Glemad Moves to Join the Open Secure AI Alliance to Advance Verifiable AI Security
Glemad is moving to join the Open Secure AI Alliance, bringing our work in continuous infrastructure reasoning, adversarial evaluation and controlled AI defense into a broader effort to build open security systems that defenders can inspect, adapt and trust.
Glemad is moving to join the Open Secure AI Alliance, bringing our work in continuous infrastructure reasoning, adversarial evaluation and controlled AI defense into a broader effort to build open security systems that defenders can inspect, adapt and trust.
July 28, 2026
AI is becoming part of the infrastructure that governments, enterprises and critical industries depend on.
As these systems gain greater access, authority and operational responsibility, security cannot remain an external control applied after deployment. It must be built into the models, harnesses, permissions, evaluation systems and infrastructure through which AI operates.
Today, Glemad is announcing its intention to join the Open Secure AI Alliance, an industry initiative introduced by NVIDIA alongside organizations across AI research, cybersecurity, cloud infrastructure, enterprise technology and open source development.
The Alliance is working to develop and share open technologies, research methods and defensive tools that can safeguard software and AI agents as these systems become more capable and widely deployed.
This direction closely aligns with the work Glemad is advancing.
Security Must Extend Beyond the Model
A capable model does not become dependable simply because it performs well on a benchmark.
Its real security depends on the complete system around it.
That includes the information it can access, the authority it receives, the tools it can invoke, the actions it is permitted to take, the evidence it preserves and the process used to verify its conclusions.
Open model weights matter because they allow serious institutions to inspect, evaluate and adapt intelligence for their own environments. But weights are only one part of the security problem.
The harness, identity layer, permissions, runtime, policy controls, evaluation methods and operational records determine whether an AI system remains accountable when deployed into consequential infrastructure.
This is one of the central reasons Glemad is moving to participate in the Open Secure AI Alliance.
We believe secure AI must be evaluated as a complete operating system for intelligence, not as an isolated model.
What Glemad Brings to the Alliance
Glemad is an AI security research and product company developing auditable and dependable intelligence for consequential digital infrastructure.
Our research asks a direct question:
How should an intelligent defense system understand a changing threat, act within authority and leave evidence worthy of review?
Through the Ollandi model class, we are developing systems that can maintain an evolving understanding of infrastructure, interpret incomplete and conflicting threat evidence, reason within operational constraints and support controlled response.
Ollandi 5 Preview, our latest model, advances this work across persistent infrastructure state, cross-domain threat reasoning, constraint-aware response and evidence preservation.
Our contribution to the Alliance will focus on areas where AI security becomes operational:
Continuous infrastructure reasoning
Security incidents develop across identities, applications, cloud systems, endpoints, networks and physical infrastructure.
Defenders need models that can maintain context as those environments change, connect signals across disconnected systems and distinguish legitimate activity from operational failure or malicious intent.
Evaluation under adversarial conditions
Security models must be tested against deception, compromised inputs, incomplete evidence, conflicting telemetry and unfamiliar attack behaviour.
Evaluation must measure more than whether a model produced a plausible answer. It must establish whether the reasoning remained grounded, whether uncertainty was represented honestly and whether the resulting action was justified.
Policy-bounded defensive action
An AI system operating within critical infrastructure must understand what it is authorized to do.
It must distinguish observation from recommendation, recommendation from approval and approval from execution.
Every action should remain constrained by permission, consequence and reversibility.
Evidence and accountability
AI security systems should preserve the connection between the original signal, the model’s interpretation, the authority governing its response and the final operational outcome.
Without that record, organizations cannot reliably investigate decisions, verify restoration or assign responsibility.
Open Systems Give Defenders Greater Control
The security community needs access to both capable closed models and capable open models.
Different environments require different deployment choices.
Critical infrastructure operators, governments and enterprises may need to run models within controlled infrastructure, retain sensitive telemetry locally, perform independent evaluations and adapt systems to specialized operational requirements.
Open models and inspectable tools make that possible.
They also allow researchers and defenders to reproduce findings, identify weaknesses, challenge assumptions and improve safeguards without depending entirely on the decisions of a single provider.
This does not eliminate the risks associated with capable models.
It creates the conditions required to understand and manage those risks with evidence.
The answer to AI misuse is not less capability for defenders. It is stronger evaluation, enforceable constraints, clear accountability and broader access to systems that can be inspected and secured.
Building a Shared Defense Foundation
Cybersecurity has always improved through shared knowledge.
Vulnerability research, threat intelligence, open standards and reproducible tools allow defenders to learn from one another rather than repeatedly confronting the same problems in isolation.
AI security now requires the same foundation.
The Open Secure AI Alliance is bringing together organizations working across models, infrastructure, security operations, identity, software development and open source systems to build that foundation. NVIDIA has stated that the initiative will support an open defense stack covering areas such as agent identity, isolation, model formats, scanning and secure development workflows.
Glemad intends to contribute to this work through research, evaluation and practical systems developed for environments where incorrect reasoning or uncontrolled action can carry serious consequences.
“The future of AI security will not be secured by opacity. It will be secured by systems that can be inspected, challenged, constrained and held accountable. Glemad is moving to join the Open Secure AI Alliance because dependable intelligence must be built through serious shared work.”
David Idris, Founder and CEO of Glemad
The Work Ahead
The next generation of AI will not operate only inside chat interfaces.
It will participate in software development, infrastructure operations, security investigations, financial systems, public services and critical industrial environments.
These systems must be capable.
They must also remain legible, bounded and answerable.
Glemad is moving to join the Open Secure AI Alliance to help advance an open technical foundation for that future.
Our position is clear: intelligence trusted with consequential infrastructure must remain connected to evidence, authority and human responsibility.
That is the standard we are building toward.
