What do you use for AI security in a large enterprise?

For a large enterprise, I would use a defense-in-depth AI security architecture, rather than relying on a single AI security product. The goal is to secure the entire AI lifecycle: employees using AI, enterprise data accessed by AI, LLMs, RAG applications, AI agents, APIs, and autonomous actions. Here is the stack I would consider for an enterprise with thousands of employees and multiple AI platforms. # 1. Recommended enterprise AI security stack 1. AI governance and discovery Foundation Discover, inventory, approve and govern AI usage. - Microsoft Purview - Credo AI - IBM watsonx.governance - Holistic AI 2. AI security posture management (AI-SPM) Identify AI-specific risks, misconfigurations and vulnerabilities. - Microsoft Defender - Palo Alto Networks Prisma AIRS - Wiz - Protect AI 3. AI runtime security and guardrails Protect prompts, model interactions, responses and agent activity. - Lakera - HiddenLayer - Cisco AI Defense - Palo Alto Prisma AIRS - AWS Bedrock Guardrails 4. Data security and DLP Prevent confidential information from leaking through AI. - Microsoft Purview - Netskope - Zscaler - BigID - Cyberhaven 5. Identity and access management Control which users, agents and applications can access resources. - Microsoft Entra ID - CyberArk - Okta - SailPoint 6. AI application and API security Secure AI applications, integrations, APIs and model endpoints. - Cloudflare - F5 - Akamai - Salt Security 7. Monitoring, detection and response Detect AI-related attacks and investigate incidents. - Microsoft Sentinel - Splunk - CrowdStrike - Google Security Operations These are candidates to evaluate, not products that all need to be purchased. Your existing security ecosystem should determine the final selection. # 2. The architecture I would build I would organize the enterprise AI security architecture like this: Enterprise users and applications Employees · Developers · Business applications · AI agents AI security gateway Authentication · Prompt inspection · DLP · Threat detection · Policy enforcement AI models OpenAI · Azure OpenAI · Claude · Gemini · Llama Enterprise data SharePoint · Databases · Data lakes · Vector stores Runtime security and agent controls Least privilege · Tool permissions · Sandboxing · Human approvals · Output validation Enterprise SOC and governance SIEM · SOAR · Audit logs · Compliance · Incident response An important principle: never rely on the LLM itself to enforce security permissions. Enforce permissions outside the model, in the application, identity and execution layers. # 3. The most important AI threats to protect against | Threat | Required protection | | --- | --- | | Prompt injection | AI runtime security | | Sensitive data leakage | DLP and data classification | | Unauthorized AI usage | AI discovery and access controls | | Model theft or tampering | Model supply-chain security | | RAG data exposure | Retrieval authorization | | Malicious AI agents | Agent identity and least privilege | | Excessive agent permissions | Runtime policy enforcement | | AI-generated malicious code | Application security and code scanning | | Model/API abuse | Rate limiting and monitoring | | Shadow AI | AI discovery and governance | I would pay particular attention to prompt injection, sensitive information disclosure and excessive agent permissions. These are prominent risks in the 2026 OWASP GenAI guidance. [OWASP GenAI LLM Top 10 2026](https://genai.owasp.org/resource/owasp-genai-llm-top-10-2026/?trk=article-ssr-frontend-pulse_little-text-block)[OWASP updates top 10 security risks for LLM applications | analysis](https://www.scworld.com/analysis/owasp-updates-top-10-security-risks-for-llm-applications) # 4. My recommended starting combination If I were designing a Microsoft-centric enterprise, I would initially evaluate: | Security area | My starting choice | | --- | --- | | AI governance | Microsoft Purview | | Identity | Microsoft Entra ID | | Data protection | Purview DLP | | AI security posture | Defender + Prisma AIRS evaluation | | Runtime protection | Cisco AI Defense or Lakera | | Privileged access | CyberArk | | SOC | Microsoft Sentinel | | AI risk framework | NIST AI RMF + OWASP | For an AWS-centric environment, I would evaluate Amazon Bedrock Guardrails, IAM, CloudTrail, CloudWatch and the AWS enterprise AI security architecture. [AI Defense Capabilities for Enterprise AI Security](https://learn.microsoft.com/en-us/security/zero-trust/catalog-ai-defense-capabilities/catalog-ai-defense-capabilities)[Layer 3: Security and governance for generative AI platforms on AWS](https://docs.aws.amazon.com/prescriptive-guidance/latest/strategy-enterprise-ready-gen-ai-platform/security.html)[Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile](https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-generative-artificial-intelligence) # 5. Three questions that determine the right architecture There is a big difference between securing employee access to ChatGPT and securing hundreds of autonomous AI agents that can access corporate databases and execute transactions. I'd like to narrow this down to the architecture you actually need. I can then put together a detailed enterprise reference architecture with recommended vendors, security controls, integration points and a phased implementation roadmap.

**Large enterprises typically use a layered AI security strategy that combines specialized platforms (CrowdStrike, Check Point, Palo Alto Networks, IBM, NVIDIA) with governance frameworks to protect both AI workloads and employee use of generative AI.** ## 🔑 Key Considerations for Enterprise AI Security * **Scope of protection**: Do you need to secure AI models, agentic workflows, or employee use of GenAI tools? * **Performance vs. security trade-off**: Can your solution inspect encrypted AI traffic without slowing operations? * **Governance & compliance**: Does the platform include AI risk management, trust, and regulatory compliance? * **Integration**: How well does it connect with existing SOC, SIEM, and observability tools? ## 📊 Comparison of Leading AI Security Solutions | Vendor / Platform | Focus Area | Strengths | Risks / Trade-offs | |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------|-----------------------------------------------------------------------------------------|----------------------------------------------------------------------------| | **CrowdStrike Falcon \+ Dynatrace** [CrowdStrike (CRWD) Stock: Extends Losses as Falcon Platform Expands Into AI Security](https://blockonomi.com/crowdstrike-crwd-stock-extends-losses-as-falcon-platform-expands-into-ai-security/) | Endpoint \+ observability | Integrates endpoint telemetry with app/infrastructure data; reduces duplication in SIEM | Primarily endpoint-centric; may require broader coverage for AI agents | | **Check Point Maestro Hyperscale Orchestrator** [Check Point Secures AI-Scale Traffic for Neoclouds, Sovereign AI and the Enterprise with New Maestro Hyperscale Orchestrator](https://aithority.com/machine-learning/check-point-secures-ai-scale-traffic-for-neoclouds-sovereign-ai-and-the-enterprise-with-new-maestro-hyperscale-orchestrator/) | Network & AI traffic | Secures encrypted AI-scale traffic at hyperscale; integrates AI firewall | Hardware scaling complexity; requires orchestration expertise | | **Palo Alto Networks Agentic AI Security** [Agentic AI Security Solutions: Top 7 Platforms Compared - Palo Alto Networks](https://www.paloaltonetworks.com/cyberpedia/agentic-ai-security-solutions) | Autonomous AI agents | Protects against prompt injection, memory poisoning, privilege escalation | Still evolving; enterprises must adapt workflows to agentic security | | **Akamai AI Security Suite** [Top 9 AI Security Solutions for 2026 \| Akamai](https://www.akamai.com/resources/white-paper/best-ai-security-tools) | Browser & edge | Detects shadow AI, enforces granular data policies in browser | Focused on user-facing GenAI risks; less coverage for backend AI workloads | | **IBM Security \+ watsonx.governance** [Artificial Intelligence (AI) Cybersecurity Solutions \| IBM](https://www.ibm.com/solutions/ai-cybersecurity) | Governance & SOC | AI-powered threat detection, automated triage, compliance frameworks | Governance-heavy; may need complementary runtime security | | **NVIDIA OpenShell \+ Sentry/BlueField** [AI Cybersecurity Solutions for Securing AI Workloads \| NVIDIA](https://www.nvidia.com/en-us/solutions/ai/cybersecurity/) | AI infrastructure & runtime | In-silicon security, confidential computing, runtime safety controls | Hardware-dependent; best for enterprises with NVIDIA-based infrastructure | ## 🚨 Risks & Challenges * **Shadow AI**: Employees using unsanctioned GenAI tools can leak sensitive data. Solutions like Akamai and IBM focus on discovery and governance. * **Agentic threats**: Autonomous AI agents introduce new attack surfaces (prompt injection, privilege escalation). Palo Alto and NVIDIA target these risks. * **Encrypted traffic blind spots**: Traditional firewalls miss AI traffic context. Check Point’s hyperscale orchestrator addresses this but requires investment. * **Integration overhead**: Legacy SIEMs and SOCs may not handle AI telemetry efficiently. CrowdStrike and IBM emphasize reducing duplication and automating triage. ## ✅ Recommended Approach For a **large enterprise**, a layered strategy works best: 1. **Network-level AI traffic inspection** (Check Point Maestro). 2. **Endpoint \+ observability integration** (CrowdStrike Falcon \+ Dynatrace). 3. **Agentic AI security for autonomous workflows** (Palo Alto Networks, NVIDIA). 4. **Governance and compliance frameworks** (IBM watsonx.governance). 5. **Browser-based controls for employee GenAI use** (Akamai). This combination balances **performance, governance, and runtime protection**, ensuring resilience against both external attacks and internal misuse. Would you like me to **map these solutions to a practical enterprise architecture diagram** (e.g., where each layer fits: network, endpoint, agent, governance)? That could make the strategy more actionable.

Securing Artificial Intelligence in a large enterprise requires a **defense-in-depth architecture** that spans governance, data protection, application guardrails, and real-time operational monitoring. Unlike traditional software, AI systems introduce non-deterministic, probabilistic behavior, persistent context windows, and autonomous agent capabilities that require specialized security controls. [The Five-Layer Architecture Behind Successful Enterprise AI](https://www.emergentsoftware.net/resources/insights/ai-architecture-a-five-layer-framework-for-enterprise-ai/#:~:text=Planning%2C%20tool%20selection%2C%20state%2C%20evaluation%2C%20agent%20coordination.,These%20architecture%20types%20are%20not%20mutually%20exclusive.)[AI Security Best Practices: Building a Foundation for Responsible](https://www.obsidiansecurity.com/blog/ai-security-best-practices#:~:text=AI%20systems%20process%20sensitive%20data%20dynamically%2C%20make,monitor%2C%20govern%2C%20and%20constrain%20without%20breaking%20functionality.) Enterprise AI security is structured across **five foundational layers**: ## 1\. Governance & Compliance Frameworks Before deploying tools, enterprises align their AI initiatives with established industry standards to ensure accountability, auditability, and regulatory compliance. [AI Security Best Practices: The Complete Guide | Cyberhaven](https://www.cyberhaven.com/blog/ai-security-best-practices#:~:text=For%20organizations%20running%20AI%20workloads%20in%20public,organizational%20risk%20before%20deployment%20rather%20than%20after.) * **NIST AI Risk Management Framework (AI RMF):** Provides a structured framework organized around four functions (_Govern, Map, Measure, Manage_) to assess and mitigate lifecycle risks. [AI Security Best Practices: The Complete Guide | Cyberhaven](https://www.cyberhaven.com/blog/ai-security-best-practices#:~:text=NIST%20AI%20Risk%20Management%20Framework.%20The%20NIST,organizations%20at%20different%20stages%20of%20AI%20maturity.) * **ISO/IEC 23894 & ISO 42001:** Standards for embedding AI risk management and AI Management Systems (AIMS) into enterprise governance. [AI Security Best Practices: The Complete Guide | Cyberhaven](https://www.cyberhaven.com/blog/ai-security-best-practices#:~:text=ISO%2FIEC%2023894.%20ISO%2FIEC%2023894%20addresses%20AI%20system,afterthought%20during%20deployment.%20CSA%20AI%20Security%20Guidelines.) * **OWASP Top 10 for LLMs:** Defines tactical threat vectors such as prompt injection, data poisoning, sensitive information disclosure, and supply chain vulnerabilities. [AI Security Best Practices: Building a Foundation for Responsible](https://www.obsidiansecurity.com/blog/ai-security-best-practices#:~:text=A%202024%20OWASP%20study%20found%20that%2067%25,\(PII\).%20*%20Identity%20Spoofing%20and%20Token%20Compromise.) ## 2\. AI Security Posture Management (AISPM) & Data Security To prevent shadow AI deployments and protect sensitive corporate data from leaking into training sets or inference logs: [AI Security Best Practices: Building a Foundation for Responsible](https://www.obsidiansecurity.com/blog/ai-security-best-practices#:~:text=*%20AI%20security%20best%20practices%20encompass%20the,landscape%20differs%20fundamentally%20from%20traditional%20software%20environments.) * **Shadow AI Discovery:** Tools inspect network traffic, browser extensions, and cloud API gateways to detect unapproved SaaS AI tools and unauthorized LLM endpoint usage. [AI Security Best Practices: Building a Foundation for Responsible](https://www.obsidiansecurity.com/blog/ai-security-best-practices#:~:text=In%20early%202024%2C%20a%20Fortune%20500%20financial,first%20line%20of%20defense%20for%20AI%20security.) * **AI Data Loss Prevention (DLP):** Redacts personally identifiable information (PII), intellectual property (IP), and trade secrets before prompts reach external or third-party AI models. [AI Security Best Practices: Building a Foundation for Responsible](https://www.obsidiansecurity.com/blog/ai-security-best-practices#:~:text=AI%20agents%20often%20operate%20with%20service%20accounts,abuse%2C%20query%20based%20extraction%2C%20and%20insider%20threats.) * **Data Lineage & Training Security:** Secures Retrieval-Augmented Generation (RAG) pipelines and vector databases using fine-grained, role-based access control (RBAC) to ensure AI models only retrieve information a user is authorized to see. [The Five-Layer Architecture Behind Successful Enterprise AI](https://www.emergentsoftware.net/resources/insights/ai-architecture-a-five-layer-framework-for-enterprise-ai/#:~:text=These%20architecture%20types%20are%20not%20mutually%20exclusive.,data%20access%2C%20identity%2C%20governance%2C%20monitoring%2C%20and%20security.) ## 3\. Runtime Protection & AI Gateways Runtime defense sits between users, orchestration layers, and base models to inspect prompts and outputs in real time. [Secure AI in the Enterprise: A Practical Framework | CSA](https://cloudsecurityalliance.org/blog/2026/03/03/how-to-secure-ai-in-the-enterprise-a-practical-framework-for-models-data-and-agents#:~:text=Non%2Dcompliant%20data%20transmission%20across%20jurisdictions.%20Securing%20AI,AI%20risks%20are%20introduced%20long%20before%20deployment.) * **AI Firewalls & Reverse Proxies:** Route all enterprise model traffic through centralized gateways (e.g., Portkey, LiteLLM Enterprise, custom API gateways) to enforce rate limits, cost caps, and logging. [Secure AI in the Enterprise: A Practical Framework | CSA](https://cloudsecurityalliance.org/blog/2026/03/03/how-to-secure-ai-in-the-enterprise-a-practical-framework-for-models-data-and-agents#:~:text=Operational%20AI%20risks%20include%3A%20Agents%20using%20permissions,ability%20to%20capture%20agent%20state%20for%20investigation.) * **Input/Output Guardrails:** Systems inspect incoming prompts for direct or indirect prompt injection attacks, jailbreaks, and harmful intent, while inspecting outgoing responses for hallucinatory compliance leaks or policy violations. [Secure AI in the Enterprise: A Practical Framework | CSA](https://cloudsecurityalliance.org/blog/2026/03/03/how-to-secure-ai-in-the-enterprise-a-practical-framework-for-models-data-and-agents#:~:text=Key%20risks%20include%3A%20Malicious%20prompt%20injection%20designed,layer%20requires%20monitoring%20intent%2C%20not%20just%20access.) * **Zero-Trust Model Access:** Enforces short-lived API keys, managed identities, and continuous token validation across internal microservices and external AI services. [AI Security Best Practices: Building a Foundation for Responsible](https://www.obsidiansecurity.com/blog/ai-security-best-practices#:~:text=The%20breach%20originated%20from%20a%20shadow%20SaaS,Multi%20Factor%20Authentication%20\(MFA\)%20for%20AI%20Platforms.) ## 4\. Agentic AI & Identity Governance As enterprises transition from simple chat applications to autonomous AI agents that act on behalf of users, identity becomes the new security perimeter. [The Five-Layer Architecture Behind Successful Enterprise AI](https://www.emergentsoftware.net/resources/insights/ai-architecture-a-five-layer-framework-for-enterprise-ai/#:~:text=Agentic%20AI%20architecture%20adds%20the%20ability%20for,additional%20orchestration%2C%20identity%2C%20permissions%2C%20monitoring%2C%20and%20governance.) * **Least-Privilege Agent Permissions:** Agents are granted tightly scoped service accounts rather than broad admin tokens. * **Human-in-the-Loop (HITL) Triggers:** High-risk actions (e.g., executing financial transactions, modifying production databases, or altering access permissions) require explicit human sign-off. [AI Governance Best Practices: Frameworks & Principles - Databricks](https://www.databricks.com/blog/ai-governance-best-practices-how-build-responsible-and-effective-ai-programs#:~:text=For%20high%2Drisk%20or%20sensitive%20use%20cases%2C%20humans,on%20summaries%2C%20model%20cards%2C%20or%20decision%20rationales.) * **Session & Context Boundary Controls:** Isolates persistent memory stores so an agent's context window cannot be exploited across execution boundaries or shared with unauthorized entities. [AI Security Best Practices: The Complete Guide | Cyberhaven](https://www.cyberhaven.com/blog/ai-security-best-practices#:~:text=*%20Session%2Dbased%20vs.%20persistent%20context.%20Generative%20AI,known%20set%20of%20applications%20and%20network%20destinations.) ## 5\. Continuous Observability & Red Teaming Security teams must continuously stress-test models and monitor running environments for novel attack patterns. [Secure AI in the Enterprise: A Practical Framework | CSA](https://cloudsecurityalliance.org/blog/2026/03/03/how-to-secure-ai-in-the-enterprise-a-practical-framework-for-models-data-and-agents#:~:text=Non%2Dcompliant%20data%20transmission%20across%20jurisdictions.%20Securing%20AI,AI%20risks%20are%20introduced%20long%20before%20deployment.) * **Automated AI Red Teaming:** Automated vulnerability scanners evaluate models for adversarial vulnerability, prompt injection resistance, and training data extraction risks. [AI Security Best Practices: Building a Foundation for Responsible](https://www.obsidiansecurity.com/blog/ai-security-best-practices#:~:text=A%202024%20OWASP%20study%20found%20that%2067%25,\(PII\).%20*%20Identity%20Spoofing%20and%20Token%20Compromise.) * **AI Observability & SIEM Integration:** AI interaction logs (prompts, completions, tool invocations, and token metadata) are fed into enterprise SIEM systems (e.g., Splunk, Microsoft Sentinel) for telemetry analysis and incident response.

Securing artificial intelligence in a large enterprise requires a defense-in-depth framework that covers data, models, agentic workflows, and infrastructure. Because large language models (LLMs) and autonomous agents process unstructured data and trigger downstream actions, security stacks go far beyond traditional application firewalls. [Enterprise AI Security Guide 2026: The C-Suite Playbook](https://uvik.net/blog/enterprise-ai-security-guide/) Core Pillars of Enterprise AI Security * **AI Gateways and Proxies**: Centralized entry points that route all model traffic. They enforce uniform API governance, manage rate limits and quotas, scrub Personally Identifiable Information (PII), and apply Data Loss Prevention (DLP) policies before requests reach external or internal model endpoints. [Enterprise AI Security Guide 2026: The C-Suite Playbook](https://uvik.net/blog/enterprise-ai-security-guide/) * **Asset and Shadow AI Inventory**: Comprehensive registries mapping every deployed model, agent, dataset, vector store, and API vendor. Security teams assign business and technical owners to mitigate unmanaged "shadow AI" usage across departments. [Enterprise AI Security Guide 2026: The C-Suite Playbook](https://uvik.net/blog/enterprise-ai-security-guide/) * **Deterministic Guardrails & Sandboxing**: Systems that treat models as untrusted processors rather than policy engines. Authorization decisions, tool execution, and parameter validations happen via strict programmatic schemas outside the model. Code generated by AI is executed exclusively in isolated, deny-by-default sandboxes. [Enterprise AI Security Guide 2026: The C-Suite Playbook](https://uvik.net/blog/enterprise-ai-security-guide/) * **Permission-Aware Retrieval (RAG)**: Integration layers ensuring that Retrieval-Augmented Generation architectures respect document-, row-, and tenant-level access controls. Users should never retrieve context via AI that they lack the clearance to see in the native source repository. [Enterprise AI Security Guide 2026: The C-Suite Playbook](https://uvik.net/blog/enterprise-ai-security-guide/) * **Identity and Least-Privilege Access for Agents**: Treating autonomous AI agents as distinct digital workloads. Rather than sharing human sessions or master API keys, agents receive unique machine identities, short-lived task-scoped credentials, and tightly restricted permissions. High-impact actions like financial transactions or data deletion require explicit human-in-the-loop approval. [Enterprise AI Security Guide 2026: The C-Suite Playbook](https://uvik.net/blog/enterprise-ai-security-guide/) Key Frameworks & Community Standards * **OWASP GenAI Security Project**: A primary reference for mapping vulnerabilities. Large enterprises use the OWASP Top 10 for LLM Applications to benchmark risks like prompt injection, insecure output handling, and training data poisoning. * **NIST AI Risk Management Framework (RMF)**: Widely adopted for governing, mapping, measuring, and managing AI risks related to validity, reliability, and security. [OWASP Top 10 for Large Language Model Applications | OWASP Foundation](https://owasp.org/projects/top-10-for-large-language-model-applications) If you'd like to narrow this down, let me know: * Are you looking to secure **internal chatbot deployments**, **customer-facing generative features**, or **autonomous AI agents**? * Are you interested in specific **commercial gateway tools** or **open-source guardrail frameworks**?

For a large enterprise, I’d use a **layered** security stack - not one “AI firewall.” Assuming you mean securing employee AI use, internal AI applications, and autonomous agents, I’d cover three areas: who can use AI, what data it can access, and what actions it can take. Enterprise AI security needs identity controls, data protection, application security, runtime monitoring, and testing together.([gtt.net][1])([orca.security][2]) I’m making an architecture recommendation here, not describing Perplexity’s internal security stack. ## What I’d put in the stack | Layer | What I’d use it for | Products I’d evaluate | |---|---|---| | Employee AI controls | Discover unapproved AI tools; inspect prompts and uploads; block sensitive-data leakage while allowing approved uses. | Netskope One AI Security, Zscaler AI Security, or a specialist such as Prompt Security. ([akamai.com][3])([venn.com][4]) | | Data security and permissions | Identify sensitive data and excessive access before AI makes that data easier to retrieve. | Cyera or Varonis; evaluate Microsoft Purview coverage in a Microsoft-heavy environment. ([forcepoint.com][5])([eon.io][6]) | | AI application and agent protection | Discover AI assets, assess vulnerabilities, inspect runtime interactions, and defend against prompt injection and data leakage. | Cisco AI Defense or Palo Alto Networks Prisma AIRS; consider Lakera for application-focused protection. ([akamai.com][3])([venn.com][4]) | | Identity, infrastructure, and monitoring | Authenticate users and services, enforce least privilege, secure APIs, isolate workloads, and feed activity into existing security operations. | Extend the enterprise’s existing identity, cloud security, API security, and SIEM stack rather than creating a separate AI-only security island. ([gtt.net][1])([fortinet.com][7])([orca.security][2]) | | Testing and governance | Run adversarial tests before deployment and after meaningful changes; establish ownership and an inventory of AI systems. | A repeatable red-team and risk-management process, not just another product purchase. ([fortinet.com][7])([orca.security][2]) | These are evaluation candidates, not a universal ranking: several comparisons come from vendors, and deployment fit matters more than a feature checklist. ## What matters most for agents For agents that can send messages, modify records, execute code, or spend money, I’d prioritize permission boundaries over prompt filtering. Authenticate and log interactions, give each agent only the access its task requires, and require human approval for consequential actions. Runtime guardrails help, but they should sit alongside access controls and secure APIs - not replace them.([orca.security][2])([fortinet.com][7]) ## How I’d choose My starting recommendation would be to extend your existing enterprise security stack first, then add specialist protection where it leaves gaps: - If employee use of public AI is the main risk, start with discovery and data-loss prevention. - If you’re deploying internal assistants over company documents, start with data classification and access governance. - If you’re building AI applications or agents, add runtime protection and adversarial testing early. These are different security surfaces, so a tool that covers one may not cover the others.([orca.security][2])([forcepoint.com][5])([cyera.com][8]) For a proof of concept, I’d test your actual workflows: sensitive uploads, malicious instructions embedded in retrieved documents, unauthorized retrieval, and attempts to invoke tools beyond an agent’s permissions. Measure protection, false positives, latency, and auditability - not just whether a vendor can block a demonstration jailbreak. That evaluates the controls across data, applications, and runtime behavior that enterprise AI security needs.([gtt.net][1])([orca.security][2]) [1]: https://www.gtt.net/resources/what-is-ai-security/ "What Is AI Security? Risks, Controls & Best Practices | GTT" [2]: https://orca.security/wp-content/uploads/2026/04/TAG-Enterprise-AI-Security-Handbook_2026.pdf "ENTERPRISE AI SECURITY HANDBOOK" [3]: https://www.akamai.com/resources/white-paper/generative-ai-best-genai-security-tools "8 Best Generative AI Security Tools for 2026 | Akamai" [4]: https://www.venn.com/learn/ai-security/top-8-enterprise-solutions-for-real-time-ai-monitoring-and-defense/ "3. Netskope One Ai Security" [5]: https://www.forcepoint.com/blog/insights/microsoft-365-ai-security-gap "Microsoft 365 Only Covers One of Three AI Risk Surfaces - Forcepoint" [6]: https://www.eon.io/blog/ai-data-security-platform "7 Best AI Data Security Platforms in 2026 Compared | Eon" [7]: https://www.fortinet.com/tw/resources/cyberglossary/generative-ai-security "What Is Generative AI Security? Risks, Benefits & Best Practices" [8]: https://www.cyera.com/blog/ai-security-platforms "12 Best AI Security Platforms in 2026 - cyera.com"