AI Security:

The Next Critical Layer of SASE

AI Security:

The Next Critical Layer of SASE

AI Security: The Next Critical Layer of SASE | 2

Enterprise AI Transformation Artificial Intelligence is rapidly becoming embedded across modern enterprise environments.

Employees now interact daily with AI-powered SaaS applications, browser copilots, developer

assistants, autonomous agents, embedded generative AI services, and AI-enhanced business

workflows. AI capabilities are increasingly integrated into collaboration platforms, productivity suites,

customer engagement systems, browsers, and operational applications — often becoming part of

normal business processes without introducing separate user experiences.

AI now lives inside everyday work Employees touch AI continuously — usually without leaving the tools they already use.

SaaS copilots

Assistants built into

productivity & CRM suites

Browser copilots

AI search & extensions

running in the browser

Developer assistants

Code generation inside

IDEs and workflows

Autonomous agents Embedded generative AI AI-enhanced workflows

Acting on data and

triggering actions via

APIs

GenAI baked into the apps

people use all day

Automation that moves

data across business

processes

This rapid adoption is fundamentally reshaping how organizations operate, accelerating productivity

and automation across every business function. At the same time, it is introducing a new and highly

dynamic security challenge. Sensitive enterprise information now flows through prompts, uploads,

browser sessions, APIs, automated workflows, and AI-driven interactions that frequently operate

outside the visibility boundaries of traditional security architectures.

Many organizations already face growing exposure associated with unsanctioned AI usage, shadow AI

applications, AI-powered browser extensions, autonomous workflow automation, and external

generative AI platforms capable of processing sensitive corporate data. As AI adoption accelerates,

security teams must extend governance and protection consistently across an increasingly

distributed and rapidly evolving AI ecosystem.

This shift requires more than isolated AI security controls. Organizations need a unified architecture

capable of securing AI interactions wherever they occur — across users, devices, browsers, SaaS

applications, cloud environments, and enterprise workflows.

Enterprise AI Transformation

AI Security: The Next Critical Layer of SASE | 2

Artificial Intelligence is rapidly becoming embedded across modern enterprise environments.

Employees now interact daily with AI-powered SaaS applications, browser copilots, developer

assistants, autonomous agents, embedded generative AI services, and AI-enhanced business

workflows. AI capabilities are increasingly integrated into collaboration platforms, productivity suites, customer engagement systems, browsers, and operational applications — often becoming part of

normal business processes without introducing separate user experiences.

This rapid adoption is fundamentally reshaping how organizations operate, accelerating productivity

and automation across every business function. At the same time, it is introducing a new and highly

dynamic security challenge. Sensitive enterprise information now flows through prompts, uploads, browser sessions, APIs, automated workflows, and AI-driven interactions that frequently operate

outside the visibility boundaries of traditional security architectures.

Many organizations already face growing exposure associated with unsanctioned AI usage, shadow AI

applications, AI-powered browser extensions, autonomous workflow automation, and external

generative AI platforms capable of processing sensitive corporate data. As AI adoption accelerates,

security teams must extend governance and protection consistently across an increasingly

distributed and rapidly evolving AI ecosystem.

This shift requires more than isolated AI security controls. Organizations need a unified architecture

capable of securing AI interactions wherever they occur — across users, devices, browsers, SaaS

applications, cloud environments, and enterprise workflows.

Employees touch AI continuously — usually without leaving the tools they already use.

AI now lives inside everyday work

SaaS copilots Assistants built into

productivity & CRM suites

Browser copilots AI search & extensions

running in the browser

Developer assistants

Code generation inside IDEs and workflows

Autonomous agents

Acting on data and triggering actions via APIs

Embedded generative AI

GenAI baked into the apps people use all day

AI-enhanced workflows

Automation that moves data across business

processes

AI Security: The Next Critical Layer of SASE | 3

AI Introduces a New Enterprise Risk Model Enterprise data increasingly moves through AI-enabled environments in ways that are difficult to

monitor using traditional security models. Employees may unknowingly expose source code,

customer records, financial information, strategic plans, internal communications, or regulated data

to external AI services during normal business activity. AI-powered browser extensions and

embedded assistants further expand the attack surface by introducing additional third-party access

points into enterprise workflows.

Sensitive data now crosses the visibility boundary

Inside the enterprise Beyond enterprise control

Source code

Customer records

prompts, uploads,

API calls

External generative AI tools

Shadow AI applications

Financial information AI browser extensions

Strategic plans Autonomous AI agents

Regulated & personal data Third-party access points

The rapid proliferation of AI applications and AI agents has created a new operational and security

challenge: Shadow AI. Organizations need visibility into how AI tools are being used in real time, what

data is being shared, and which AI services are being accessed across the enterprise.

The emergence of autonomous AI agents introduces an additional layer of operational and security

complexity. Modern AI systems are increasingly capable of interacting directly with enterprise

applications, retrieving data, executing workflows, generating actions, and orchestrating business

processes through APIs and automation platforms. These environments require organizations to

govern both user-driven and AI-driven activity using consistent security policies and centralized

visibility.

The convergence of AI-driven productivity, automation, and autonomous operational capabilities is

fundamentally reshaping enterprise security requirements.

AI Introduces a New Enterprise Risk Model

Enterprise data increasingly moves through AI-enabled environments in ways that are difficult to

monitor using traditional security models. Employees may unknowingly expose source code,

customer records, financial information, strategic plans, internal communications, or regulated data to external AI services during normal business activity. AI-powered browser extensions and

embedded assistants further expand the attack surface by introducing additional third-party access

points into enterprise workflows.

Sensitive data now crosses the visibility boundary

The rapid proliferation of AI applications and AI agents has created a new operational and security

challenge: Shadow AI. Organizations need visibility into how AI tools are being used in real time, what

data is being shared, and which AI services are being accessed across the enterprise.

The emergence of autonomous AI agents introduces an additional layer of operational and security

complexity. Modern AI systems are increasingly capable of interacting directly with enterprise

applications, retrieving data, executing workflows, generating actions, and orchestrating business processes through APIs and automation platforms. These environments require organizations to

govern both user-driven and AI-driven activity using consistent security policies and centralized

visibility.

The convergence of AI-driven productivity, automation, and autonomous operational capabilities is

fundamentally reshaping enterprise security requirements.

AI Security: The Next Critical Layer of SASE | 3

Beyond enterprise controlInside the enterprise

Source code

Customer records

Financial information

Strategic plans

Regulated & personal data

External generative AI tools

Shadow AI applications

AI browser extensions

Autonomous AI agents

Third-party access points

prompts, uploads, API calls

AI Security: The Next Critical Layer of SASE | 4

Security Architecture Requirements for the AI Era Traditional security architecture was not designed to govern highly distributed AI environments that

span browsers, SaaS applications, cloud services, APIs, remote users, embedded assistants, and

autonomous systems simultaneously. Many organizations currently rely on fragmented security tools

that provide only partial visibility into AI-related activity, creating inconsistent policy enforcement and

operational complexity.

From fragmented tools to one unified architecture

Today: fragmented point tools Unified SASE: converged management

Check Point SASE SWG CASB

SWG CASB

DLP ZTNA

ZTNA DLP

Browser tool

Browser security Threat prevention

Gaps & blind spots

for AI activity Users · Devices · Browsers · SaaS ·

Cloud · APIs · AI workflows

One pLATFORM COVERING

Securing AI-enabled enterprise environments requires an architecture capable of extending visibility,

governance, threat prevention, identity-aware access control, and data protection consistently across

the entire digital experience.

These requirements align naturally with the architectural foundations of Secure Access Service Edge

(SASE). SASE already operates at the convergence point of users, devices, browsers, SaaS

applications, cloud services, enterprise data, identity, and internet connectivity. This position enables

SASE to function as a centralized enforcement architecture for AI-driven environments without

requiring organizations to deploy disconnected point products across multiple operational domains.

A unified SASE solution enables organizations to apply consistent governance policies across AI

applications, browser-based interactions, embedded AI services, autonomous workflows, and cloud-

delivered AI platforms while maintaining centralized operational control.

As AI adoption accelerates, extending AI security directly into the SASE architecture becomes

increasingly important for maintaining visibility, governance, compliance, and operational simplicity

across distributed enterprise environments.

Security Architecture Requirements for the AI Era

Traditional security architecture was not designed to govern highly distributed AI environments that span browsers, SaaS applications, cloud services, APIs, remote users, embedded assistants, and

autonomous systems simultaneously. Many organizations currently rely on fragmented security tools

that provide only partial visibility into AI-related activity, creating inconsistent policy enforcement and operational complexity.

Today: fragmented point tools Unified SASE: converged management

SWG CASB

DLP ZTNA

Browser tool

Gaps & blind spots

for AI activity

Check Point SASE

SWG CASB

ZTNA DLP

Browser security Threat prevention

One pLATFORM COVERING

Users · Devices · Browsers · SaaS ·

Cloud · APIs · AI workflows

Securing AI-enabled enterprise environments requires an architecture capable of extending visibility,

governance, threat prevention, identity-aware access control, and data protection consistently across

the entire digital experience.

These requirements align naturally with the architectural foundations of Secure Access Service Edge (SASE). SASE already operates at the convergence point of users, devices, browsers, SaaS

applications, cloud services, enterprise data, identity, and internet connectivity. This position enables SASE to function as a centralized enforcement architecture for AI-driven environments without

requiring organizations to deploy disconnected point products across multiple operational domains.

A unified SASE solution enables organizations to apply consistent governance policies across AI

applications, browser-based interactions, embedded AI services, autonomous workflows, and cloud-

delivered AI platforms while maintaining centralized operational control.

As AI adoption accelerates, extending AI security directly into the SASE architecture becomes

increasingly important for maintaining visibility, governance, compliance, and operational simplicity across distributed enterprise environments.

AI Security: The Next Critical Layer of SASE | 4

From fragmented tools to one unified architecture

AI Security: The Next Critical Layer of SASE | 5

Extending SASE Security to AI Environments Check Point SASE extends security across AI-enabled environments through integrated capabilities

that include Secure Web Gateway (SWG), CASB, Zero Trust Network Access (ZTNA), Data Loss

Prevention (DLP), browser security, threat prevention, and AI-powered threat intelligence. Together,

these capabilities provide organizations with centralized visibility and governance across AI-related

activity throughout the enterprise.

AI application visibility plays a critical role in this model. Security teams require the ability to identify

which AI services employees access, understand usage patterns, monitor interactions with

enterprise data, and evaluate exposure associated with sanctioned and unsanctioned AI applications.

Centralized reduces operational blind spots associated with shadow AI usage.

Data protection also becomes increasingly important as employees interact with external AI

platforms during normal business operations. Sensitive information such as source code, financial

records, customer information, intellectual property, regulated data, and strategic business content

frequently moves through prompts, uploads, and AI-driven workflows. Extending DLP enforcement

into AI environments helps organizations reduce the risk of unauthorized data exposure while

supporting secure AI adoption across the business.

Browser-based AI interactions introduce additional governance requirements. Many AI services

operate directly within enterprise browsers through embedded assistants, AI-enhanced search

platforms, browser extensions, and generative AI interfaces. Security teams therefore require

consistent visibility and policy enforcement across browser-driven AI activity as part of the broader

enterprise security architecture.

Shadow AI Usage

Employees

Sensitive
 Posts

Data Leakage

Tool/ Privilege Abuse

Agents

Unsafe Autonomy

Loop/ DoW Risks

Prompt Injections

Applications

Harmful Outputs

Data Exfiltration

Discover

Protect

Govern

AI Defense Plane

One platform. From employees to applications to agents.

Extending SASE Security to AI Environments

Check Point SASE extends security across AI-enabled environments through integrated capabilities that include Secure Web Gateway (SWG), CASB, Zero Trust Network Access (ZTNA), Data Loss

Prevention (DLP), browser security, threat prevention, and AI-powered threat intelligence. Together,

these capabilities provide organizations with centralized visibility and governance across AI-related

activity throughout the enterprise.

AI application visibility plays a critical role in this model. Security teams require the ability to identify which AI services employees access, understand usage patterns, monitor interactions with

enterprise data, and evaluate exposure associated with sanctioned and unsanctioned AI applications.

Centralized reduces operational blind spots associated with shadow AI usage.

Data protection also becomes increasingly important as employees interact with external AI

platforms during normal business operations. Sensitive information such as source code, financial records, customer information, intellectual property, regulated data, and strategic business content frequently moves through prompts, uploads, and AI-driven workflows. Extending DLP enforcement

into AI environments helps organizations reduce the risk of unauthorized data exposure while

supporting secure AI adoption across the business.

Browser-based AI interactions introduce additional governance requirements. Many AI services

operate directly within enterprise browsers through embedded assistants, AI-enhanced search

platforms, browser extensions, and generative AI interfaces. Security teams therefore require consistent visibility and policy enforcement across browser-driven AI activity as part of the broader

enterprise security architecture.

AI Security: The Next Critical Layer of SASE | 5

Discover

Protect

Govern

AI Defense Plane

One platform. From employees to applications to agents.

Shadow AI

Usage

Sensitive Posts

Data

Leakage

Employees Tool/ Privilege Abuse

Unsafe

Autonomy Loop/ DoW Risks

Agents

Prompt Injections

Harmful

Outputs Data

Exfiltration

Applications

AI Security: The Next Critical Layer of SASE | 6

Threat prevention capabilities also play a critical role in protecting AI-enabled environments. Threat

actors increasingly leverage AI to accelerate phishing attacks, automate malicious content

generation, improve credential theft techniques, and enhance social engineering campaigns. AI-

powered threat intelligence and advanced threat prevention technologies help organizations identify

and block evolving AI-assisted threats before they impact enterprise environments.

Architectural Framework for AI Security Within SASE The following architectural model illustrates how AI security capabilities integrate naturally into the

SASE architecture.

Users & Devices

Check Point SASE

SWG CASB ZTNA

Browser Security

AI Governance & Visibility

DLP

Threat Prevention

AI

AI SaaS
 Apps

Autonomous

Workflows

AI
 Browsers

Copilots

AI Agents & Chatbots

API-driven
 AI

This architecture enables organizations to extend centralized governance, visibility, and protection

consistently across AI-driven interactions while maintaining operational simplicity through a unified

cloud-delivered SASE platform.

Architectural Framework for AI Security Within SASE The following architectural model illustrates how AI security capabilities integrate naturally into the SASE architecture.

Users & Devices

Check Point SASE

SWG CASB ZTNA DLP

Browser Security

AI Governance &

Visibility Threat

Prevention

AI

AI SaaS

Apps AI

Browsers AI Agents & Chatbots

Autonomous Workflows Copilots

API-driven AI

This architecture enables organizations to extend centralized governance, visibility, and protection

consistently across AI-driven interactions while maintaining operational simplicity through a unified cloud-delivered SASE platform.

AI Security: The Next Critical Layer of SASE | 6

Threat prevention capabilities also play a critical role in protecting AI-enabled environments. Threat actors increasingly leverage AI to accelerate phishing attacks, automate malicious content

generation, improve credential theft techniques, and enhance social engineering campaigns. AI-

powered threat intelligence and advanced threat prevention technologies help organizations identify

and block evolving AI-assisted threats before they impact enterprise environments.

AI Security: The Next Critical Layer of SASE | 7

Securing the Future of Enterprise AI

AI capabilities continue to expand across enterprise infrastructure, business applications,

operational workflows, and cloud-delivered services. Organizations therefore require security

architectures capable of supporting large-scale AI adoption while maintaining visibility, governance,

compliance, data protection, and operational control.

SASE provides the architectural foundation required to secure modern AI-enabled enterprise

environments through the convergence of networking, security, identity, browser protection, SaaS

governance, Zero Trust enforcement, data protection, and AI-powered threat prevention within a

unified cloud-delivered architecture.

By extending AI security directly into the SASE framework, organizations can apply consistent

security controls across users, applications, browsers, SaaS platforms, cloud environments, APIs,

and AI-driven workflows without increasing operational fragmentation.

As enterprise AI adoption accelerates, integrating AI security into the SASE architecture will become

an increasingly important component of modern cybersecurity strategy.

Employee AI Interactions Covered by Check Point Workforce AI Check Point Workforce AI extends consistent governance and protection across the full range of AI

tools employees use every day — from public chat assistants to code assistants, desktop agents, and

autonomous workflows.

Employee AI interactions Covered by Check Point Workforce AI

Check Point Workforce AI Security Essentials Edition

Public AI tools e.g. ChatGPT, Gemini, Claude, Copilot

Browser extensions

Desktop agents

SaaS-integrated AI features

Code assistants e.g. GitHub Copilot, Cursor and MCP-connected workflows.

Check Point Workforce AI Security Enterprise Edition

All the features included in the Essentials Edition plus:

AI Security for Desktop AI Applications

AI Security for Code Assistance (IDEs)

AI Security for Agents

Inventory Endpoint Scanner for Agents including risk assessment

www.checkpoint.com  © 2026 Check Point Software Technologies Ltd. All rights reserved.

Employee AI Interactions Covered by Check Point Workforce AI

AI Security: The Next Critical Layer of SASE | 7

Check Point Workforce AI extends consistent governance and protection across the full range of AI

tools employees use every day — from public chat assistants to code assistants, desktop agents, and autonomous workflows.

Check Point Workforce AI Security Essentials Edition

Public AI tools e.g. ChatGPT, Gemini, Claude, Copilot

Browser extensions

Desktop agents

SaaS-integrated AI features

Code assistants e.g. GitHub Copilot, Cursor and MCP-connected workflows.

Check Point Workforce AI Security Enterprise Edition

All the features included in the Essentials

Edition plus:

AI Security for Desktop AI Applications

AI Security for Code Assistance (IDEs)

AI Security for Agents

Inventory Endpoint Scanner for Agents including risk assessment

Employee AI interactions Covered by Check Point Workforce AI

SASE provides the architectural foundation required to secure modern AI-enabled enterprise

environments through the convergence of networking, security, identity, browser protection, SaaS

governance, Zero Trust enforcement, data protection, and AI-powered threat prevention within a unified cloud-delivered architecture.

By extending AI security directly into the SASE framework, organizations can apply consistent

security controls across users, applications, browsers, SaaS platforms, cloud environments, APIs,

and AI-driven workflows without increasing operational fragmentation.

As enterprise AI adoption accelerates, integrating AI security into the SASE architecture will become

an increasingly important component of modern cybersecurity strategy.

Securing the Future of Enterprise AI

AI capabilities continue to expand across enterprise infrastructure, business applications, operational workflows, and cloud-delivered services. Organizations therefore require security architectures capable of supporting large-scale AI adoption while maintaining visibility, governance,

compliance, data protection, and operational control.

www.checkpoint.com © 2026 Check Point Software Technologies Ltd. All rights reserved.

https://www.checkpoint.com https://www.checkpoint.com