Cloud management & security

The opposite, in most programs we have run. A policy check inside the pipeline returns a result in seconds, and it returns it to the developer who can act on it at the moment. The friction teams associate with governance almost always comes from bolted-on checks and a review board that meets weekly, or a security sign-off that arrives after the code is written. Automating the rule removes the wait, and it removes the incentive to work around the rule.

An assessment establishes where controls exist and where the gaps are on the day it is performed. Continuous monitoring watches the running environment and catches violations and drift as they occur. The two do different jobs, and most enterprises need both: the assessment tells you where to concentrate, and the monitoring stops the picture going stale the week after you fix things.

The enforcement mechanism does not change. Policy-as-code and access control are applied to a wider surface on the data a model can reach, the compute it can consume, the tools an agent is allowed to call, and the boundaries its output has to stay inside. Compliance mapping extends to the NIST AI RMF and DORA alongside the frameworks you already report against. Most governance programs were scoped before agentic systems were in production, which is why this reads as new work even though the underlying control model is the one you already have.

Controls are mapped to GDPR, HIPAA, PCI DSS, SOC 2, ISO 27001, DORA, and the NIST AI RMF, plus sector-specific standards where they apply. The mapping lives in the policy-as-code layer, which means a control is written once and satisfies every framework that requires it. For enterprises reporting against four or five frameworks at once, that single mapping is usually where the audit preparation savings come from.

No. Those are the tools this practice operates on. A CNAPP platform tells you what is wrong across the environment; it does not decide which findings matter most to your business, write the policy that stops the problem recurring, or embed that policy in your delivery pipeline. Torry Harris does that work on top of the tooling you have already bought, and we work with the major platforms rather than requiring a particular one.

Cloud security architects and GRC leads usually own day-to-day policy enforcement. The CISO and CIO sponsor the program jointly, since it affects risk posture, delivery speed, and audit cost across every team. Platform engineering stays involved throughout, because the controls live inside the infrastructure they operate, and a control nobody on the platform team understands is a control that gets disabled the first time it blocks a release.

Cloud Managed Services keeps operations running day to day, such as observability, cost management, delivery automation, and incident handling. Cloud Management and Security defines the policy, identity, and compliance controls those operations run inside. One runs the environment. The other decides what running it safely means, and enforces that decision automatically.

Cloud Transformation standardizes the infrastructure, which means the landing zones and provisioning templates every environment inherits. Cloud Management and Security determines what those templates have to enforce, and validates that they keep enforcing it once environments are live. Standardization gives the controls a single place to live. Governance decides what goes in it. Enterprises that run the two together usually find the second is much cheaper, because the control is written once for the blueprint instead of separately for every environment.

Twenty-five years of integration work where we build governance as code into the platform layer rather than delivering a security overlay that sits beside it and needs its own maintenance. We lead with the AI governance extension, whereas only a handful of firms with an integration heritage have taken data access and model usage into scope against DORA and the NIST AI RMF, leaving a gap where most enterprises end up exposed.