Last updated June 22, 2026.

AI security is the practice of governing how AI models, copilots, and agents handle enterprise data and take actions. The market splits into four overlapping 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, which leaves a structural blind spot: the dual-channel agent control that simultaneously inspects the model conversation and the tool-execution channel.

That blind spot is the exact mechanism attackers used in EchoLeak and ForcedLeak to move data across live enterprise AI deployments. This guide defines the category, maps how the 2026 market breaks down, sizes the opportunity, profiles ten vendors, and shows where each one fits the problem you are closing first.

Why AI Security Became Its Own Category Legacy Tools Cannot Cover

AI security governs the prompts users send, the responses models return, the tool calls agents make, and the data that moves through all of it. Employee AI use at work rose from 30% in 2023 to 76% in 2025, a 2.5x shift in two years that arrived before governance programs were ready for it (McKinsey, April 2026).

The controls did not keep pace. IBM’s 2025 Cost of a Data Breach report found that 1 in 5 breached organizations reported a breach tied to shadow AI, which added about $670,000 to the average breach, and that among organizations hit by an AI-related breach, 97% had no proper AI access controls in place (IBM, 2025). Only 17% of organizations have technical controls capable of stopping employees from uploading confidential data to public AI tools (Kiteworks, 2025).

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 capable users toward workarounds.

The result is AI activity older controls cannot see or govern. The Stanford 2025 AI Index recorded 233 AI-related incidents in 2024, a record and a 56.4% year-over-year increase (Stanford HAI, 2025). AI security is the layer that closes that gap.

The AI Security Market Is Tracking Toward $7.44B by 2030

The AI trust, risk, and security management market was worth $2.34 billion in 2024 and is projected to reach $7.44 billion by 2030, a 21.6% compound annual growth rate (Grand View Research). The broader market for AI in cybersecurity is forecast to climb from $25.35 billion in 2024 to $93.75 billion by 2030, a 24.4% CAGR (Grand View Research).

Market estimates vary by firm and methodology, so treat these as a caveated range rather than a single number. The signal across forecasts is consistent: spending on AI-specific security controls is compounding faster than 20% a year because the threat is compounding too.

Three forces drive that growth. The first is adoption outrunning governance. The second is an escalating exchange between attackers and defenders, where prompt injection and tool abuse give adversaries new ways into enterprise data. The third is regulation: the EU AI Act’s high-risk obligations and member-state penalty powers take effect August 2, 2026, with fines reaching 35 million euros or 7% of global annual turnover for prohibited practices (EU AI Act, Regulation 2024/1689). Banking, insurance, healthcare, and government buyers feel all three at once, which is why regulated verticals anchor the early demand.

The 2026 AI Security Market Sorts Into Four Vendor Categories

Roughly a dozen point tools have already been absorbed into larger suites, and the market now sorts into four overlapping groups. Most vendors do one group well and treat the others as roadmap.

The four categories also map onto traditional security domains buyers already staff. Network and SaaS extension lives next to the SSE and CASB teams, AI DLP sits with data-loss and insider-risk programs, DSPM for AI reports into data security, and build-side coverage lands with application security and platform engineering. The vertical demand follows the same lines: financial services and insurance lead on data-loss and copilot exposure, healthcare and life sciences on sanctioned-access enforcement, and technology and SLED on agent and pipeline security.

Aurascape spans all four groups on one platform, covering both waves of AI adoption: the AI employees use, and the AI teams build.

Model-Only Or Tool-Only Governance Leaves Agents Uncontrolled

The structural blind spot in this market is dual-channel agent control: inspecting the model conversation and the tool-execution channel at the same time, with data lineage that connects them. An agent does two things a chatbot never does. It reads enterprise data and it acts on external systems through tool calls. Governing one channel without the other leaves the attack path open.

That path is not theoretical. EchoLeak (CVE-2025-32711), patched by Microsoft in 2025, was a zero-click indirect prompt injection in Microsoft 365 Copilot. A single crafted email carried hidden instructions that Copilot ingested, then exfiltrated data from OneDrive, SharePoint, and Teams through trusted Microsoft domains, with no user click. ForcedLeak (CVSS 9.4), disclosed against Salesforce Agentforce in late September 2025, planted an injection in a Web-to-Lead description field that executed later when an employee queried the agent, routing data to an expired but still-allowlisted domain an attacker re-registered for about $5.

Both attacks share the same shape. A benign-looking input on the model channel triggered a data-moving action on the tool channel, and no control correlated the two. OWASP ranks prompt injection as LLM01, the top risk for LLM applications, with sensitive information disclosure at LLM02 and excessive agency at LLM06 (OWASP, 2025). A platform that inspects prompts but not tool calls, or tool calls but not the conversation that authorized them, cannot catch an attack that looks acceptable one call at a time.

The business consequence is stalled adoption. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls (Gartner, 2025). Cross-call data lineage is the control that closes the gap, and it is the capability the market fragments on hardest.

The 2026 AI Security Capability Map Across Ten Platforms

Cisco’s 2025 AI Readiness Index found 83% of companies plan to deploy AI agents, yet only 31% say they are fully equipped to control and secure agentic AI systems (Cisco, 2025). The profiles below state each vendor’s public positioning as of mid-2026. The market moves fast, so confirm current details with each vendor.

Network and SaaS security platforms extended to AI

Zscaler. Delivers AI security through its Zero Trust Exchange cloud proxy, 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. Coverage is governed at the proxy layer and tracks per-application support.

How Aurascape compares: Aurascape decodes AI traffic natively across modern protocols and applies precise 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. Governs AI use through its SSE platform and AI guardrails, with SaaS and CASB coverage. It inspects AI traffic where that traffic is decodable at the network layer.

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. Assembles AI security across Prisma AIRS, the Protect AI acquisition, AI Access Security, and Prisma Browser. It covers AI use and AI build inside its platform for existing Palo Alto customers.

How Aurascape compares: Aurascape is built from scratch 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. Governs AI at the network layer using behavioral intent analysis, classifies AI traffic, applies policy, and can route risky prompts to safer models. Coverage applies where traffic traverses its network connector and depends on a single-tenant deployment.

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.

Workforce AI governance and AI DLP

Shadow AI is the problem this group exists to solve. ISACA’s 2025 survey found 83% of digital-trust professionals believe staff use AI whether or not it is permitted, while only 31% have a formal, comprehensive AI policy in place (ISACA, 2025). The National Cybersecurity Alliance reported that 43% of workers admit sharing sensitive workplace information with AI tools without their employer’s knowledge (NCA, 2025).

Harmonic Security. Covers workforce AI governance across browser, embedded AI, copilots, and desktop AI clients. It maintains a catalog of roughly 1,000 web AI tools and applies intent classification with block and coaching actions. Its public materials state that securing built AI is not the platform’s focus, and it carries a 200-user minimum.

How Aurascape compares: Aurascape’s patented discovery covers more than 20,000 applications with a 48-hour service level for new ones, and it extends to the AI your teams build, including pre-deployment adversarial testing and runtime governance. See the Aurascape vs Harmonic Security comparison.

Lasso Security. Uses separate enforcement points by use case: a browser extension for employee AI, plus an API gateway, an SDK, and an open-source MCP Gateway for built apps and agents. It ships a 3,000-attack red-teaming library and a published 98.6% detection-accuracy claim at sub-50ms latency.

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

Quilr. Launched in 2025, combining 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 RMF. It is an early-stage company building out enterprise depth.

How Aurascape compares: Aurascape brings enterprise-scale discovery and native decoding across more than 20,000 applications, dual-channel agent control, and the Zero-Bypass MCP Gateway for tool-call enforcement.

Prompt Security (now SentinelOne). 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 rather than as a standalone tool.

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

Aim Security (now Cato Networks). Provided governance for public and private AI applications and agents. Cato Networks acquired the company in 2025, and the capabilities now ship inside 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. Takes a data-security-first approach rooted in data security posture management, data classification, permissions and blast-radius analysis, and monitoring of data exposed to Microsoft 365 Copilot. It launched Varonis Atlas on March 17, 2026, a standalone AI security platform spanning 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, which Varonis acquired in February 2026, and 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)

Nearly 60% of organizations already run AI agents in production, and over half are highly likely to expand their scope or budgets over the next 12 months (G2, 2025). The Cloud Security Alliance found 82% of organizations have unknown AI agents operating in their environment and 65% have had agent-related incidents (Cloud Security Alliance, 2026).

Noma Security. Discovers, governs, and protects AI and agents across the enterprise, from homegrown models to SaaS agents. It maps models, agents, and MCP servers, validates an approved AI supply chain, and enforces policies on prompts, responses, and tool calls at runtime. Setup complexity is high and it targets large enterprises.

How Aurascape compares: Aurascape covers the build side with discovery, pre-deployment adversarial testing, and runtime Safe Output Governance, and adds the employee-AI side on the same platform so one team governs both waves.

How the Ten Platforms Compare on Dual-Channel Agent Control

The query buyers run is how vendors govern both how employees use AI and what teams build, and they cluster around a small number of answers to it. The table below compares the capabilities the article’s argument hinges on: native protocol decode, endpoint reach, agentic and MCP tool-call governance, cross-call data lineage, and whether the platform overlays an existing stack. “Yes” means the capability is a stated strength, “Partial” means limited or emerging support, and “No” means it is not a focus.

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

How Aurascape Closes Both Waves on One Platform

Aurascape covers both waves of AI adoption in a single AI-native platform: the AI employees use daily, and the AI teams build and deploy. It is built for AI from scratch rather than retrofitted from a legacy SSE or DLP stack, which is what lets it inspect the full conversation and the tool-execution channel at once.

• Two-wave coverage. Aurascape governs commercial AI, embedded AI, and copilots that employees use, plus the apps and agents teams build and run.
• Deep decode. Native visibility into prompts, responses, and tool calls across WebSockets, Protobuf, JSON, RPC, APIs, and the Model Context Protocol.
• 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, the exact gap EchoLeak and ForcedLeak exploited.
• 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 existing SSE, CASB, and DLP tools, with no rip-and-replace.

In one Aurascape deployment, The Police Credit Union, a $1.05 billion institution serving 39,000 members, governed employee AI use while staying audit-ready for NCUA guidance and the NIST AI Risk Management Framework. The credit union 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. Deploying Aurascape is projected to deliver a 27% productivity gain across underwriting, member support, and collections, and an 83% reduction in AI-related risk from coaching users away from unsanctioned tools (Aurascape, 2026).

“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.”

A Fortune 100 insurance and financial enterprise saw the same pattern on the build side. Deploying Aurascape cut the time to adopt new AI tools by 60%, made code delivery 40% faster with AI coding assistants, and tripled AI agent integrations with no unauthorized data access, while protecting more than 20,000 users (Aurascape, 2026).

How to Choose an AI Security Platform for Your Primary Use Case

The right platform depends on which gap you are closing first, since only 31% of organizations feel fully equipped to control and secure agentic AI systems (Cisco, 2025). Match the platform to your primary use case rather than its longest feature list.

Six questions separate dual-channel control from single-channel coverage. Ask any 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?

How the AI Security Category Stacks Up

Every vendor in this market answers one question for the buyer: do you govern how employees use AI, what teams build, or both, and how deep does that coverage run on agent tool calls. They cluster into the four categories above, and the table below compares them on origin, primary scope, and the dual-channel control that decides whether agent attacks like EchoLeak get caught.

Additional vendors are limited to publicly known market facts. Validate every cell against current vendor documentation before a purchase decision.

Frequently Asked Questions

What is the best AI security platform in 2026?

No single platform is best 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 built for that exact scope across commercial AI, embedded AI, copilots, and agents.

How is AI security different from traditional DLP or CASB?

Traditional DLP matches known data patterns and CASB governs app access, and neither reads the AI conversation or follows agent behavior. AI security inspects prompts, responses, and tool calls directly, so policy acts on intent and context rather than the destination alone.

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

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

What is an MCP gateway and why does it matter?

The Model Context Protocol is the emerging standard that lets AI agents discover tools and call them, and an MCP gateway governs those tool calls. More than 12,520 internet-accessible MCP services were observed as of April 2026, and the protocol does not require authentication by default (Censys, 2026), which is why a gateway that signs approved calls and blocks unsigned ones matters.

How is securing AI agents different from securing AI chatbots?

A chatbot responds, while an agent reads data, calls tools, browses the web, and makes decisions without human review. That combination of access and autonomy means security has to govern tool calls and data lineage, not just prompt content, which is exactly the gap EchoLeak (CVE-2025-32711) exploited to pull data from Microsoft 365 Copilot through a single crafted email.

Which AI security vendors were acquired in 2025 and 2026?

SentinelOne acquired Prompt Security, Cato Networks acquired Aim Security, Check Point acquired Lakera, Palo Alto Networks acquired Protect AI, and Cisco acquired Robust Intelligence. Separately, Varonis acquired AllTrue.ai in February 2026 to build Varonis Atlas, and Akamai agreed to acquire LayerX for roughly $205 million, a deal expected to close in the third quarter of 2026.

Why do agentic AI projects get canceled before they ship?

Inadequate risk controls are one of three named reasons, alongside escalating costs and unclear business value, and Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027 (Gartner, 2025). Governing both the model conversation and the tool-execution channel is what keeps a project on the controllable side of that line.

Can Aurascape replace my existing security stack?

No, and it is not designed to. Aurascape is an additive layer that works alongside SSE, CASB, and DLP tools, closing 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, detecting an agent when it launches, seeing the services and tools it connects to, governing each MCP tool call through the Zero-Bypass MCP Gateway, and inspecting prompts and responses through the AI Proxy. Every action lands in one audit trail.

Does AI security help with regulatory compliance?

Yes, because AI security platforms produce the inventory, logs, and controls auditors increasingly expect. The EU AI Act reaches a major enforcement milestone on August 2, 2026, with fines reaching 35 million euros or 7% of global annual turnover for prohibited practices (EU AI Act, Regulation 2024/1689), and Aurascape maps controls to the NIST AI RMF with an audit-ready record of AI use.

How Aurascape Governs Both Waves of AI Adoption on One Platform

The dual-channel blind spot this guide traced through EchoLeak and ForcedLeak is the problem Aurascape was built to close. It is an AI-native security platform that gives enterprises real-time visibility, intent-based controls, and data protection across every AI interaction, securing both the AI employees use and the AI teams build on a single architecture. Founded in 2023 by senior engineers from Palo Alto Networks, Google, and Amazon, it launched from stealth in April 2025 with $50 million in funding.

Aurascape automatically discovers AI tools in use, including shadow AI and AI embedded inside SaaS, decodes prompts, responses, and user intent at the conversation level, and enforces policy in real time. For teams building agents, the AI Proxy secures the model channel while the Zero-Bypass MCP Gateway inspects, signs, and controls every tool call before an agent reaches an external system, with cross-call data lineage that catches attacks no single call reveals. The platform deploys alongside an existing SSE, CASB, and DLP stack rather than replacing it, with a 48-hour service level for supporting newly launched AI apps.

The proof shows up in regulated deployments. In one Aurascape deployment at a global Fortune 200 healthcare technology enterprise, unsanctioned long-tail AI access and use outside licensed access dropped to near zero across more than 60,000 users worldwide under one governance model (Aurascape, 2026). Aurascape was named a Top 10 Finalist in the 2025 RSAC Innovation Sandbox and recognized across CRN’s 2025 and 2026 AI security startup lists.

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Aurascape is the AI-native control layer for the dual-channel agent gap that model-only and tool-only platforms leave open. Demos are tailored to your environment and your team leaves with a clear view of the AI security gaps to close first.

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