In most enterprise environments, yes. Traditional security tools such as firewalls, secure web gateways, and data loss prevention (DLP) solutions were not designed to understand the full context of AI interactions.

AI introduces new security risks that traditional controls cannot easily detect. For example, a firewall may see traffic to an AI service, but cannot detect the specific intention involved, the sensitivity of data shared, or whether an AI agent crosses data boundaries, for example.

A dedicated AI security platform provides visibility into how AI tools are used, what data is shared with models, and what actions AI agents take. It allows security teams to enforce AI policies, prevent data leakage, and monitor AI usage and development wherever it occurs.

An effective AI acceptable use policy defines which AI tools are allowed, what data can be shared, and how AI systems may interact with corporate resources. To be enforceable, the policy must translate into technical controls rather than relying only on employee guidelines.

Security teams typically define rules such as restricting the input of sensitive data to AI tools, requiring corporate accounts for approved AI platforms, and limiting access to AI agents or integrations with internal systems.

Once policies are defined, organizations need visibility into AI activity across browsers, local agents, and embedded AI within SaaS and websites. Monitoring full AI conversations, MCP servers and tool calls, plus safe output governance and testing for homegrown AI tools, allows security teams to dynamically enforce policies in real time.

Agentic AI refers to AI systems that can take actions on behalf of users instead of simply generating responses. AI agents can interact with software tools, access data sources, execute workflows, and make decisions with limited human input.

This capability introduces new security risks because AI agents may have access to sensitive systems such as databases, internal APIs, or SaaS applications. If an agent receives malicious instructions, has loose parameters, or simply accesses an untrusted resource, it could expose sensitive data or perform unintended actions.

Security teams must treat AI agents like automated users with powerful privileges. Proper security requires monitoring agent activity, controlling which tools agents can access, and validating inputs and outputs to prevent manipulation or abuse.

Agentic AI risks are security threats that arise when AI agents take autonomous actions, such as calling APIs, reading files, writing to systems, and chaining decisions across tools and models,  rather than simply responding to prompts.

Common examples include:

Prompt injection: Malicious instructions hidden in a document, web page, or tool description hijack an agent mid-session, causing it to exfiltrate data or execute unauthorized commands without the user’s knowledge.

Data lineage risk: As agents pass context across multi-step workflows, sensitive information crosses system boundaries in ways traditional security tools cannot track.

Ungoverned agent usage: Employees connect AI agents to enterprise systems without IT oversight, including shadow MCP servers that expose internal systems to unvetted tool calls.

In-build vulnerabilities: IDOR flaws, poisoned MCP tool definitions, and vulnerable dependencies embedded in an agent’s code before it reaches production represent new risks that manual code review alone won’t catch.

Unsafe outputs: Without proper output governance, agents can surface harmful, misleading, or sensitive content to users or pass it downstream to connected systems.

Unlike the first wave of AI chatbots, AI agents act with speed and autonomy that outpaces existing security controls. That makes purpose-built agentic AI security essential for any enterprise leveraging or building AI agents.

Model Context Protocol (MCP) is an open standard that allows AI agents to connect to external tools, APIs, and data sources.
It defines how agents discover available MCP servers and tools, make tool calls, and retrieve data,  and for this reason MCP is becoming the primary communication layer between AI agents and the enterprise systems they interact with.

MCP is one area where agentic AI risk becomes concrete. Server connections, tool calls, and data retrievals all present new possibilities for data compromise and risk. Without visibility and control at this layer, organizations cannot govern what their agents are actually doing.

Key MCP security risks include:

Shadow MCP usage: Employees connect agents to unauthorized MCP servers without IT oversight, exposing internal systems to unvetted tool calls.

Tool poisoning: Compromised MCP tool descriptions manipulate agent behavior, causing unintended actions or data exfiltration.

Ungoverned tool calls: Agents access resources, modify records, or invoke workflows beyond their intended scope.

Cross-call data leakage: Sensitive data retrieved through MCP flows across multi-step workflows and crosses system boundaries undetected.

Securing MCP requires visibility and access control for every MCP server in use, enforceable policy for tool access, and real-time data and threat inspection on every tool call and its relevant data lineage before it reaches enterprise systems.

Major AI security risks include prompt injection, sensitive data leakage, shadow AI and MCP usage, insecure AI integrations, and excessive AI agent permissions.

Prompt injection attacks attempt to manipulate AI models into ignoring safeguards or revealing sensitive information. Shadow AI occurs when employees use unapproved AI tools and MCP servers without IT or security oversight. AI copilots connected to corporate data sources may also expose confidential information if permissions are not properly configured.

In addition, AI agents capable of executing tasks introduce operational risks if they interact with enterprise systems without proper monitoring and controls.

An AI security incident occurs when an AI tool, copilot, or agent causes unauthorized data exposure, policy violations, unintended system actions, or results in unauthorized access to sensitive systems.

Unlike traditional incidents triggered by malware or credential theft, AI incidents can originate from prompts, agent actions, and ungoverned integrations.

Common examples include an employee sharing confidential data with an AI agent running under a personal account, a prompt injection attack hijacking an agent mid-session to exfiltrate data or invoke unauthorized tool calls, and an AI agent connected via MCP modifying records or accessing sensitive systems beyond its intended scope.

What makes AI incidents uniquely difficult to detect is speed and scale. Agents execute actions across multiple systems in seconds, and traditional security tools have no visibility into the tool calls, data exchanges, or model interactions driving them. Purpose-built agentic AI security is the only way to catch and prevent these incidents before they cause damage.

Traditional security tools, including SSE, SASE, firewalls, and secure web gateways, were built to monitor network traffic and control web access. They can detect that an employee accessed an AI service. They cannot see what happened inside it.

That blind spot is critical. Traditional tools cannot inspect all AI prompts and responses, understand user attributes like account and authentication type, decode specific in-app intentions, monitor tool calls made through MCP, or detect sensitive data moving across multi-step agent workflows.

This is why securing AI usage and AI development requires purpose-built controls that monitor AI activity in real time, enforce policy based on the full context of every interaction, and provide full visibility into AI activity wherever it appears.

Shadow AI refers to the use of AI tools by employees without approval or visibility from IT or security teams.

Shadow AI is the next progression of the Shadow IT problem. The next phase of this problem is now referred to as Shadow Agents, or Shadow MCP.

Employees often experiment with AI tools to improve productivity, but this can result in sensitive company data being entered into external AI systems without protection.

Shadow AI makes it difficult to enforce data protection policies, monitor for threats, or track where corporate information is processed. Organizations must discover and monitor all AI tools in use and define policies for approved AI usage to reduce this risk.

Embedded AI refers to AI capabilities built directly into software platforms employees already use, including SaaS tools, productivity applications, websites, and enterprise systems.

It is not a separate AI tool an employee chooses to open. It is AI functionality that activates inside tools they use every day, often without a deliberate decision to engage it.

That invisibility is the core security challenge. Employees interact with embedded AI without realizing sensitive data is being processed, analyzed, or shared outside corporate boundaries. Traditional security tools treat AI-enabled apps and websites like any other application, missing the AI activity happening inside them entirely.

As AI becomes a native feature of nearly every enterprise platform, embedded AI represents one of the fastest-growing and least visible sources of data exposure and risk. Security teams need purpose-built visibility into what embedded AI tools are doing, what data they are touching, and whether that activity complies with organizational policy.

Prompt injection is an attack technique that manipulates an AI model by embedding malicious instructions inside content the model processes, such as a document, web page, email, or tool description.

The AI reads the hidden instructions and follows them, often without any indication to the user that its behavior has been compromised.

The consequences range from data exfiltration and policy bypass to unauthorized tool calls and lateral movement across connected systems. Prompt injection is especially dangerous in agentic systems, where an agent autonomously retrieves external content, makes tool calls,, and executes multi-step workflows with minimal human oversight.

Unlike traditional cyberattacks that target infrastructure, prompt injection targets the AI system itself. Defending against prompt injection requires real-time inspection of AI inputs and outputs during usage, guardrail evaluation before any agent deployment, and continuous monitoring of agent behavior in production.

Data entered into AI tools like ChatGPT or Microsoft Copilot may be used for model training, depending on the account type.

Consumer accounts often retain prompts and responses for model improvement unless the user explicitly opts out. Enterprise accounts typically prohibit training on customer data by contract, keeping interactions within defined security boundaries.

This distinction is a critical enterprise security risk. An employee using a personal ChatGPT account at work operates under consumer data terms. Sensitive company data entered into that prompt can be retained, reviewed, or used to train the model. The same action on a sanctioned enterprise account carries fundamentally different data protections.

Most organizations cannot enforce account type access and usage control at scale without purpose-built AI security controls. Monitoring whether employees access AI tools through personal or enterprise accounts, and enforcing policy based on that distinction in real time, is a foundational requirement for enterprise AI security in 2026.

When employees use personal AI accounts for work, corporate data can leave the enterprise security boundary entirely, exposing the organization to security and compliance risks.

Personal accounts for tools like ChatGPT, Claude, or Notion AI operate under consumer data terms, meaning sensitive information entered into a prompt may be retained, reviewed, or used to train underlying models — with no contractual protection for the organization.

The security risk compounds with AI agents. An employee running an AI agent from a personal account can connect that agent to enterprise systems via MCP, execute multi-step workflows, and exfiltrate sensitive data across tool calls, all outside the visibility of traditional security controls.

Detecting and controlling personal AI account usage at scale requires purpose-built AI security. Organizations need real-time visibility into whether employees are accessing AI tools through personal or enterprise accounts, with policy enforcement that acts on that distinction automatically, before sensitive data crosses the boundary.

AI copilots connected to enterprise systems can access large volumes of internal information such as documents, emails, and databases.

If permissions are not properly configured, the AI system may surface sensitive information that users should not normally see. Copilots can also combine data from multiple sources, meaning they might unintentionally expose confidential insights.

Organizations must apply strong access controls, data governance policies, and monitoring to ensure copilots index and share only appropriate information.

This often necessitates automated labeling of sensitive data, permission-aware policy, and the capability to instruct the AI copilot to unlearn previously learned sensitive information.

Discovering all AI tools in use across an enterprise requires purpose-built AI discovery, not traditional network monitoring.

Employees access AI through browsers, SaaS integrations, embedded features, and increasingly through AI agents connected via MCP and other emerging protocols, most of which are invisible to firewalls, secure web gateways, and standard application logs.

Traditional security tools can detect that an employee visited an AI service. They cannot identify embedded AI features inside trusted SaaS apps, unsanctioned AI agents running inside sanctioned tools, or shadow MCP servers employees connect without IT oversight.

Comprehensive AI discovery requires three capabilities:

First, automatic detection of every AI application in use, including new and emerging tools, with zero-day coverage that does not rely on manual signature updates.
Second, visibility into embedded AI activity inside SaaS platforms employees already use every day.
Third, continuous monitoring of AI agent activity, including every MCP server, tool call, and data exchange occurring across the enterprise.

Without all three, organizations have an incomplete picture of their AI attack surface.

Intent-based control in AI security is an approach that governs AI usage based on what a user is trying to accomplish, not simply which AI tool they are accessing.

Rather than blocking or allowing an entire AI application, intent-based controls evaluate the specific action being taken inside that application and enforce policy accordingly.

A user summarizing public research in ChatGPT represents a different risk profile than the same user uploading confidential customer data or activating agent mode to execute automated workflows. Intent-based control distinguishes between these scenarios in real time and applies the appropriate policy to each one.

This approach is essential for enterprises that want to enable productive AI usage without creating broad data exposure.

Blocking AI tools entirely kills productivity. Allowing them without intent-based controls creates ungoverned risk.

Purpose-built AI security platforms analyze the full context of every AI interaction, including user identity, account type, the specific intentions being invoked, and the sensitivity of the data involved, to enforce precise policy that protects the enterprise without slowing teams down.

Preventing sensitive data leakage through AI tools requires real-time visibility into every AI interaction, policy enforcement based on data sensitivity and user intent, and continuous monitoring at the conversation level across every AI tool in use.

Static upload restrictions and broad application blocks are not sufficient. Employees access AI through browsers, embedded SaaS features, and AI agents. Sensitive data leaks through prompts, responses, file uploads, multi-turn conversations, and agent outputs, often in ways traditional DLP tools cannot detect or classify accurately.

Effective AI data leakage prevention requires four capabilities. Real-time data classification that understands the content and context of every AI interaction, not just pattern matching against static rules. Intent-based policy enforcement that distinguishes between acceptable AI usage and risky behavior based on user identity, account type, and the specific intentions being invoked. Multimodal inspection that covers text, code, and data of every format moving through AI interactions. And output governance that prevents sensitive content from reaching users or flowing downstream to connected systems or other agents. Without all four, organizations are trying to manage AI data leakage with tools built for a different era.

Discovering and controlling AI agent usage requires purpose-built agentic AI security. AI agents run inside SaaS applications, automation platforms, and developer environments, connecting to enterprise systems through MCP servers and executing multi-step workflows autonomously.

Effective security for agentic AI requires visibility and control across two communication legs:

The agent-to-LLM intelligence channel, where every prompt, response, and instruction between the agent and the model must be decoded and policy-enforced in real time based on context.

And the agent-to-MCP tool execution channel, where every tool call, API invocation, and data retrieval must be inspected, verified, signed, and controlled through an MCP Gateway before the agent reaches any external system.

Agents should be treated like privileged software users, with policy-bound permissions and continuous monitoring across both communication legs.

Securing MCP requires a dedicated AI security platform that inspects, verifies, signs, and controls every tool call, API invocation, and data retrieval before an AI agent reaches any external system. Authentication, least-privilege access, and real-time enforcement at the MCP layer are the foundational requirements.

Most organizations approach MCP security too late. Shadow MCP servers are already in use across development teams and employee workflows, connecting AI agents to enterprise systems without IT oversight.

Tool poisoning, where compromised MCP tool definitions manipulate agent behavior, introduces risk before a single tool call is made. And without cross-call visibility, sensitive data moves silently across multi-step agent workflows.

Comprehensive MCP security requires four controls:

A Zero Bypass Gateway that ensures no agent call reaches enterprise systems ungoverned.

A custom MCP registry that defines which MCP servers and tools agents are permitted to access.

Role-based endpoint controls that restrict tool access based on agent identity and user permissions.

And full observability into every tool call made by every agent, in real time.

Traditional DLP was built to protect files, emails, and network traffic. It identifies sensitive data by matching patterns against static rules and blocks unauthorized transfers at the network layer. AI data protection requires a fundamentally different approach, because sensitive data in AI interactions does not move like a file.

Data in AI interactions flows through natural language prompts, multi-turn conversations, agent outputs, and automated workflows that traditional DLP cannot inspect, classify, or govern.

Three gaps make traditional DLP insufficient for AI environments. Traditional DLP cannot decode the intent behind a prompt or determine whether a response contains sensitive information. It cannot distinguish between an employee using a sanctioned enterprise AI account and a personal account where data protections do not apply. And it has no visibility into data moving across multi-step agent workflows.

AI data protection requires real-time classification of prompts and responses with deep contextual understanding, not pattern matching. It requires intent-based policy enforcement that accounts for user identity, account type, and the specific intentions being invoked. And it requires output governance that prevents sensitive, harmful, or policy-violating content from reaching users or flowing downstream to connected systems. Organizations relying on traditional DLP alone likely have significant unprotected exposure across every AI tool their employees use.

AI governance frameworks like NIST AI RMF, ISO 42001, ISO 27001, and HITRUST each address a distinct layer of AI risk management. No single framework covers everything. Together, they form a governance architecture that covers security, risk, and accountability across the full AI lifecycle.

NIST AI Risk Management Framework (AI RMF): A voluntary US framework that helps organizations identify, assess, and mitigate AI-related risks across four functions: Govern, Map, Measure, and Manage. It is the de facto standard for US-based organizations and increasingly referenced in federal procurement and enterprise due diligence requirements.

ISO 42001: The first certifiable international standard for AI Management Systems. It provides auditable governance structures, lifecycle controls, and accountability mechanisms for organizations that develop, deploy, or use AI. ISO 27001 protects information. ISO 42001 governs AI behavior, decisions, and impact. Many organizations use NIST AI RMF as a conceptual foundation and operationalize it through ISO 42001.

ISO 27001: The established standard for information security management. It does not address AI-specific risks directly, but provides the baseline security controls that AI governance programs build on.

HITRUST: Provides a certifiable framework that increasingly incorporates AI risk controls, helping organizations in regulated industries demonstrate responsible and secure AI adoption.

In 2026, the question is no longer “Are you using AI?” The question is now “Can you prove that AI use is compliant?” Purpose-built AI security controls are what make that proof possible.

The EU AI Act establishes regulations governing the development and deployment of artificial intelligence systems within the European Union.

The law introduces a risk‑based framework that requires stronger controls, transparency, and oversight for high‑risk AI systems.

Security teams must help ensure AI systems meet these compliance requirements. This requires strong acceptable use policies, operationalized through real-time visibility, controls, and audit logs.

Organizations must demonstrate that AI systems are deployed with documented policies, controls, and monitoring.

This includes maintaining an inventory of AI tools, operationalizing acceptable use policies, and logging AI interactions.

Audit trails and monitoring reports help prove that AI deployments follow governance and security standards.