AI innovation is moving incredibly fast. Organizations are aggressively rolling out AI to drive efficiency and accelerate growth. At the same time, employees are taking things into their own hands, actively using AI tools that may or may not be approved. And AI agents are increasingly executing work with little to no human oversight.
That speed creates real exposure.
When your business is exposed to real AI liabilities, you don't need a theory deck. You need a practical approach that you can actually implement. Most AI security frameworks are valuable on paper, but they weren't written to tell your engineering and risk teams what to do to stop a data leak or lock down an agent pipeline.
GuidedSec delivers targeted safeguards built to reduce risk in your organization. We establish governance, audit third-party AI usage, and continuously validate AI outputs. The result: AI security becomes an operational reality that protects your business, not an abstract concern that lives in a slide deck while innovation keeps running ahead of it.
We provide embedded AI security leadership for organizations building and deploying AI without a dedicated security team. Acting as an extension of your organization, we develop governance, review architecture, guide executive and board discussions, and evaluate AI vendors to help you manage risk and scale AI securely.
We help organizations build and deploy AI securely through risk assessments, red teaming, architecture reviews, governance, executive risk briefings, incident response planning, and AI vendor evaluations. We identify security risks and deliver practical control matrices and remediation blueprints built to map, assess, and defend your unique AI footprint.
We help organizations measure and improve AI performance with custom evaluation frameworks and continuous testing. From establishing accuracy, safety, bias, and hallucination baselines to automated regression testing for every release, we provide the visibility and confidence needed to ship AI systems safely, reliably, and at scale.