Secure AI

SWG and CASB logs show traffic to known AI domains, but only tell you a user visited the site — not what they did there, whether they used a personal or corporate account, or what data they shared. Manual surveys and self-reporting are unreliable. Procurement records miss free-tier signups and personal accounts. None of these approaches catch AI browser extensions or OAuth-authorized AI agents accessing corporate data autonomously.

Push discovers AI tools through browser-level observation across four dimensions: AI apps accessed, AI browser extensions installed, AI OAuth integrations connected, and agentic browsers in use — with visibility into account type, data input, and permissions granted.

Blocking AI tools entirely via SWG or firewall is increasingly impractical as teams adopt them for legitimate work. Acceptable use policies tell employees not to paste sensitive data but provide no enforcement. Endpoint DLP monitors file operations at the OS level but doesn't see what users type or paste inside browser sessions. Network DLP sees traffic to AI domains but can't inspect prompt content.

Push provides clipboard telemetry with configurable regex-based rules that flag sensitive patterns — source code, API keys, customer data, PII — being pasted into AI tools. This lets you allow AI usage while monitoring and controlling what data goes in. Push can also block unsanctioned AI applications entirely, or enforce that approved tools are only accessed through organizational accounts.

The use of AI tools, extensions, and integrations without organizational approval or security oversight. The risks are amplified versus traditional shadow IT because AI tools actively process and potentially retain the data employees share — prompts, pasted content, uploaded files, and OAuth-authorized data access.

Push tracks shadow AI across four dimensions: apps, tenants (personal accounts on approved tools), extensions, and OAuth integrations. Read about shadow AI.

SWGs and firewalls can block known AI domains at the network layer, but they miss new AI tools, AI features embedded in existing apps, and AI browser extensions. They also require traffic routing through the proxy — no coverage on BYOD or off-network devices. URL-level blocking is also blunt: you can block chatgpt.com but can't distinguish between an approved organizational account and a personal one.

Push provides application-level blocking at the browser layer with more granularity — block specific AI apps, categories, or all tools not on an approved list. For graduated enforcement, Push supports discovery → monitoring → app banners with guidance → selective blocking → allowlist enforcement.

Most organizations start with a written policy — approved tools, acceptable use guidelines, data handling rules. The problem is enforcement. Network-level blocking is too blunt (block the domain or don't). Acceptable use policies rely on employee compliance with no technical backstop. And you can't write a meaningful policy without first knowing what AI tools are actually in use.

Start with discovery, then enforce in stages. Push discovers all AI tools, extensions, and OAuth integrations in use, then supports graduated enforcement: discover → monitor → app banners with guidance → selective blocking → allowlist enforcement. This lets you write policy based on actual usage data rather than assumptions.

Start with discovery — you can't enforce a policy on tools you don't know about. Push discovers all AI tools, extensions, and OAuth integrations in use. Then classify AI tools as approved, restricted, or blocked using Push's app categorization. Enforce through browser-level controls.

Push enables graduated enforcement: discover → monitor → guide → block selectively → enforce allowlist.

They can identify that a user visited an AI tool — they see the destination URL. But they can't see what the user does inside it: prompts, pasted data, file uploads, or OAuth permissions. An SWG sees "user visited chatgpt.com"; Push sees that the user pasted source code into the prompt.

Push's browser-level visibility sees the actual user interaction.

Most security tools offer a binary choice — block AI domains or allow them. SWGs and firewalls operate at the URL level with no middle ground between full access and full block. CASB can apply policies to sanctioned apps via API, but that doesn't cover the long tail of AI tools employees adopt on their own.

Push supports a monitoring-first approach: discover AI tools in use, observe what data employees share, and generate alerts for policy violations without blocking. App banners surface policy guidance in the browser without preventing access. This lets you understand usage patterns and identify real risks before implementing restrictions.

AI visibility is knowing which AI tools employees use, what data they share, and what permissions they've granted. AI governance is enforcing policies based on that visibility — blocking unauthorized tools, restricting data input, controlling OAuth permissions, and ensuring approved tools are used through organizational accounts.

Visibility without governance is awareness without action. Governance without visibility is policy without enforcement. Push provides both — AI discovery and monitoring for visibility, plus application blocking, clipboard rules, and OAuth consent controls for governance. Read about the AI security maturity model.

It depends on context. ChatGPT used through an approved organizational account with sanctioned data handling policies is managed AI. ChatGPT accessed through a personal account, an unauthorized browser extension, or without organizational awareness is shadow AI.

The distinction isn't the tool — it's the oversight. Push detects both scenarios: which AI tools are in use, whether users access them through personal or organizational accounts, and what data they share. Tenant-level visibility matters because the same tool can be sanctioned or shadow depending on how it's accessed. Read about shadow AI.

DSPM (Data Security Posture Management) monitors data at rest in cloud services and SaaS applications — it can flag sensitive data stored in an AI tool after the fact. It doesn't prevent data from being shared with AI tools in real time, and it can't discover AI tools that aren't connected via API.

Push operates at the point of interaction — detecting when employees use AI tools, what data they paste or upload, and what OAuth permissions they grant, in real time. DSPM tells you what's already there; Push prevents sensitive data from getting there. The two are complementary for different stages of the data lifecycle. Read about AI security controls.