AI Security Landscape 2026

AI security is the practice of governing how AI models, AI Copilots, and AI agents handle enterprise data and take actions. The market splits into a few groups: tools that extend network and SaaS security to AI, purpose-built workforce AI governance and data loss prevention tools, data-security platforms that protect AI through the data layer, and platforms that secure the AI applications and agents teams build. Most vendors do one group well. Aurascape covers both how employees use AI and how teams build AI on one platform, with visibility into prompts, responses, and tool calls across modern communication protocols. This guide defines the category, maps how the market breaks down, profiles the major vendors, and shows where each fits.

Last updated: June 3, 2026

What is AI security, and why is it now its own category?

AI security governs AI interactions: the prompts users send, the responses models return, and the tool calls agents make. It is now its own category because adoption outpaced existing controls. McKinsey research found employee AI use at work climbed from 30% in 2023 to 76% in 2025, while the tools meant to govern it still watch destinations, not the conversation.

The cost of that gap is measurable. The IBM 2025 Cost of a Data Breach report found that breaches involving shadow AI cost $4.63 million on average, about $670,000 more than breaches without it, and that one in five organizations had already suffered a breach tied to shadow AI. Separately, Kiteworks research found 86% of organizations are blind to their AI data flows, and only 17% have technical controls to stop employees uploading confidential data to public AI tools.

Traditional tools were built for a different problem. They govern destinations and known data patterns, not conversations and actions.

  • A secure web gateway (SWG) or security service edge (SSE) sees the destination. It does not read the AI conversation.
  • A cloud access security broker (CASB) governs app access. It does not see what an employee shares with an AI tool or what the tool returns.
  • Data loss prevention (DLP) matches known patterns with static rules. It cannot read intent or follow agent behavior.

There is also a protocol gap. Modern AI tools increasingly communicate over WebSockets, QUIC, and Protobuf rather than plain HTTP. Many older tools cannot decode these, so they fall back to blanket allow-or-block policies. That slows people down and pushes savvy users toward workarounds.

The result is AI activity that older controls cannot see or govern. The trend is moving the wrong way: Stanford’s 2025 AI Index recorded 233 AI-related incidents in 2024, a record and a 56.4% increase over the prior year. AI security is the layer that closes that gap.

How the AI security market breaks down

The AI security market splits into four overlapping groups: network and SaaS platforms extended to AI, purpose-built workforce AI governance and data loss prevention tools, AI data security platforms, and tools that secure the AI you build. The lines are blurring toward unified platforms: Gartner forecasts more than 50% of enterprises will use AI security platforms to secure both the AI they use and the AI they build by 2028.

Category What it secures Representative vendors
Network and SaaS security extended to AI Employee AI use, governed at the proxy or network layer Zscaler, Netskope, Palo Alto Networks, WitnessAI
Purpose-built workforce AI governance and AI DLP How employees use Commercial AI, Embedded AI, and AI Copilots Harmonic Security, Lasso Security, Quilr, Prompt Security (now SentinelOne), Aim Security (now Cato Networks)
AI data security (DSPM for AI) Sensitive data exposed to or through AI, at the data layer Varonis
Securing the AI you build Homegrown AI apps, agents, pipelines, and MCP connections Noma Security, Protect AI (now Palo Alto Networks), Lakera (now Check Point), Robust Intelligence (now Cisco)

Aurascape spans these groups. It is one platform for both waves of AI adoption: the AI employees use, and the AI teams build.

AI security vendors compared

More than a dozen vendors now sell AI security, grouped into four categories that each solve a different slice of the problem. The profiles below describe each vendor’s public positioning as of mid-2026, lead with what the vendor does well, then note where Aurascape’s approach differs. The market moves fast, so confirm current capabilities with each vendor before you buy.

Network and SaaS security platforms extended to AI

Zscaler

Zscaler delivers AI security through its Zero Trust Exchange cloud proxy. It discovers AI applications using its existing CASB shadow-IT framework, applies inline threat detection and DLP through AI Guard, and announced an MCP Gateway in early 2026. It is a strong fit for organizations standardizing on Zscaler SSE.

How Aurascape compares: Aurascape decodes AI traffic natively across modern protocols and applies policy at the tool-call level, with data lineage that tracks information across chained actions. It runs alongside an existing SSE such as Zscaler rather than replacing it. See the Aurascape vs Zscaler comparison.

Netskope

Netskope governs AI use through its SSE platform and AI guardrails, backed by broad SaaS and CASB coverage. It inspects AI traffic where that traffic is decodable. It is a strong fit for existing Netskope SSE customers.

How Aurascape compares: Aurascape’s deep decoders reach the long tail of AI applications and non-browser activity, and inline inspection preserves streaming responses so features like deep research keep flowing. Aurascape is additive to a Netskope deployment. See the Aurascape vs Netskope comparison.

Palo Alto Networks

Palo Alto Networks has assembled AI security across Prisma AIRS, the Protect AI acquisition, AI Access Security, and Prisma Browser. It covers both AI use and AI build inside its platform and is a strong fit for Palo Alto platform customers.

How Aurascape compares: Aurascape is purpose-built as one AI interaction layer. It decodes modern protocols natively, sets policy at the tool-call level, and commits to a 48-hour service level for supporting new AI applications. It is additive to an existing stack. See the Aurascape vs Palo Alto Networks comparison.

WitnessAI

WitnessAI governs AI at the network layer using behavioral intent analysis. It classifies AI traffic, applies policy, and can route risky prompts to safer models. Coverage applies where traffic traverses its network connector.

How Aurascape compares: Aurascape enforces policy at the AI interaction itself, across browsers, desktop apps, Embedded AI, and AI agents, without depending on network routing. See the Aurascape vs WitnessAI comparison.

Purpose-built workforce AI governance and AI DLP

Shadow AI is the problem this group exists to solve. ISACA’s 2025 survey found 81% of digital-trust professionals believe employees use AI whether or not it is permitted, while only 28% of organizations have a comprehensive AI policy. A January 2026 BlackFog survey reported that 49% of workers used AI tools their employer had not sanctioned, and 33% admitted sharing enterprise data with them.

Harmonic Security

Harmonic Security focuses on workforce AI governance across browser, Embedded AI, AI Copilots, and desktop AI clients. It maintains a catalog of roughly 1,000 high-volume web AI tools, updated weekly, and applies intent classification with block and coaching actions. Its public materials state that securing built AI is not the platform’s focus.

How Aurascape compares: Aurascape’s patented discovery covers tens of thousands of applications with a 48-hour service level for new ones, and it extends to the AI your teams build, including pre-deployment testing and runtime governance. See the Aurascape vs Harmonic Security comparison.

Lasso Security

Lasso Security uses multiple enforcement points by use case: a browser extension for employee AI, plus an API gateway, an SDK, and an MCP Gateway for built apps and agents. It has invested heavily in features for application security and AI engineering teams.

How Aurascape compares: Aurascape provides one unified interaction layer across employee and built AI, with consistent coverage for desktop and non-browser agents such as Claude Code and OpenClaw. See the Aurascape vs Lasso Security comparison.

Quilr

Quilr launched in 2025 and combines DLP and AI security with an agentic, human-risk approach. It offers in-flow employee coaching, shadow AI discovery, and prompt injection defense across the browser, APIs, IDEs, an AI model gateway, and an MCP gateway, with controls mapped to the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework (AI RMF). Quilr is an early-stage company building out enterprise depth.

How Aurascape compares: Aurascape brings enterprise-scale discovery and native decoding across tens of thousands of applications, dual-channel agent control, and the Zero-Bypass MCP Gateway for tool-call enforcement.

Prompt Security (now part of SentinelOne)

Prompt Security built inline inspection of prompts and responses, with threat detection and DLP for AI use. SentinelOne acquired the company in 2025, and the technology now ships inside the SentinelOne Singularity platform.

How Aurascape compares: Aurascape is a standalone, AI-native platform spanning both AI use and AI build, with native MCP tool-call governance and data lineage across chained calls.

Aim Security (now part of Cato Networks)

Aim Security provided governance for public and private AI applications and agents. Cato Networks acquired the company in 2025, and the capabilities are now delivered within the Cato SASE Cloud platform.

How Aurascape compares: Aurascape deploys across the network, endpoint, and API planes, and stays additive to whatever SASE or SSE you already run.

AI data security (DSPM for AI)

Varonis

Varonis takes a data-security-first approach, rooted in data security posture management (DSPM), data classification, permissions and blast-radius analysis, and monitoring of data exposed to Microsoft 365 Copilot. In March 2026 Varonis launched Varonis Atlas, a standalone AI security platform that spans AI inventory and shadow AI discovery, AI-SPM, AI pen testing, runtime guardrails, AI detection and response, and third-party AI risk. Atlas is built in part on AllTrue.ai, an AI trust, risk, and security management company Varonis acquired in February 2026, and it connects to the Varonis Data Security Platform for data context. Its thesis is that AI security starts with data security.

How Aurascape compares: Aurascape complements data-layer posture with inline visibility and control at the AI interaction itself. It inspects prompts, responses, and tool calls in real time across surfaces and modern protocols, and enforces DLP on AI-bound data flows, including embedded AI and agent tool calls.

Securing the AI you build (AI-SPM, agent and MCP security)

Agents are reaching production faster than the governance meant to secure them. Cisco’s 2025 AI Readiness Index found that only 31% of organizations feel fully equipped to control and secure the AI agents they are deploying. The platforms in this group focus on closing that gap, from pre-deployment testing to runtime control of agent and MCP tool calls.

Adoption is broad and accelerating. A 2025 G2 survey found nearly 60% of organizations already run AI agents in production, and Gartner predicts task-specific AI agents will appear in 40% of enterprise applications by 2026, up from less than 5% in 2025.

Noma Security

Noma Security is an independent platform for the build-and-run side of AI. It combines AI security posture management (AI-SPM), AI red teaming, runtime detection and response, and access control for AI agents and MCP servers. It is a strong fit for securing homegrown AI and coding agents.

How Aurascape compares: Aurascape covers the build side and the employee-use side on one platform, including Commercial AI and Embedded AI governance, with the Zero-Bypass MCP Gateway enforcing every tool call before it runs.

Others shaped by 2025 consolidation

Several specialist vendors are now part of larger suites. Protect AI runs inside Palo Alto Networks Prisma AIRS for model scanning and red teaming. Lakera, focused on agentic AI protection and red teaming, is now part of Check Point. Robust Intelligence, focused on model validation in the AI pipeline, is now part of Cisco. Pillar Security and Cyera remain independent in adjacent areas, runtime AI application security and data security respectively. The pattern is clear: the market is folding point tools into broader platforms.

How Aurascape compares: Aurascape is one platform built for both waves of AI adoption, rather than a feature inside a broader suite.

AI security platform comparison table

The comparison below scores ten vendors across eight AI security capabilities, from securing employee AI use to governing agent tool calls. Aurascape is the only platform marked a stated strength on all eight. Network platforms tend to lead on employee AI use, and build-side tools lead on agent governance, but few cover both sides and the full protocol range at once.

This table reflects public positioning as of mid-2026. A “Yes” means the capability is a stated strength, “Partial” means limited or emerging support, and a “No” means it is not a focus of the platform.

Capability Aurascape Zscaler Netskope Palo Alto Networks WitnessAI Harmonic Lasso Quilr Varonis Noma
Secures employee AI use (Commercial AI, Embedded AI, AI Copilots) Yes Yes Yes Yes Yes Yes Yes Yes Partial No
Secures the AI you build (apps, agents, pipelines) Yes Partial Partial Yes Partial No Yes Partial Yes Yes
Native decode across modern protocols (beyond HTTP and URL) Yes Partial Partial Partial Partial Partial Partial Partial No Partial
Endpoint and non-browser AI coverage (CLI, IDEs, desktop agents) Yes Partial Partial Partial Partial Partial Partial Partial No Partial
Agentic and MCP tool-call governance Yes Partial Partial Partial Partial Partial Yes Partial Partial Yes
Cross-call data lineage Yes No No Partial No No Partial No Partial Partial
Pre-deployment adversarial testing Yes No No Yes No No Partial No Yes Yes
Additive overlay (works alongside existing SSE, CASB, DLP) Yes No No No Partial Yes Yes Yes Yes Yes

Capabilities evolve quickly, especially for agentic and MCP features. Treat this table as a starting point, then validate against each vendor’s current documentation.

Where Aurascape fits

Aurascape is one platform for both waves of AI adoption: the AI employees use, and the AI teams build. It decodes prompts, responses, and tool calls across modern protocols, governs every agent tool call through the Zero-Bypass MCP Gateway and AI Proxy, and tracks data lineage across chained actions. It runs alongside your existing SSE, CASB, and DLP, with no rip-and-replace.

  • Two-Wave coverage. Aurascape governs Commercial AI, Embedded AI, and AI Copilots that employees use, plus the apps and AI agents your teams build and run.
  • Deep decode. Aurascape provides native visibility into prompts, responses, and tool calls across WebSockets, Protobuf, JSON, RPC, APIs, and the Model Context Protocol (MCP).
  • Dual-channel agent control. The AI Proxy secures the model channel. The Zero-Bypass MCP Gateway secures the tool-execution channel. Together they correlate intent with action across both legs of every agent interaction.
  • Zero-Bypass MCP Gateway. Aurascape cryptographically signs approved tool calls. Unsigned calls cannot reach the tool or the model, so unauthorized actions cannot run.
  • Cross-call data lineage. Aurascape tracks data across chained actions and catches attacks that look benign one call at a time.
  • See, Test, Protect for built AI. Aurascape discovers what teams have built, tests it against real attack vectors before launch, and governs it at runtime with Safe Output Governance.
  • Additive. Aurascape works alongside your existing SSE, CASB, and DLP tools. No rip-and-replace.

Aurascape in practice: The Police Credit Union

The Police Credit Union (TPCU), a $1.05 billion institution serving 39,000 members, used Aurascape to govern employee AI use while staying audit-ready for NCUA AI guidance and the NIST AI Risk Management Framework. TPCU deployed in two phases: visibility first, building an automated AI app inventory with risk and data-exposure assessments, then protection, enforcing enterprise accounts and credit-union-specific classifiers for SSNs, account numbers, and card data. The credit union projects a 27% productivity gain from letting staff use AI across underwriting, member support, and collections, and an 83% reduction in AI-related risk from coaching users away from unsanctioned tools.

“Without Aurascape, we had seriously considered blocking all GenAI usage,” said Victor To, CISSP, Senior Security Architect at The Police Credit Union. “That would have held us back while others moved forward.”

Read the full Police Credit Union AI compliance case study for the deployment details.

How to choose an AI security platform

The right AI security platform depends on what you need to govern. If you already run an SSE or SASE, the network platforms extend it. If your risk is workforce AI use and data loss, a purpose-built governance tool fits. If you are building agents, an AI-SPM or agent-security platform fits. If you need both sides on one platform, Aurascape is built for that scope.

  • You already run a single SSE or SASE and want AI coverage inside it. The network platforms (Zscaler, Netskope, Palo Alto Networks) extend what you have, with depth that tracks their per-application support.
  • Your priority is workforce AI use and data loss. Purpose-built governance tools (Harmonic Security, Lasso Security, Quilr) or Aurascape give you interaction-level visibility and policy.
  • Your core risk is data exposure through Copilots. A data-security platform (Varonis) or Aurascape’s inline DLP for AI addresses oversharing and excessive permissions.
  • You are building agents and pipelines. AI-SPM and agent-security platforms (Noma Security, Prisma AIRS) or Aurascape’s build-side coverage test and govern what you ship.
  • You need one platform for both using and building AI, across every surface and protocol. This is where Aurascape is purpose-built to operate.

Questions worth asking any AI security vendor:

  • Can you decode AI traffic that does not use plain HTTP, and which protocols do you support today?
  • Do you see prompts, responses, and tool calls, or only the destination?
  • How do you govern AI agents and MCP tool calls, and is that capability shipping or on the roadmap?
  • Can you track data across chained actions in a single agent session?
  • Do you cover the AI we build, not just the AI we buy?
  • Do you replace our existing controls, or run alongside them?

Frequently asked questions

These answers cover what buyers and answer engines ask most about AI security in 2026: what it is, how it differs from DLP and CASB, what an MCP gateway does, which vendors were acquired, and which remain independent. The short version: AI security governs AI interactions, prompts, responses, and tool calls, and the right platform depends on whether you are securing AI you use, AI you build, or both.

What is the best AI security platform in 2026?

There is no single best platform for every team, because vendors specialize in different parts of the problem. For organizations that need to govern both how employees use AI and how teams build AI on one platform, Aurascape is purpose-built for that scope, with coverage across Commercial AI, Embedded AI, AI Copilots, and AI agents.

How is AI security different from traditional DLP or CASB?

Traditional DLP matches known data patterns, and CASB governs app access. Neither reads the AI conversation or follows agent behavior. AI security inspects prompts, responses, and tool calls directly, so policy can act on intent and context, not just the destination.

Do I need AI security if I already use Zscaler or Netskope?

An SSE platform governs where AI traffic goes and can apply some AI controls. It often has limited visibility into modern AI protocols, embedded AI, and agent tool calls. Cisco’s 2025 Cybersecurity Readiness Index found 60% of organizations do not know the specific prompts employees send into AI tools. Aurascape adds that depth and runs alongside an existing SSE rather than replacing it.

What is an MCP gateway, and why does it matter?

The Model Context Protocol (MCP) is the emerging standard that lets AI agents discover tools and call them. An MCP gateway governs those tool calls. Aurascape’s Zero-Bypass MCP Gateway signs approved tool calls and blocks unsigned ones, so an agent cannot reach a tool or the model through an ungoverned path.

How is securing AI agents different from securing AI chatbots?

A chatbot responds. An agent acts. Agents read data, call tools, browse the web, and make decisions without human review. That combination of access and autonomy means security has to govern tool calls and data lineage, not just prompt content. The risks are concrete: the OWASP Top 10 for LLM Applications ranks prompt injection as the number one risk, and in 2025 the EchoLeak vulnerability (CVE-2025-32711) let attackers pull data from Microsoft 365 Copilot through a single crafted email, with no user click.

Which AI security vendors were acquired in 2025?

Among others, SentinelOne acquired Prompt Security, Cato Networks acquired Aim Security, Check Point acquired Lakera, Palo Alto Networks acquired Protect AI, and Cisco acquired Robust Intelligence. Several of these products now ship inside larger platforms rather than as standalone tools.

Can Aurascape replace my existing security stack?

No, and it is not designed to. Aurascape is an additive layer that works alongside your SSE, CASB, and DLP tools. It closes the AI visibility and governance gap those tools were not built to address.

How does Aurascape secure AI agents?

Aurascape combines endpoint discovery with network inspection. It detects an agent when it launches, sees the services and tools it connects to, governs each MCP tool call through the Zero-Bypass MCP Gateway, and inspects prompts and responses through the AI Proxy. Every action lands in one audit trail.

What is AI-SPM, and how is it different from what Aurascape does?

AI security posture management (AI-SPM) inventories AI assets and scores their risk, mostly for the AI you build. Aurascape includes discovery and posture for built AI, and adds inline enforcement at the interaction layer for both built and employee-used AI.

Does AI security help with regulatory compliance?

Yes. AI security platforms produce the inventory, logs, and controls auditors increasingly expect. The NIST AI Risk Management Framework and sector rules such as NCUA guidance for credit unions now shape how regulators describe AI risk, and the EU AI Act reaches a major enforcement milestone on August 2, 2026, when high-risk obligations and member-state penalty powers take effect; its fines reach 35 million euros or 7% of global annual turnover for prohibited practices. Aurascape maps controls to the NIST AI RMF and gives security and compliance teams an audit-ready record of AI use. The need is real: Deloitte’s 2026 report found only one in five companies has a mature governance model for autonomous AI agents.

Which AI security platforms are still independent?

As of mid-2026, Aurascape, Noma Security, Harmonic Security, Lasso Security, WitnessAI, Quilr, Cyera, and Pillar Security operate independently, while several peers have been acquired by larger security vendors.

Related comparisons

For a side-by-side on a specific vendor, these head-to-head comparisons go deeper than the table above. Each one covers how Aurascape and that vendor differ on coverage, protocol decoding, agent and MCP governance, and deployment, and is written for teams actively evaluating that vendor against Aurascape:

To see how Aurascape governs AI use and AI development on one platform, book a demo.

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