Runtime AI Control: Turning Governance Into a Real-Time Capability
Runtime AI control is the practice of enforcing safety, compliance, and governance policies on AI models, agents, and workflows at the moment they act or infer, so enterprises can monitor and adjust AI behavior in real time instead of relying only on static rules or post-deployment reviews. At this point, that shift is no longer optional. Agentic AI systems can independently take action, spend money, and create liability on an enterprise’s behalf. Without runtime constraints, misaligned behavior moves from a lab concern to a board-level risk. The most important development is that runtime control is being embedded directly into enterprise AI infrastructure, rather than bolted on later. That architectural choice is what allows organizations to demand strong enterprise AI governance and agentic AI safety without treating compliance as a performance tax.
The headline lesson is clear: governance is moving into the AI runtime, not staying in policy documents. Enterprises that keep treating safety as an afterthought will find their AI programs blocked—not by regulators first, but by their own risk committees. Runtime AI control is becoming a gating factor for deploying agentic AI at scale, and the vendors that can embed it into everyday infrastructure will set the pace.
Trustwise and HPE: Control Towers for Agentic AI Behavior
Trustwise, a provider of runtime AI control through Trust Posture Management, has joined the HPE Unleash AI partner program. This is more than a logo swap; it is a statement that runtime control belongs beside GPUs and orchestration in the stack. With Trustwise AI Control Tower running on HPE Private Cloud AI, organizations can enforce runtime controls on agentic and generative systems, applying safety, compliance, and cost policies at the moment of inference and action. In plain terms, every prompt, tool call, and agent decision can be controlled as it happens, with audit-grade evidence produced for internal committees and external regulators.
The design is unapologetically governance-first. Customers in financial services, healthcare, public sector, and other regulated environments can discover, classify, and catalog agents, then assess their risk posture against frameworks such as the NIST AI Risk Management Framework, the OWASP Top 10 for Agentic AI, the EU AI Act, and ISO 42001. Trustwise claims more than 90% alignment of AI system behavior with enterprise policy, more than 25% reduction in AI operating costs, and up to 64% reduction in the carbon footprint of AI workloads. Those numbers matter because they argue that control does not have to mean overhead; governance can improve efficiency when it is part of the runtime.
From Guardrails to Trust Posture: Why Runtime Beats Static Policy
Static policies and pre-launch model reviews are no longer enough for agentic AI safety. Without runtime control, off-policy behavior, unauthorized tool use, hallucinations, prompt injection, and drift become financial, regulatory, and reputational risks instead of research topics. Trustwise’s approach pushes enterprises toward a "trust posture" mindset: continuously discovered agents, evaluated and red-teamed before and after deployment, then governed by real-time guardrails that block unsafe outputs, prompt injections, and policy violations as they happen.
This runtime posture is designed to scale. Trustwise AI Control Tower on HPE Private Cloud AI delivers capabilities critical to production AI: continuous performance and behavior monitoring for drift, plus generation of governance and audit evidence mapped to 17 global AI security, regulatory, and risk management frameworks. To accelerate adoption, Trustwise offers Forge AI, a production-grade evaluation environment that is cloud, model, and agent framework agnostic. With Forge AI, enterprises can validate use cases against their own data and policies, test risk and compliance in real conditions, then move to production with runtime enforcement in place from day one. The message is uncompromising: if AI is going to act on behalf of the business, it must live inside a framework that assumes continuous scrutiny, not one-off sign-offs.
Veeam and Everpure: DataAI Resilience and Trusted Recovery
On the data side, Veeam Software—the Data and AI Trust Company—announced at Pure//Accelerate a major expansion of its global strategic alliance with Everpure, unveiling the next generation of integrated cyber resilience and DataAI Resilience. DataAI Resilience is the convergence of data protection, cybersecurity, and artificial intelligence aimed at keeping systems secure and recoverable against machine-speed threats, autonomous AI agent errors, and ransomware, while supporting compliance and data quality. Historically, data resilience meant surviving human error or natural disasters, but today threats move too fast for manual response.
Veeam’s view is blunt: "Resilience now requires more than recovery; it requires trusted recovery: restoring data that is clean, governed, compliant, and ready to use". Together, Veeam and Everpure are turning alliance momentum into an integrated resilience roadmap across Everpure’s Enterprise Data Cloud (EDC) and Kubernetes, combining anomaly-driven workflows, planned fleet-scale EDC integration in Veeam Data Platform v13.1, and Cyber Resilience Delivered as-a-Service through trusted partners. The offering is designed to deliver a cloud-like experience while keeping customer data on premises for control, compliance, and data sovereignty. This is runtime AI control’s counterpart: data that AI systems draw from must itself be governed, resilient, and recoverable at machine speed.
Fleet-Scale Governance: Where Data Resilience Meets AI Safety
The expanded Veeam–Everpure alliance moves from point integrations to fleet-level control. Led by the upcoming Veeam EDC Fleet Management Integration, planned for Veeam Data Platform v13.1, customers will register an Everpure fleet once and automate discovery of additional arrays and associated objects. Consolidated inventory awareness—volumes, snapshots, datastores—reduces configuration sprawl and supports consistent policy design. As infrastructure grows, enterprises gain higher confidence that new resources are discovered and protected without manual overhead.
Veeam and Everpure are also expanding collaboration into cloud-native environments via integration of Portworx by Everpure and Kasten by Veeam. Planned for Q3 2026, this will deliver policy-driven protection for Kubernetes workloads using Portworx persistent storage, application-consistent operations for stateful containerized applications, and simplified mobility and disaster recovery workflows in hybrid and multi-cloud environments. Together, they are helping enterprises adopt DataAI Resilience, aligning cyber readiness with the governance and data quality needs required to scale AI safely. The joint innovations strengthen customers’ ability to keep data secure, recoverable, and trusted across hybrid environments. The bottom line: data resilience AI and runtime AI control are converging into a single discipline where data sovereignty, trusted recovery, and agentic AI safety are treated as one strategic requirement.
