AI adoption has outpaced AI security. It's what we're seeing across our client base every single week.
Employees are using AI tools their IT teams don't know about. Developers are deploying AI agents that can take autonomous action across entire tool ecosystems. Sensitive data is flowing into large language models over encrypted channels that traditional security controls can't inspect. And the attack surface is growing, while changing it's shape entirely.
The good news is the market has responded. A new generation of purpose-built tools has emerged alongside meaningful AI-aware upgrades to the platforms you already own. The harder truth is there's no single product that covers all of it. At NXGN, we've spent the last year evaluating this space alongside our clients, and what's become clear is that "securing AI" isn't one problem, it's three. Each requires different tools, different buyers, and a different level of urgency.
Here's how we think about it, and what we're recommending.
The Three Problems
1. Securing AI and LLMs: An Advanced DLP Problem
This is the most immediate challenge for most organizations, and the one most commonly underestimated.
When employees interact with AI tools, whether that's ChatGPT, Microsoft Copilot, Google Gemini, or any number of AI-powered SaaS applications — they're often pasting in sensitive data: customer PII, financial records, source code, legal documents, internal strategies. That data is leaving your environment in plain conversational language, flowing over HTTPS to a cloud-hosted model, and your traditional DLP tools almost certainly aren't catching it.
Classic DLP was built to detect known data patterns such as social security numbers, credit card numbers, structured formats. AI prompts are contextual, conversational, and dynamic. They require AI to catch AI.
The risks here are real and largely unaddressed in most stacks:
• Sensitive data submitted in public LLM prompts
• AI-generated outputs that surface or reconstruct regulated information
• Prompt injection as an attack vector against internally built AI applications
• Enterprise data being used to train third-party models without explicit consent
The question isn't whether this is happening in your environment. It is. The question is whether you can see it.
2. AI Governance and Shadow AI: A Visibility Problem
Before you can secure AI, you have to know it exists.
Most organizations are dramatically underestimating how many AI tools are already running in their environment. Employees and departments are adopting them independently, often with OAuth access to core business systems like Microsoft 365, Salesforce, Google Workspace, and Slack, without any formal review, approval, or visibility from IT or security.
This is shadow IT 2.0, and the stakes are higher. A rogue SaaS app might sync your contacts. A rogue AI tool with read access to your CRM doesn't just store data; it reasons over it, summarizes it, generates outputs from it, and can connect to other systems autonomously. The risk profile is categorically different.
The governance challenge is also about accountability: Who approved this tool? What data can it access? Who owns it when something goes wrong? For most organizations, the honest answer to all three is: we don't know.
The exposure here spans visibility, compliance, and access control:
• No real-time inventory of AI tools in use across the organization
• Ungoverned OAuth grants connecting AI tools to core business systems
• No process for approving, reviewing, or offboarding AI tool access
• Compliance exposure from AI tools processing regulated data outside reviewed workflows
3. Securing Agentic AI: The Frontier Problem
This is the newest challenge and the one moving fastest.
Agentic AI refers to AI systems that don't just respond to prompts, they take autonomous action. They browse the web, execute code, send emails, call APIs, interact with databases, and make decisions across multi-step workflows with minimal human oversight. These agents can operate across dozens of tools simultaneously, and unlike traditional applications, they don't follow predictable, auditable paths.
The security problem here is fundamentally different from anything the industry has dealt with before. A compromised or misbehaving agent with broad permissions can cause damage across your entire connected environment before a human ever notices. One over-permissioned agent whose creator has left the organization and is still running in the background is a very real risk most teams aren't tracking.
The gaps are significant:
• Agents operating with excessive permissions, taking unintended actions
• Prompt injection attacks hijacking agent behavior mid-task
• No audit trail of what agents did, when, and why
• Shadow agents built by employees outside any IT review process
• No runtime monitoring or enforcement layer above the agents themselves
Traditional security tools: EDR, SIEM, CNAPP, have no concept of an AI agent. They weren't built for this. A new control plane is required.
Your Existing Platforms Are Evolving: But There Are Still Gaps
The platforms your clients already own are responding to these challenges, and that matters. Before adding net-new tools, it's worth understanding what your existing investments cover and where they still fall short.
CrowdStrike continues to expand its AI security surface area, AI Security Posture Management (AI-SPM), agentic threat detection, and AI workload visibility are all part of the Falcon platform roadmap. Strong on endpoint and cloud; agentic governance controls are still maturing.
Zscaler is arguably the strongest existing platform for the AI/LLM DLP problem, inline inspection of AI traffic, prompt and response controls, and DLP enforcement at the network layer. The limitation is scope: Zscaler sees what flows through its proxy. It misses the identity layer, browser-based AI tools, and anything adopted outside the managed network path.
SentinelOne made significant AI security announcements at RSAC 2026, an end-to-end AI security portfolio covering Agent Security, Data Security Posture Management, and an AI Data Pipeline. One of the most credible platform plays in the space right now.
Palo Alto Networks (Prisma AIRS) has built a dedicated AI Security Posture Management platform, with runtime monitoring and a governance framework designed for enterprise-scale AI deployments. Strong fit for organizations already deep in the Palo Alto ecosystem.
The honest answer: these platforms are essential and increasingly AI-aware. But they weren't purpose-built for the agentic era, and they leave real gaps, particularly around shadow AI visibility, AI-native DLP, and agentic runtime governance. That's where the following point products come in.
Point Products Filling the Gaps
Below is a curated look at the purpose-built tools we're evaluating and recommending across the three challenge areas. These aren't vendor pitches, they're the products earning conversations in our client engagements right now.
Securing AI and LLMs
Dope.security: AI-Aware Secure Web Gateway + Dopamine DLP
Dope runs a fly-direct, on-device Secure Web Gateway that never routes traffic through a data center proxy — faster performance, better privacy, and inline control at the endpoint. Their Dopamine DLP capability is purpose-built for the AI era: real-time, generative AI classification that inspects prompt inputs and file uploads at the point of exfiltration. Zero data retention, HIPAA/BAA-compliant LLM processing, and a response within seconds. The difference between Dopamine and legacy DLP is the difference between understanding context and matching patterns. Additive to Zscaler — not competitive.
Cyera: AI-Powered Data Security Posture Management
Before you can protect data flowing into AI tools, you have to know where it lives. Cyera uses AI to automatically discover, classify, and map sensitive data across cloud environments, on-prem systems, and databases. Learning your organization's unique data patterns and context rather than relying on rigid rule sets. The result is 95%+ accuracy with far fewer false positives and no manual tuning required. Think of Cyera as the discovery and classification layer that makes your downstream DLP controls actually mean something.
MIND: Autonomous DLP Platform
MIND is the first AI-native DLP platform built from the ground up for the way data moves today. Across endpoints, cloud environments, SaaS applications, and GenAI tools. Their Autonomous DLP Analyst, announced in March 2026, operates at AI speed: continuously monitoring, investigating, and remediating data risk without requiring a human analyst to triage every alert. Covers DLP and Insider Risk Management in a single platform. Purpose-built for organizations that have accepted that manual DLP programs don't scale.
AI Governance and Shadow AI
Nudge Security: AI and SaaS Discovery + Governance
Nudge Security discovers every AI and SaaS tool in use across the organization — including shadow apps, OAuth grants, and browser extensions. Nudge uncovers AI agents via API connection into platforms like Salesforce Agentforce, ServiceNow, Tines, Workato, and more, along with browser-based AI agent discovery for apps like Cursor, Zapier, ChatGPT Workspace, and Atlassian Rovo without proxies, network taps, or endpoint agents. Their browser extension passively surfaces AI tools that no API-based tool can see, giving security and IT teams the complete picture they've been missing. The governance loop is built in: policy enforcement, owner notification, and audit-ready documentation that is designed to manage AI sprawl without killing productivity. If you don't know what AI tools are running in your environment, Nudge is where we start.
Securing Agentic AI
Geordie AI: Agent Security and Governance Platform
Geordie is purpose-built for how AI agents actually operate in the enterprise. It connects across cloud, code, and endpoint to automatically discover and maintain a continuously updated inventory of every agent in your environment — including their configurations, tools, permissions, and live activity. Their Beam remediation suite uses context engineering to assess agent risk holistically and feed mitigation back to the agent in real time, before a bad action completes. Raised $30M in a Series A led by Balderton Capital in May 2026. One of the fastest-moving companies in this space, and one we've been watching closely.
Onyx: Secure AI Control Plane
Onyx sits as a unified enforcement layer above your entire AI asset ecosystem, discovering agents, models, AI-powered applications, and MCP-connected tool ecosystems, then turning governance policies into enforceable runtime controls. The Onyx Guardian Agent acts as a supervisory AI that continuously monitors deployed agents, blocking unsafe actions, requiring human approval when appropriate, or narrowing an agent's operational scope before anything reaches a downstream system. For organizations already running AI agents across their stack, Onyx provides the governance infrastructure that should have been there from the start.
RAD Security: Agentic AI for Security Operations
RAD Security's FusionAI platform unifies reasoning, context, and automation across your security environment. It ingests signals from cloud, identity, and runtime environments, correlates them with real-world context, and drives explainable, auditable action through purpose-built AI Workers — VulnBot for vulnerability management, CloudBot for cloud risk, GRCBot for compliance. 100+ integrations and recognized as a Cloud Security Innovator in 2025. A strong fit for security operations teams that want AI-driven investigation and response without sacrificing auditability.
Sondera: Provable Runtime Controls for Agents
Sondera is the provable control plane for agents: define rules in natural language, and Sondera generates verified policy-as-code, stress-tested with adversarial simulation (research accepted at ICML 2026). Deployed in minutes, Sondera wraps agents across endpoint and cloud, governing statefully so sensitive data picked up in step three can't leak in step seventy-three. Sondera's neurosymbolic engine uses probabilistic AI classifiers to monitor behavior, but only deterministic policy-as-code adjudicates every action, with explainable audit trails for regulators. Strong for enterprises turning on coding agents and apps like Cowork. Founded by a Flashpoint co-founder and a research and engineering leader at Cylance and NetRise; backed by Decibel, Runtime, and Aviso Ventures. The team's published research sets them apart in a space full of claims.
NightVision: Developer-First Web and API Security Testing
For organizations building their own AI applications, pipelines, or agent frameworks, NightVision (www.nightviz.ai) delivers developer-native DAST and API security testing that catches exploitable vulnerabilities before they reach production. Set up in under a minute, scanning in 10-15 minutes, NightVision integrates directly into CI/CD pipelines including GitHub Actions, GitLab CI, Jenkins, and Azure DevOps, and drops pinpointed findings directly on the pull request, tied to the exact line of code. No false-positive noise, no manual schema setup. The security layer for teams that aren't just using AI, but building with it.
The NXGN Lens: How to Sequence This
Not every client needs all of these. Here's the diagnostic framing we use to figure out where to start:
Do you know every AI tool your employees are using?
If the answer isn't a confident yes, start with Nudge Security. Visibility before controls.
Are AI prompts and sensitive data inspected before they leave your environment?
If not, Dope.security, Cyera, and MIND address the data exfiltration problem at different layers. Most clients need more than one.
Are your cloud workloads and AI pipelines monitored for runtime threats?
RAD Security covers this with the security operations depth most teams are missing.
Are you running AI agents, or are your employees building them?
Geordie, Onyx, or Sondera depending on whether the primary need is discovery and governance, runtime enforcement, or developer-native controls.
Are you building AI applications internally?
NightVision covers the AppSec layer before anything ships.
Is your core platform fully activated for AI use cases?
Make sure your investment in CrowdStrike, Zscaler, SentinelOne, or Palo Alto is doing everything it can before adding point products on top of gaps it was already designed to fill.
Where We Go From Here
AI governance isn't a future problem. It's a now problem, and for most organizations, the exposure already exists. The clients we're most confident about are the ones who've taken the time to map which of these three challenges they're actually facing, matched the right tools to the right gaps, and built a sequenced plan rather than trying to solve everything at once.
If you're not sure where your exposure is, that's exactly where NXGN starts. Reach out and we'll help you build the map.