by the prevalence of “shadow AI,” where employees use generative tools without formal disclosure, limiting visibility for security teams.
AIEM is designed to close this gap by continuously aggregating usage data and risk signals from across enterprise systems, converting them into enforceable governance actions. The platform maintains a live inventory of AI deployments and assigns ownership for each instance, enabling organizations to track accountability and ensure that risk is actively managed rather than passively monitored.
Operationally, the system emphasizes policy-driven automation. Governance workflows replace manual approval processes, allowing security teams to define rules that govern AI adoption while enabling business units to deploy tools more rapidly within those constraints. This approach reflects a shift from static compliance checks to continuous, auditable governance embedded in operational workflows.
The platform also focuses on translating security telemetry into decision-making processes. Signals from tools such as endpoint detection and response (EDR), secure access service edge (SASE), and firewalls are normalized and converted into governance events, including approvals, reassessments, and enforcement actions. Each action is logged, creating a traceable record of how AI-related risks are identified and addressed.
According to ArmorCode, a key feature is the generation of board-level reporting. AIEM continuously records AI usage, ownership, risk posture, and policy decisions, enabling security leaders to produce real-time dashboards and audit-ready reports. This aligns with increasing regulatory and board scrutiny around AI risk management, where demonstrable oversight is becoming as critical as technical controls.
By positioning governance as a system of action rather than a reporting layer, ArmorCode is targeting enterprises seeking to scale AI adoption without introducing unmanaged risk. The platform reflects a broader trend toward embedding governance directly into AI operations, where visibility, accountability, and enforcement are treated as core infrastructure requirements.