Wand AI интегрировала технологию VLNO для обеспечения защищенности открытых моделей в суверенных вычислениях

WAND AI adds VLNO as a Model Robustness Layer to Enable Sovereign AI on Open-Weight Models VLNO's adversarial hardening technology becomes an additional security capability within Wand AI sovereign AI labor infrastructure. PALO ALTO, Calif., Aug. 12, 2026 /PRNewswire/ -- Wand AI today announced the integration of VLNO's model robustness technology into its Sovereign AI offering, enabling governments and regulated enterprises to deploy open-weight models on their own sovereign infrastructure with measurable, certified robustness. By incorporating VLNO's security-training layer into the Wand ecosystem, organizations can operate open models on sovereign compute with a verified security and resilience posture—rather than relying on assumptions. The integration extends Wand's sovereign AI platform with a model robustness layer from VLNO. Wand provides the sovereign AI infrastructure, while VLNO continuously tests customer models against adversarial attacks and generates training data to strengthen the models themselves. The resulting protections are incorporated directly into the model, allowing protections to travel with it wherever it is deployed while existing runtime controls remain in place. As models evolve, VLNO continuously re-tests and re-hardens them to maintain a current security posture. This capability addresses a growing challenge for governments and regulated enterprises as they adopt open-weight models to maintain sovereign control over infrastructure and reduce inference costs. Security remains the primary barrier to production deployment. Unlike frontier models, open-weight models often lack standardized safety evaluations, accountable vendors, and proven adversarial robustness. Existing runtime guardrails and evaluation tools can identify vulnerabilities but do not strengthen the underlying model. "Sovereign programs have had to choose between models they control and models they can defend," said Hertzel Kuriel, Co-founder and CEO of VLNO. "Robustness is the gate on that decision, and it has been treated as something you inspect rather than something you fix. We produce the data that makes a model stronger, so a ministry or a bank can put an open model into production on the evidence of a measured attack success rate rather than on a vendor's assurance." "Wand provides the foundational infrastructure through which nations can deploy, operate, manage, and continuously evolve AI labor at scale. We are pleased to welcome VLNO into Wand's sovereign technology ecosystem, adding a robustness capability that lets ministries, institutions, and agencies adopt open-weight models on a unified national stack without lowering their security bar," said Cristian Felix, Chief AI Architect of Wand AI. How the Wand AI and VLNO Integration Works The integrated solution combines Wand AI's sovereign AI infrastructure with VLNO's model robustness capabilities to help governments and regulated enterprises securely deploy and maintain open-weight models. How are models evaluated before deployment? Before a model is added to Wand's Open Model Registry, VLNO benchmarks it against adaptive attacks across agentic scenarios. Robustness becomes an admission criterion for the sovereign environment, helping organizations measure security before a model enters production. How are models hardened against adversarial attacks? If a model does not meet the required robustness threshold, VLNO generates adversarial scenarios based on the customer's operating environment, executes them against sandboxed agentic workflows, and produces training artifacts—including RLHF- and DPO-ready trajectories or a hardening LoRA—that customers can incorporate into their own fine-tuning pipeline. No model weights or sensitive workflow content leave the customer's trust boundary. How is model robustness maintained over time? Because robustness is tied to the model weights, every model update, checkpoint, or newly identified attack class triggers re-measurement. VLNO continuously re-tests and re-hardens models so their security posture remains current as both models and threats evolve. How does VLNO complement Wand AI's existing security capabilities? Wand's existing inference privacy and confidential computing capabilities protect the data path surrounding the model. VLNO complements those capabilities by strengthening the behavior of the model itself, creating a layered approach to AI security. When is the joint solution available? The Wand AI and VLNO joint reference architecture is available now for sovereign programs and regulated enterprises. About Wand AI Wand AI is building the sovereign infrastructure for AI labor. Wand's operating system enables governments and enterprises to deploy, manage, govern and continuously evolve AI agents alongside humans as trusted members of the workforce, with identity, objectives, authority, security, governance and auditability built into the platform. For nations, Wand provides a unified foundation through which ministries, public institutions, and critical national organizations can deploy AI labor while maintaining sovereign control over models, compute, data, policies and operations. Wand powers production-scale deployments with some of the world's most consequential institutions, including leading banks, asset management firms, hedge funds, consulting firms, and system integrators. Wand exists to power the biggest transition in human history: infinite labor—so that everything humanity can imagine, we can finally build. Founded in 2023 and headquartered in Palo Alto, California, with offices in Palo Alto, New York and Abu Dhabi, Wand is backed by world-class investors, leaders, and a tier-one research team. Learn more at wand.ai. About VLNO VLNO builds the security training layer for open and agentic models. Its platform generates adversarial attacks tailored to real agentic workflows, runs them at scale against sandboxed enterprise surfaces, and converts the results into training data that hardens the model itself—RLHF- and DPO-ready trajectories and hardening LoRAs that plug directly into a customer's fine-tuning pipeline. Where evaluation labs produce reports and runtime vendors produce filters, VLNO produces the data that changes model behavior, measured by attack success rate against an adaptive attacker and re-certified on every checkpoint. VLNO's public robustness benchmark lets any team measure their own model for free. The company was founded by leaders in frontier-model red-teaming, adversarial evaluation, and RL-based model hardening. Learn more at vlno.ai. MEDIA CONTACTS Wand AI Hope Frank, CMO [email protected] VLNO Hertzel Kuriel, CEO [email protected] SOURCE Wand AI