What Is AI Security?
Risks, Threats, and Enterprise Defense Framework

In short, ai security is the practice of protecting artificial intelligence ai systems, models, and data from threats that are unique to machine learning and generative ai. Currently, as firms race to adopt large language models, AI agents, and automated decision systems, new ai security risks are emerging that traditional security tools cannot catch. Specifically, prompt injections, data poisoning, shadow ai, and model theft are just a few of the threats that security teams must now manage ai risk around. Specifically, in this guide you will learn what ai security means, the key ai security risks facing enterprises, how to build a defense framework, and how ai security connects to broader cybersecurity. Regardless of whether you are deploying a customer-facing chatbot or running internal ai deployments, this article gives your security teams the context they need to protect sensitive data and keep generative ai safe.

What AI Security Means

AI security is the set of practices, tools, and policies that protect AI systems from being attacked, misused, or exploited. Unlike traditional cybersecurity — which focuses on networks, endpoints, and applications — ai security targets the unique attack surfaces that come with machine learning models, training data, and generative ai outputs.

Essentially, the field of ai security covers two sides, each with its own ai security risks. First, protecting the AI itself: making sure the model, the training data, the prompts, and the outputs are safe from tampering. Second, defending against AI-powered attacks: stopping threat actors who use generative ai to craft better phishing, automate scans, or build polymorphic malware. Both sides matter because ai security risks grow on both fronts at once.

Notably, Gartner, NIST, and OWASP have all published frameworks that address ai security risks. For instance, OWASP released its LLM Top 10 and a separate Top 10 for Agentic Applications, confirming that ai specific threats now warrant their own category. Therefore, for security teams, ai security is no longer a future concern — it is a current operational requirement that sits alongside endpoint security, cloud security, and data loss prevention.

The AI Security Risk Landscape

The ai security risks facing enterprises fall into several categories. Importantly, each ai security risk targets a different part of the AI lifecycle — from ai development and training data preparation through deployment and ongoing use. Consequently, understanding these ai security risks helps security teams prioritize defenses.

Prompt Injections

Prompt injections happen when an attacker inserts malicious instructions into the input that a large language model processes. The model follows the injected instructions instead of the user’s original intent. As a result, this can cause the system to leak sensitive data, bypass access controls, or take unauthorized actions. Notably, prompt injections are the top-ranked ai security risk in the OWASP LLM Top 10. Therefore, for security teams, defending against prompt injections requires input validation, output filtering, and guardrails that limit what the model can do.

Data Poisoning

Data poisoning attacks corrupt the training data that a model learns from. Specifically, by injecting bad data into the training pipeline, attackers can embed backdoors, create biases, or degrade model accuracy. Because ai development teams often use data from open sources, the supply chain for training data is hard to secure. Consequently, detecting data poisoning requires data security checks at every stage of the pipeline — from collection through labeling to model training. Similarly, security teams must treat training data with the same care they give to sensitive data in other systems.

Shadow AI

Shadow ai is the use of unsanctioned AI tools by employees without the knowledge or approval of security teams. Notably, a HiddenLayer report found that 76% of firms now cite shadow ai as a definite or probable problem — up from 61% the prior year. Shadow ai creates ai security risks because the tools bypass enterprise data security policies, potentially exposing sensitive data to third-party models. Obviously, security teams cannot protect what they do not know exists. Therefore, managing shadow ai requires clear policies, approved tool lists, and monitoring for unauthorized ai deployments.

Model Theft and Extraction

Model theft occurs when attackers steal the weights, architecture, or behavior of a proprietary AI model. For instance, through repeated queries an attacker can build a copy of the model (model extraction) without accessing the source code. This is one of the ai security risks that is ai specific because the stolen model reveals trade secrets and can be used to find weaknesses. Consequently, for firms that invest heavily in ai development, model theft is a direct threat to competitive advantage and data security.

Adversarial Attacks

Adversarial attacks use carefully crafted inputs to fool AI models into making wrong decisions. For example, a tiny change to an image, document, or audio file — invisible to humans — can make the model misclassify, ignore threats, or approve fraud. Fundamentally, these attacks target the core logic of the model, making adversarial attacks one of the hardest ai security risks for security teams to detect. Defending against adversarial attacks requires adversarial testing during ai development and continuous monitoring in production.

AI-Powered Attacks: How Threat Actors Use Generative AI

AI security is not only about protecting AI — it is also about defending against attackers who use generative ai as a weapon. Currently, threat actors now use large language models and generative ai tools to scale attacks, craft better lures, and automate tasks that used to require human effort.

AI-enhanced phishing. Generative ai produces phishing emails with perfect grammar, personalized details, and convincing tone. Notably, about 40% of BEC emails in recent analysis were flagged as AI-generated. Security teams face a harder job because these messages bypass traditional spam filters and look like real business communication.

Deepfake fraud. Meanwhile, attackers use generative ai to clone voices and create video deepfakes of executives. For instance, in one case criminals used a deepfake CEO voice call to authorize a wire transfer. About 85% of firms reported at least one deepfake-related security incident in the past year. Ultimately, this ai security risk targets the trust that security teams place in identity verification.

Automated vulnerability scanning. AI tools help attackers scan for weaknesses faster than manual methods. IBM’s X-Force report found a 44% increase in attacks that exploited public-facing applications, driven in part by AI-enabled vulnerability discovery. Attackers use generative ai to write exploit code and test it against target systems at scale.

AI-generated malware. Threat actors use generative ai to create polymorphic malware that changes its code signature on every run. This makes it harder for traditional security tools to detect. Automated response systems and behavioral detection are the main defenses, but security teams must update their models constantly to keep pace with ai security risks from this angle.

Building an AI Security Framework

Protecting AI systems requires a structured approach. A good ai security framework covers the full lifecycle — from ai development through deployment to ongoing operations. Below is a practical framework that security teams can adapt to their own ai deployments.

Step 1: Inventory your AI assets. List every AI model, dataset, API, and tool in use — including shadow ai. You cannot manage ai risk on systems you do not know about. This inventory should cover both sanctioned ai deployments and any generative ai tools employees use on their own.

Step 2: Classify data by sensitivity. Map which ai deployments handle sensitive data. Training data that contains personal information, financial records, or trade secrets needs stronger data security controls. Apply the same classification rules you use for other data stores to every AI pipeline and generative ai tool.

Step 3: Apply access controls. Limit who can interact with each AI model and what data they can feed into it. Zero Trust principles apply to AI just as they do to networks. Every request — from a human user, an API call, or an AI agent — should be authenticated and authorized. Strong access controls prevent unauthorized use and reduce the risk of prompt injections.

Testing, Monitoring, and Governance

Step 4: Red team your AI. Run adversarial tests against your models before and after deployment. A red team simulates the attacks that threat actors would use — prompt injections, data poisoning, model extraction, and jailbreaking. Red team testing reveals weaknesses that standard QA misses and gives security teams a clear list of fixes before the model goes live.

Step 5: Monitor in production. Deploy monitoring that tracks model inputs, outputs, and behavior in real time. Look for anomalies: unusual query patterns, unexpected outputs, spikes in sensitive data exposure, or signs of prompt injections. Connect AI monitoring to your SIEM so security teams can see AI events alongside other security alerts.

Step 6: Govern AI use with policy. Write clear policies that define which generative ai tools are approved, what sensitive data can and cannot be entered into AI prompts, and how ai development teams must handle training data. Communicate these policies to every team — not just security teams. AI governance is a company-wide effort that sits at the intersection of data security, compliance, and ai security risks.

Securing Generative AI and Large Language Models

Generative ai and large language models introduce ai specific risks that traditional security tools were not built to handle. Securing these systems requires controls at every layer — input, processing, and output.

Input controls. Validate and sanitize every prompt before it reaches the model. Block known prompt injections patterns. Limit the length and format of inputs. For customer-facing generative ai tools, use a content filter that flags or blocks prompts containing sensitive data, harmful instructions, or attempts to extract training data. Security teams should treat the prompt interface as an attack surface, just like a web form.

Output controls. Filter model outputs for sensitive data leakage, harmful content, and hallucinated facts. If a generative ai model generates a response that includes personal data from its training data, the output filter should catch it before it reaches the user. Pair output filtering with logging so security teams can audit what the model says and trace any data security incidents back to the source.

Model hardening. Use techniques like differential privacy, federated learning, and output perturbation to make models more resistant to extraction and adversarial attacks. For large language models that handle sensitive data, consider running inference in a secure enclave where the model and data never leave a protected environment. Security teams should work with ai development teams to embed these protections during the build, not bolt them on after deployment.

Supply chain security. Many generative ai pipelines depend on pre-trained models, open-source libraries, and third-party datasets. HiddenLayer’s report found that malware hidden in public model repositories was the most cited source of AI-related data breaches (35%). Security teams must vet every component in the AI supply chain — just as they vet software dependencies in traditional ai development.

The Rise of Agentic AI Risks

Agentic AI — autonomous systems that can plan, decide, and act without human input — is the fastest-growing category of ai security risks. Unlike a chatbot that only generates text, an AI agent can browse the web, execute code, access files, and trigger real-world workflows. If compromised, the damage extends far beyond a bad response.

OWASP published a separate Top 10 for Agentic Applications in late 2025, confirming that these ai security risks need their own framework. HiddenLayer found that 1 in 8 AI-related data breaches are now linked to agentic systems. Palo Alto Networks warns that autonomous agents outnumber humans 82:1 in some enterprise environments, making them the most valuable target for attackers.

Specific Agentic AI Threats

Key agentic ai security risks include:

• Excessive permissions: Agents granted broad access controls can be tricked into deleting data, exfiltrating sensitive data, or modifying critical systems.
• Prompt injection via external data: An agent that browses the web or reads emails can encounter hidden prompt injections in documents it processes.
• Tool misuse: Agents connect to APIs and security tools through plugins. A compromised plugin turns the agent into an insider threat with automated response capability.
• Chain-of-thought manipulation: Attackers can influence an agent’s reasoning process, causing it to take harmful actions that appear logical.

For security teams, agentic AI demands a new set of controls: just-in-time permissions, human-in-the-loop approval for high-impact actions, zero-trust architecture for every agent interaction, and continuous monitoring of agent behavior across all ai deployments.

How AI Security Connects to the Broader Security Stack

AI security does not exist in a vacuum. It connects to every layer of the enterprise security stack. Security teams that treat ai security as a standalone project miss the links that make their defenses stronger.

AI + SIEM. Feed AI monitoring logs into your SIEM platform so security teams can correlate AI events with network, endpoint, and cloud alerts. A spike in prompt injections or unusual generative ai usage may signal a broader attack that spans multiple systems.

AI + EDR/XDR. Endpoint detection and response and XDR platforms protect the devices and networks that host ai deployments. If an AI agent runs on an endpoint, EDR monitors its behavior. If the agent’s actions trigger a data security alert, XDR correlates it across the full stack.

AI + Threat Intelligence. Threat intelligence feeds now include indicators specific to AI attacks — malicious prompt patterns, poisoned datasets, and known adversarial inputs. Security teams that integrate AI-focused threat intel into their workflows can detect ai security risks earlier.

AI + Data Loss Prevention. Data loss prevention tools must cover AI channels. If an employee pastes sensitive data into a generative ai prompt, DLP should block or flag it. This is especially important for managing shadow ai and preventing data breaches through unsanctioned ai deployments.

Managed AI Security Services

For firms that need managed support, cybersecurity services providers now offer ai specific monitoring, red team testing, and policy development as part of their managed detection offerings. This gives security teams access to ai security expertise without building a full in-house AI risk function. For a complete view of the threat landscape, see our pillar guide on cybersecurity.

Enterprise AI Security Best Practices

Beyond the framework steps above, these best practices help security teams reduce ai security risks across all ai deployments and generative ai tools.

Treat AI prompts as sensitive data inputs. Anything an employee types into a generative ai tool becomes input to a third-party model. If that input contains sensitive data — customer records, financial figures, trade secrets — it may be stored, logged, or used for training. Security teams should enforce policies that ban sharing sensitive data with unapproved generative ai tools. Data loss prevention controls should flag or block sensitive data in prompts, just as they do in email.

Separate AI environments from production systems. Run ai deployments in isolated environments with their own access controls. If a generative ai model is compromised, the blast radius stays contained. This is the same segmentation principle that security teams apply to networks and cloud workloads, extended to AI.

Log everything. Every prompt, every response, every model action should be logged. Logs are how security teams detect ai security risks after the fact, investigate data breaches, and prove compliance. Feed AI logs into your SIEM so they sit alongside other security events for full visibility.

Train every employee. AI security awareness training should cover ai security risks specific to generative ai: what not to paste into a chatbot, how to spot AI-generated phishing, and why shadow ai is dangerous. Security teams that invest in training reduce the human-error vector — which remains the biggest source of ai security incidents.

Regulatory Awareness and AI Governance

Stay current on regulations. The EU AI Act, NIST AI RMF, and OWASP LLM Top 10 are evolving fast. Security teams must track these frameworks and map their ai deployments against the latest requirements. Non-compliance is its own ai security risk — fines, lawsuits, and reputational harm all follow from gaps in AI governance.

Managing AI Risk Across the Enterprise

AI security is not just a job for the SOC. To manage ai risk at scale, firms need cross-functional ownership that spans security teams, ai development, legal, compliance, and business units.

Assign clear ownership. Who is responsible for ai security risks in your firm? If the answer is “no one” or “everyone,” you have a governance gap. Assign a named owner — whether that is the CISO, a Chief AI Risk Officer, or a dedicated ai security lead. This person coordinates between security teams and ai development to manage ai risk across all ai deployments.

Build an AI risk register. List every ai deployment, the sensitive data it touches, the ai security risks it faces, and the controls in place. Review the register quarterly. Add new ai deployments as they go live. Flag gaps where controls are missing or where generative ai use has outpaced policy. This register becomes the single source of truth for ai security across the firm.

Run tabletop exercises. Simulate an AI-related data breach — a prompt injection that leaks sensitive data, or a shadow ai tool that exposes customer records. Walk through the incident response: who gets notified, what systems are isolated, how is the damage assessed? Tabletop exercises reveal gaps in your ai security plan before a real incident does. Security teams that practice these scenarios respond faster when the real thing hits.

Report to the board. AI security risks are board-level concerns. Data breaches, regulatory fines, and reputational damage from AI incidents can cost millions. Security teams should present ai security risk metrics — number of ai deployments, shadow ai instances found, red team findings, and incident trends — to leadership on a regular cadence. Board-level visibility drives budget and prioritization for ai security programs.

Protecting Sensitive Data in AI Pipelines

Sensitive data flows through every stage of the AI lifecycle. From the training data that shapes the model to the prompts that users type to the outputs the model produces — sensitive data is always at risk. Security teams must protect sensitive data at each stage to prevent ai security risks from turning into real data breaches.

Training data protection. Models learn from their training data. If that training data contains sensitive data — customer records, medical files, financial transactions — the model may memorize and later leak it. Security teams should audit all training data before it enters the pipeline. Strip or mask sensitive data that is not needed for the model’s task. Use techniques like differential privacy to limit what the model can reveal about individual records in its training data.

Prompt-level protection. Every prompt a user sends to a generative ai tool is a potential sensitive data leak. An employee who pastes a customer list, a contract, or internal financials into a chatbot has just shared sensitive data with a third-party model. Security teams must deploy data loss prevention controls on every generative ai interface. Specifically, these controls scan prompts for sensitive data patterns and block or flag violations before the data leaves the firm.

Output-level protection. Generative ai models can produce outputs that include sensitive data from their training data or from the prompt context. An output filter scans every response for sensitive data — names, account numbers, health records — and redacts or blocks it before it reaches the user. Without output filtering, ai security risks include accidental exposure of sensitive data to users who should not see it.

Data Classification for AI Systems

Importantly, not all sensitive data carries the same risk. Security teams should classify data into tiers — public, internal, confidential, restricted — and apply ai specific rules for each tier. Public data can flow freely into generative ai prompts. Confidential and restricted sensitive data should never enter an unapproved generative ai tool. This classification drives the rules in your data loss prevention policies, access controls, and ai deployment approvals.

Overall, the goal is simple: know where your sensitive data is, control who can feed it into AI, and monitor what comes out. Security teams that build these controls early prevent the ai security risks that lead to data breaches, regulatory fines, and loss of customer trust. Every generative ai deployment that touches sensitive data needs these protections from day one.

AI Security Statistics and Market Context

The numbers confirm that ai security risks are growing faster than most firms can adapt. Here are the key data points that security teams should know.

IBM’s X-Force report found a 44% increase in attacks that began with exploiting public-facing applications, many now accelerated by AI-enabled scanning. Over 300,000 AI platform credentials — including ChatGPT accounts — were exposed through infostealer malware, making generative ai platforms a new target for credential theft.

HiddenLayer’s survey found that 76% of firms cite shadow ai as a definite or probable problem. However, only 34% partner externally for AI threat detection. And 73% report internal conflict over who owns ai security controls. These gaps show that awareness of ai security risks is growing, but governance is not keeping pace.

Check Point’s report found that risky prompts nearly doubled, with 90% of firms encountering risky generative ai prompts. When the firm analyzed 10,000 Model Context Protocol servers, it found ai security weaknesses in 40% of them. Meanwhile, Palo Alto Networks warns that only 6% of firms have an advanced ai security strategy — a gap that may lead to the first major AI-related lawsuits.

In summary, ai security risks are not theoretical. They are causing real data breaches, exposing sensitive data, and creating ai security risks and liability for security teams that have not yet adapted. Every generative ai deployment without ai specific controls is an open risk.

AI Security Readiness Checklist

Use this checklist to assess your firm’s readiness against the most common ai security risks. Security teams can score each area and prioritize gaps.

• AI asset inventory: Do you know every AI model, generative ai tool, and ai deployment in your firm — including shadow ai? An inventory is the foundation of managing ai security risks.
• Sensitive data classification: Is every dataset that feeds into AI classified by sensitivity? Security teams must know which ai deployments touch sensitive data so they can apply the right controls.
• Prompt and output controls: Do your generative ai tools have input validation and output filtering? These controls are the frontline defense against prompt injections and sensitive data leaks — two of the top ai security risks.
• Access controls and Zero Trust: Are access controls applied to every AI model, API, and agent? Security teams should enforce least-privilege access for all ai deployments to reduce the blast radius of ai security risks.

Testing, Monitoring, and Policy Checklist

• Red team and adversarial testing: Have you red-teamed your AI models? Testing for prompt injections, data poisoning, and model extraction exposes ai security risks before attackers find them. Security teams should run these tests before launch and on a regular cycle.
• Monitoring and SIEM integration: Are AI logs feeding into your SIEM? Security teams need real-time visibility into generative ai usage, prompt patterns, and model behavior to catch ai security risks early.
• Policy and governance: Do you have written policies that define approved generative ai tools, ban sensitive data in prompts, and assign ownership of ai security risks? Without policy, security teams are reacting to problems instead of preventing them.
• Incident response for AI: Does your incident response plan cover AI-specific scenarios — prompt injection breaches, training data poisoning, shadow ai exposure? Security teams that drill these scenarios respond faster when ai security risks become real incidents.

Finally, score each item as green (in place), yellow (partial), or red (missing). Focus your next quarter’s budget on the red items. The firms that manage ai security risks best are those that treat this checklist as a living document — reviewed quarterly, updated as new generative ai tools and ai deployments enter the environment.

Conclusion

AI security protects the models, data, and systems that power enterprise AI. The ai security risks are real, growing, and diverse. Specifically, prompt injections, data poisoning, shadow ai, adversarial attacks, and agentic AI threats all target the unique attack surfaces that generative ai and large language models create.

The ai security risks demand action now. The path forward is clear. Inventory every AI asset — including shadow ai and unsanctioned ai deployments. Classify and protect sensitive data at every stage. Apply access controls, red team your models, and monitor them in production. Govern generative ai use with clear policy.

Also, connect ai security to your existing SIEM, EDR, threat intelligence, and data loss prevention stack so security teams see the full picture. AI security risks will keep evolving as AI does. The firms that manage ai risk today will be the ones that deploy AI safely and at scale. Inventory your AI assets, classify your sensitive data, apply access controls, red team your models, monitor with your SIEM, and govern generative ai use with clear policy. These steps, applied together, turn ai security from a gap into a strength for your security teams. Ai security risks will evolve as generative ai and large language models advance, but the firms with a solid framework, trained security teams, and proper controls on sensitive data will adapt faster than those starting from scratch.

What to Do Next

The firms that take ai security risks seriously today will be the ones that deploy AI safely and at scale. Every generative ai deployment without ai specific controls is an open risk. Every shadow ai tool that security teams do not know about is a blind spot. And every ai security risk that goes unmanaged is a potential data breach waiting to happen. Start with the checklist above, build your framework step by step, and treat ai security risks as a standing agenda item — not a one-time project.

Common Questions About AI Security

References

• IBM — 2026 X-Force Threat Intelligence Index
• HiddenLayer — 2026 AI Threat Landscape Report
• Check Point — AI Security for Enterprises