AI Innovation Needs a Place to Test Safely
How can IT leaders help teams move faster with AI without giving experimental agents unnecessary access to enterprise systems and data? Discover how isolated, on-demand environments can make AI workflows easier to test, evaluate, and contain before production.
Move AI forward with secure, governed operations environments
AI is moving from answering questions to using tools, retrieving information, interacting with applications, and taking actions. That evolution creates real opportunities for productivity and innovation, and a broader set of questions for IT and security leaders to answer.
An agent may behave as intended in one test and encounter different conditions when it processes untrusted content, uses credentials, calls tools, or passes information to another system. Prompt guidance and model safeguards remain useful, but they are only part of the picture. The workflow’s permissions, data access, network reach, execution boundaries, and downstream effects matter too.
Join Replica Cyber for a focused introduction to using instantly available, infinitely scalable secure operations environments, fully isolated from corporate network assets, to evaluate AI workflows in a controlled setting. The goal is not to slow experimentation. It is to give teams a practical way to learn what an AI workflow can do, where it should be constrained, and what needs to be true before it moves closer to production.
What you'll learn
Assess the full workflow
Why AI risk assessment should include the full workflow: model, instructions, identity, credentials, tools, data, network access, and downstream systems.
Prompt injection and compromised instructions
How to think about prompt injection and compromised instructions when agents process untrusted documents, web content, messages, or other inputs.
Permission overreach
How to evaluate overreach of permissions, even when an agent uses legitimate access that is broader than the task requires.
Isolation and containment
How isolation and containment can help limit environmental escape, unauthorized access, unintended external communication, and the impact of a failed experiment.
Practical test plans
How to design practical test plans with explicit objectives, trust boundaries, failure conditions, observability, and retesting.
IT and security as enablers
How IT and security can partner as enablers of responsible AI adoption instead of choosing between unrestricted experimentation and blanket prohibition.
Why this matters now
AI developers, including OpenAI, have publicly discussed prompt injection, unintended data exposure paths, and the challenges of keeping increasingly capable agents within intended boundaries. These disclosures are not a reason to assume every AI deployment is unsafe; they are a reminder that security depends on more than the model’s stated intentions. Sound engineering combines model-level defenses with carefully scoped access, technical isolation, monitoring, human review where appropriate, and testing that measures outcomes.
Built for the leaders moving AI forward
CIOs, CTOs, IT and infrastructure leaders, enterprise architects, AI platform owners, security leaders, application and engineering leaders, risk and governance teams, and decision-makers across financial services, healthcare, high-tech, or government.