Sweet Security enables AI security at scale by discovering the AI operating across cloud environments, understanding what agents can access and do, and enforcing their intended behavior at runtime.
AI is increasingly embedded across applications, development workflows and business processes, with agents accessing cloud services, using enterprise identities and interacting with sensitive data. The number of agents operating in your cloud is likely higher than it was last month, and some may never have passed through a formal security review. Securing this expanding environment requires more than applying policies to a known list of agents. Sweet discovers agents directly from runtime activity, including shadow AI that was never registered with or approved by security. It connects each agent to the identities, tools, data and cloud resources around it, giving security teams the context needed to apply and enforce the appropriate protections. Together, runtime discovery, cloud context and enforcement allow security teams to expand protection as AI adoption grows, without managing every agent as an isolated project.
Discovering AI Across the Cloud
Security teams cannot protect only the agents they have been told about. Developers and business teams can introduce AI into existing applications, workloads and services through ordinary deployment processes, creating gaps between an organization’s official AI inventory and what is actually running. The Sweet sensor identifies AI agents from live runtime activity without requiring each one to be registered in advance or the included Sweet AI Gateway for discovery. This gives security teams a current view of approved agents and shadow AI across their cloud environments.
Discovery is only the starting point. Sweet also shows where an agent runs, which identity it uses, the tools and resources available to it, the data it accesses and the actions it performs. That cloud context helps security teams understand which agents create meaningful risk and where stronger controls are required.
Enforcing Intended Behavior at Runtime
AI agents do not simply generate content. They can call tools, interact with applications, access sensitive data and perform actions against cloud infrastructure. Their behavior can also change as they process new instructions and information. Sweet applies policies to this live activity through its AI Gateway. Policies can identify and prevent behavior associated with Prompt Injection, Jailbreak, Agent Misuse and Agent Configuration Extraction.
Security teams can operate policies in Audit mode to record violations without interrupting activity or use Block mode to prevent prohibited behavior in real time. This allows organizations to introduce enforcement according to the risk and operational requirements of each environment. Rather than relying solely on how an agent was configured or what it was originally approved to do, Sweet evaluates what the agent is actually doing while it runs.
Managing Protection Across Changing Environments
As the number of agents increases, security teams also need an efficient way to administer these protections. Configuring every agent individually creates a process that becomes harder to maintain with every deployment. Infrastructure-based policy scopes are one way Sweet helps teams manage AI security at scale. Policies can be applied across clusters, namespaces, workloads or cloud accounts, following the same structures organizations already use to manage their cloud environments.
A security team might establish one policy posture for development namespaces and a stricter posture for production workloads that access sensitive data. Every AI agent operating within those scopes receives the appropriate protection, including shadow AI and agents discovered after the policy was defined.
Sweet shows the live reach of each scope, allowing teams to understand how many agents a policy covers before applying it. When scopes overlap, the most specific policy applies, making it possible to establish broad organizational controls while adapting protection for particular environments or workloads.
These scopes also give security teams a reviewable representation of their AI enforcement posture. Instead of maintaining a spreadsheet of individual agent settings, teams can examine which policies apply across their infrastructure, where blocking is enabled and why a particular action was allowed or prevented.
Scaling AI Security
Many AI security programs begin with a controlled group of approved agents. The real challenge comes when those agents multiply across development teams, business units and cloud accounts. Sweet addresses that challenge across the AI security lifecycle. Runtime discovery identifies approved and shadow AI. Cloud context reveals the identities, tools, data and infrastructure connected to each agent. AI Gateway policies audit or block behavior as it occurs. Infrastructure-based scopes help teams administer those protections consistently across large and constantly changing environments.
No single capability makes AI security scalable on its own. Scale comes from connecting discovery, context, policy and enforcement so that protection keeps pace as organizations deploy more agents. Sweet enables security teams to understand what AI is running, prioritize the agents that matter and enforce intended behavior across the cloud environments where those agents operate.
Interested in scaling AI Security across your organazation? Schedule a customized demo with us.
