What is AI Security?

^{Last updated: November 23, 2025}

AI security refers to the protection of artificial intelligence systems from threats that target their integrity, confidentiality, and availability. As AI becomes more integrated into digital infrastructure, AI cyber security has emerged as a crucial field focused on defending both AI-powered technologies and systems against misuse or attack.

AI security encompasses both securing AI models and using AI to strengthen broader cyber defenses.
From protecting large language models to detecting sophisticated phishing campaigns, AI cyber security plays a dual role in modern defense strategies.
As threats evolve, securing AI is no longer optional.
It is foundational to building resilient digital ecosystems.

Jump to a section

Why is AI security important? What are the risks when AI is not deployed in a secure manner? What are the benefits of AI security? How does Netskope define the current AI security landscape? What is an AI agent, and why does it represent a new security risk? What is the Model Context Protocol (MCP)? What are the primary security challenges organizations face when adopting AI? How are attackers specifically targeting AI environments? How does Netskope ensure visibility into shadow AI use? What role does the Netskope Cloud Confidence Index (CCI) play in AI security? How does Netskope One AI Guardrails protect against runtime threats? What is the Netskope One Agentic Broker? How does the Netskope One AI Gateway secure private AI deployments? What is Netskope One AI Red Teaming, and why is it necessary? How does Netskope protect sensitive data from being used in AI model training? Can Netskope block the delivery of copyrighted or patented information in AI responses? How does Netskope integrate AI security into a broader zero trust strategy?

Why is AI security important?

AI security has become the trust layer that allows enterprises to safely move from AI experimentation to competitive advantage. From a business operations perspective, this security capability directly drives efficiency, enabling fast and approved AI usage across teams. With AI security in place, employees work within governed systems instead of turning to unmanaged tools, keeping workflows consistent and productivity high.

In modern enterprises, data fuels every workflow, and AI acts as the engine that processes this data at scale. Integrated security controls ensure that data remains intact and protected across the AI lifecycle. Strong AI controls support reliable model outputs and reinforce data quality that gives confidence to the workteams in executing automated decision-making across the organization.

Strong AI security also allows leadership to approve innovation without compromising security or speed. Because intellectual property and sensitive data stay under clear control, AI adoption expands into high-impact sectors. Through continuous testing and real-time guardrails, organizations can scale AI adoption while maintaining visibility, control, and business alignment.

AI security has become the trust layer that allows enterprises to safely move from AI experimentation to competitive advantage.

What are the risks when AI is not deployed in a secure manner?

Poor AI security creates multiple business risks that go beyond traditional data breaches. As companies expand AI use from public tools to embedded SaaS features and private systems, the risk surface grows. Each environment introduces new issues such as weak access controls or unsafe data flows. New standards like the Model Context Protocol allow machine-to-machine (M2M) activity to move directly through systems. Without the right protections, this nonhuman traffic operates outside traditional security controls and enables unauthorized data access or transfer.

Data risk is another major concern. When employees enter company information into public AI models, sensitive assets such as source code, financial data, or strategy documents can be exposed. At the training stage, poor data controls also affect model behavior. Models trained on unsecured or mixed data can generate outputs that reveal confidential or regulated information. As a result, organizations face compliance failures under regulations such as GDPR or HIPAA, which lead to fines, audits, and long-term damage to brand trust.

Weak AI security leaves systems open to targeted attacks designed to influence AI behavior. Techniques such as prompt injection and jailbreaks override safeguards and extract sensitive information very quickly. As businesses adopt agent-based systems that act independently, these risks increase. Without strong governance, compromised agents gain access to internal systems and tools, which turns automation into a direct business threat rather than a productivity gain.

Poor AI security creates multiple business risks that go beyond traditional data breaches.

What are the benefits of AI security?

Deploying AI security turns AI from a risky experiment into a controlled and reliable driver of business growth. It does this by creating a single trust layer across the enterprise. As a result, organizations establish an “AI in the fast lane,” where all user and AI-agent activity passes through one inspection point. This approach protects data while keeping the user experience smooth, so employees stay productive and continue using approved tools.

At the same time, automated discovery and data classification give organizations full visibility into AI usage. Therefore, companies move beyond testing and begin to capture real business value from AI. Every interaction, whether from people, applications, or autonomous agents, stays authorized, monitored, and protected in real time, which supports consistent operations at scale.

Strong AI security also preserves the quality and value of corporate intelligence over time. It keeps sensitive data and intellectual property within approved boundaries and ensures proper use in internal models. In addition, automated testing identifies weaknesses early in the development process, which improves model readiness before deployment. With real-time content controls and data protection in place, businesses confidently assign critical tasks to AI while meeting regulatory and compliance expectations.

Deploying AI security turns AI from a risky experiment into a controlled and reliable driver of business growth.

How does Netskope define the current AI security landscape?

Netskope identifies three distinct frontiers in the current AI landscape: Public SaaS, Private AI, and Agentic AI. Public SaaS involves users accessing external tools like ChatGPT, while Private AI refers to internal models and applications hosted on enterprise-owned infrastructure such as AWS Bedrock or Google Vertex AI. The third frontier, Agentic AI, represents systems where a large language model (LLM) is given agency to act autonomously to achieve goals without constant human intervention. Security strategies must not be limited to chatbots, but the entire ecosystem of automated workflows and integrated features.

Netskope identifies three distinct frontiers in the current AI landscape: Public SaaS, Private AI, and Agentic AI.

What is an AI agent, and why does it represent a new security risk?

An AI agent is a system that uses an LLM to complete a goal by taking multiple steps on its own, instead of responding to single requests. It breaks a goal into tasks, decides what to do next, and takes action without waiting for human input. To do this, the agent often connects to tools such as business applications, databases, APIs, or cloud services.

An AI agent can read data, analyze it, trigger processes, and even make decisions that affect real systems. This makes agents useful for automation, operations, and decision support, but it also gives them far more access and influence than traditional software.

AI agents operate differently from humans, and this changes how security must work. Traditional security controls are designed around people logging in, clicking, and interacting with systems in predictable ways. AI agents act continuously in the background and communicate directly with systems, tools, and data sources. As a result, their activity can bypass many human-focused security checks.

Because agents are autonomous, any permission they receive can be misused at scale. If an agent is misconfigured, compromised, or manipulated, it can access data, move information, or trigger actions much faster than a human attacker. As more business processes rely on agent-driven automation, security failures increasingly come from agent abuse rather than stolen user credentials.

An AI agent is a system that uses an LLM to complete a goal by taking multiple steps on its own, instead of responding to single requests.

What is the Model Context Protocol (MCP)?

The model context protocol (MCP) is an industry standard that allows AI agents to connect directly to external systems, such as databases, SaaS tools, and internal services. Instead of building and maintaining custom APIs for every integration, MCP acts like a universal connector. This makes it easy and fast for AI agents to access tools and data across different environments.

MCP removes friction from AI adoption because teams can plug agents into workflows quickly, reuse integrations, and scale automation without heavy engineering effort. However, the same simplicity that accelerates adoption also introduces exposure. MCP enables nonhuman, M2M communication to move freely across systems. Traditional security tools are designed to protect human users, not autonomous agents. As a result, MCP traffic can operate outside legacy security visibility and controls.

Without monitoring and guardrails, MCP connections can allow agents to move data independently, which can lead to exposed credentials, compromised tools, or contaminated integrations. MCP makes it easier for agents to pull or transmit sensitive data, increasing the likelihood of unauthorized data extraction if access boundaries are not enforced.

The model context protocol (MCP) is an industry standard that allows AI agents to connect directly to external systems, such as databases, SaaS tools, and internal services.

What are the primary security challenges organizations face when adopting AI?

Expanding risk surface
AI risk extends beyond basic chat tools into SaaS applications, internal platforms, and private AI models. Each step adds exposure, including weak access settings and unsafe data flows used for model training. In addition, AI agents now access systems directly using new connection methods such as MCP. This machine-driven activity creates visibility gaps and allows errors, credential misuse, or compromised tools to operate without human review.
Sensitive data exposure and data movement
Data loss is the fastest and most common AI risk. Employees share source code or internal data with AI tools during daily work. Research shows that source code exposure accounts for a large portion of AI policy violations. Risk also appears during model training, where internal data can surface later in AI responses. Limited transparency in many AI platforms increases uncertainty around how company data is stored, processed, or reused.
Governance and compliance challenges
As AI usage scales, security, ethics, and compliance become closely linked. AI systems influence decisions, which increases the impact of bias, errors, and misuse. At the same time, employee and customer data must follow strict privacy laws such as GDPR, HIPAA, and emerging AI regulations. Without automated controls and clear accountability, organizations face legal risk, regulatory penalties, and loss of trust as decision-making shifts from people to autonomous systems.

AI risk extends beyond basic chat tools into SaaS applications, internal platforms, and private AI models.

How are attackers specifically targeting AI environments?

The AI attack landscape now focuses on how models understand and respond to language rather than on traditional software flaws. Attackers manipulate AI behavior through techniques such as prompt injection, where crafted inputs override system instructions, and jailbreaking, where repeated interactions bypass safety controls. Another growing risk is indirect prompt injection, where malicious instructions are hidden inside documents or web content that an AI processes. In these cases, the AI changes behavior without the user’s awareness, which makes detection difficult and increases operational risk.

New threats also emerge as AI becomes more connected and autonomous. Attackers can extract sensitive information from poorly curated training data, or corrupt models through data poisoning. The use of agent-based systems and protocols like MCP introduces additional exposure, as autonomous systems interact directly with tools and data sources. Weak access controls allow attackers to redirect agents toward malicious tools or unsafe actions. Multi-step attacks further increase risk by gradually steering AI behavior over time. Because these attacks operate at the interaction layer, traditional security tools have limited visibility. Effective protection requires real-time controls that monitor intent and behavior during every AI interaction to prevent misuse before it impacts business operations.

The AI attack landscape now focuses on how models understand and respond to language rather than on traditional software flaws.

How does Netskope ensure visibility into shadow AI use?

Netskope delivers visibility of shadow AI by routing web, API, and agent traffic through a single inspection point. This allows security teams to see every AI interaction, including activity from unsanctioned apps. Coverage extends beyond standard web usage to API traffic and the MCP, which governs how AI agents connect to data and tools. By reading these protocols, the platform shows not only which AI services are in use but also how data moves between users, AI agents, and external systems.

To support decision-making, Netskope brings this visibility into a centralized AI Dashboard. The dashboard highlights usage patterns, frequently used AI applications, and risky actions such as sensitive data uploads or policy violations. Advanced Instance Awareness adds another layer of control by distinguishing between personal AI accounts and approved corporate instances. This precision helps organizations enable approved AI use while steering users away from unmanaged tools, allowing teams to support innovation without increasing risk.

Netskope delivers visibility of shadow AI by routing web, API, and agent traffic through a single inspection point.

What role does the Netskope Cloud Confidence Index (CCI) play in AI security?

The Netskope Cloud Confidence Index (CCI) provides continuous risk intelligence across more than 85,000 cloud and SaaS applications. As AI features become embedded into everyday business tools, the index identifies where AI is actively in use, including capabilities such as smart replies and AI copilots. Security officers need this level of insight because standard SaaS applications handle enterprise data in new ways, including retaining data or using it for AI-driven processing, without explicit notice to the organization.

Beyond identification, the index evaluates how each application manages enterprise data. It assesses whether data is used for AI training or shared with external vendors and aligns these practices with compliance requirements such as GDPR, SOC 2, and ISO 27001. Security teams can apply automated policies based on real risk. The same intelligence extends to infrastructure elements, including public MCP servers, helping organizations assess protocol versions and authentication strength before allowing integration into their environments.

The Netskope Cloud Confidence Index (CCI) provides continuous risk intelligence across more than 85,000 cloud and SaaS applications.

How does Netskope One AI Guardrails protect against runtime threats?

Netskope One AI Guardrails delivers a dedicated runtime security layer built to detect and stop AI‑specific threats that traditional security tools fail to address. Instead of scanning for malicious code, it analyzes intent in real time by inspecting every AI request and response. This approach blocks advanced linguistic attacks such as prompt injection and jailbreaks that attempt to override model rules. At the same time, it prevents AI systems from generating or exposing patented or copyrighted content, which reduces intellectual property risk.

In addition to threat prevention, the platform enforces responsible AI use across the enterprise. It automatically filters harmful, abusive, and inappropriate content for both employee interactions and autonomous agent activity. This includes detection of hate speech, violent content, and criminal use cases. All findings are mapped to security frameworks such as MITRE ATLAS and the OWASP Top 10 for LLMs, giving security teams clear, actionable context to assess risk, investigate incidents, and enforce policy at scale.

Netskope One AI Guardrails delivers a dedicated runtime security layer built to detect and stop AI‑specific threats that traditional security tools fail to address.

What is the Netskope One Agentic Broker?

The Netskope One Agentic Broker is built to secure autonomous AI environments as organizations adopt agent-driven workflows. As AI agents interact directly with data sources and tools, traditional security controls centered on human activity lose visibility. The Netskope One Agentic Broker closes this gap by decoding and securing traffic that uses MCP, which governs how AI agents connect to external systems. This allows organizations to maintain control as automation increases and agents operate independently across the environment.

The Netskope One Agentic Broker delivers visibility into nonhuman traffic by decoding MCP sessions and identifying the servers, clients, tools, prompts, and resources involved in each interaction. It assesses risk using the Netskope CCI to evaluate public and private MCP servers based on protocol versions, authentication methods, and risky attributes before connections occur. It prevents tool poisoning by ensuring agents do not interact with malicious or compromised tools, and it enforces data protection through integration with Netskope One DLP to stop unauthorized data movement. The Netskope One Agentic Broker also logs detailed MCP activity, including initializations and tool responses, to support governance and investigation. Deployed standalone or within the Netskope One NG-SWG, it enables secure agentic automation without introducing a new attack surface.

The Netskope One Agentic Broker is built to secure autonomous AI environments as organizations adopt agent-driven workflows.

How does the Netskope One AI Gateway secure private AI deployments?

The Netskope One AI Gateway provides a dedicated inspection point for high-volume internal traffic that flows between private applications and large language models. As organizations build custom AI‑powered applications, risk shifts from user prompts to automated application‑to‑LLM API calls. These interactions occur inside private data centers or virtual private clouds and therefore remain outside traditional cloud security controls. The Netskope One AI Gateway addresses this gap by operating within private environments and securing internal AI traffic that never crosses a public perimeter.

The gateway deploys as a virtual appliance inside environments such as AWS VPCs or VMware ESXi to inspect local traffic. It centralizes API governance by controlling authentication and traffic flow so only approved applications and agents connect to LLMs. Through integration with SkopeAI, it performs deep content inspection on API payloads, applying data loss prevention, threat protection, and AI guardrails to stop sensitive data exposure and prompt‑based attacks. It also enforces rate limiting to protect system stability and service reliability. Every API interaction is logged in a searchable audit trail, supporting compliance, governance, and usage monitoring. This approach allows organizations to scale private AI initiatives while keeping data flows governed, authenticated, and protected.

The Netskope One AI Gateway provides a dedicated inspection point for high-volume internal traffic that flows between private applications and large language models.

What is Netskope One AI Red Teaming, and why is it necessary?

Netskope One AI Red Teaming provides a proactive way to test private AI models and agents before they enter production. Traditional security testing focuses on infrastructure, while Netskope One AI Red Teaming focuses on how large language models behave under stress. It targets weaknesses such as prompt manipulation, data extraction, and unsafe responses. By replacing slow and manual testing with automated adversarial simulations, it supports teams that are rapidly building and updating internal AI tools.

The solution addresses the reality that organizations fully own the security of their private models, even when hidden risks exist. It runs large-scale automated tests using a library of adversarial scenarios to identify weaknesses early. It simulates complex multi-step attacks that can bypass model safeguards, integrates directly into continuous integration/continuous delivery (CI/CD) pipelines to catch risks before each release, and performs continuous testing to track how risk changes over time. By identifying issues before deployment, Netskope One AI Red Teaming helps ensure private models operate safely, meet compliance requirements, and remain resilient when connected to live business data.

Netskope One AI Red Teaming provides a proactive way to test private AI models and agents before they enter production.

How does Netskope protect sensitive data from being used in AI model training?

Netskope protects sensitive data by addressing risk early in the AI lifecycle, before data is ingested into models. AI systems depend on large datasets for training, which increases the risk of exposing intellectual property or regulated information without proper controls. Using data security posture management (DSPM), Netskope continuously monitors cloud environments to identify and classify sensitive data such as financial records, personal data, and trade secrets across structured and unstructured sources. This gives organizations clear visibility into where critical data resides and how it is handled.

With this visibility, organizations apply proactive controls to restrict AI access to approved datasets only. By discovering and labeling data at rest, Netskope prevents sensitive information from being absorbed into AI models and later exposed through responses. Continuous monitoring identifies sensitive data in real time, access visibility shows how data is used and shared, and unified posture management connects AI security posture management with data security posture management. These combined controls keep training data secure, compliant, and limited to the required context, preserving control over enterprise intellectual property.

Netskope protects sensitive data by addressing risk early in the AI lifecycle, before data is ingested into models.

Can Netskope block the delivery of copyrighted or patented information in AI responses?

Netskope addresses legal and intellectual property risk tied to generative AI by applying a dedicated runtime defense layer. Through Netskope One AI Guardrails, the platform detects and blocks AI responses that contain patented or copyrighted material. This protection reduces legal exposure by preventing unauthorized IP from appearing in generated outputs. The capability is critical because many AI platforms lack transparency around data handling and content generation, which increases the risk of unintentionally including third‑party intellectual property.

This runtime protection supports responsible AI use and brand safety across the organization. By integrating data loss prevention and threat protection through SkopeAI, the platform evaluates the intent and content of every interaction in real time. Security teams can block AI‑generated material that contains protected or proprietary information, maintain compliance with internal policies and regulatory requirements, and confirm that AI outputs do not expose sensitive enterprise data. This level of control allows organizations to adopt genAI models while preserving ownership of their intellectual property and respecting external IP rights.

Netskope addresses legal and intellectual property risk tied to generative AI by applying a dedicated runtime defense layer.

How does Netskope integrate AI security into a broader zero trust strategy?

Netskope integrates AI security into a zero trust strategy by treating the AI ecosystem as an extension of the enterprise network rather than a separate silo. Using the Netskope One platform, organizations apply the same context-aware security policies to AI as they do for web, SaaS, and private applications. This approach ensures that every interaction, whether from a human user, an internal application, or an autonomous agent, is continuously verified and authorized based on real‑time risk.

This integration is delivered through a multi‑layered architecture that provides a unified inspection point for all traffic flows:

The access layer: Infrastructure including the Next Generation Secure Web Gateway (NG-SWG), Netskope One AI Gateway, and Netskope One Agentic Broker provides visibility and controlled access for users, internal applications, and nonhuman agents.

Unified policy enforcement: All AI traffic is enforced through the same data loss prevention (DLP) and threat protection engines used across the enterprise.

Contextual protection: The Netskope Zero Trust Engine evaluates intent behind each interaction to block AI‑specific threats such as prompt injection and jailbreaking while allowing secure access.

Data‑centric posture: Integration with data security posture management ensures the data used by AI models is discovered, classified, and protected under existing governance standards.

By acting as the unified inspection point, Netskope One enables secure AI adoption and keeps AI integrated into an enterprise‑wide data security strategy, allowing organizations to move from experimentation to scaled innovation without increasing risk.

Netskope integrates AI security into a zero trust strategy by treating the AI ecosystem as an extension of the enterprise network rather than a separate silo.