Yes, your AI can absolutely be tricked into ignoring its instructions or giving bad advice. In the AI era, attackers often do not need to find traditional code vulnerabilities; they simply need to craft the right prompt. Adversaries use several manipulative linguistic techniques to trick AI models:

• Jailbreaks: These are attempts to circumvent the AI's built-in safety guardrails, forcing the model to ignore its own safety rules.
• Prompt injections: Attackers use sophisticated linguistic exploits to override an AI system's core instructions and alter its intended behavior, forcing it to behave maliciously or execute unauthorized commands.
• Multi-turn attacks: Adversaries simulate complex, multi-stage conversations (such as "skeleton key" or "crescendo" attacks). By layering their interactions, they attempt to trick Large Language Models (LLMs) into bypassing safety guardrails that might lack full session context.
• Tool poisoning: If your AI operates autonomously as an agent, it can be manipulated or tricked into interacting with a malicious external tool or remote server.

Because standard built-in safeguards are not enough to prevent these exploits, organizations must deploy specialized security layers:

• Pre-deployment hardening: Solutions like Netskope One AI Red Teaming automate adversarial testing by exposing your private models to thousands of simulated prompt injections and multi-turn attacks. This helps you find and fix vulnerabilities before the AI goes live.
• Real-time guardrails: Once live, tools like Netskope One AI Guardrails provide a runtime defense layer that analyzes the multi-stage intent behind every prompt and response. It actively blocks prompt injections and jailbreak attempts in real time, ensuring the AI strictly follows its responsible usage policies.

No, the native safety filters in public large language models (LLMs) such as OpenAI or Gemini do not provide enough protection for enterprise AI use. While they offer a baseline level of safety, they are vulnerable to sophisticated exploits and lack the context-aware data security required by enterprises. Here is why relying solely on built-in safeguards is insufficient:

• Native guardrails are easily bypassed: Research shows that jailbreak attempts succeed nearly 20% of the time. Attackers, or curious employees, often need less than a minute and only five or six interactions to crack a standard model's built-in safeguards.
• Vulnerability to prompt-based attacks: The AI attack landscape has shifted, and attackers no longer need to find traditional code vulnerabilities; they just need to craft the right prompt. Adversaries use manipulative linguistic exploits, such as prompt injections and complex multi-turn attacks, to override an AI's system instructions and force it to behave maliciously or exfiltrate data.
• Legal and reputational risks: Native filters are often inadequate at preventing the generation of harmful, discriminatory, or legally problematic content. Users may inadvertently generate and distribute patented or copyrighted material, which exposes the enterprise to significant legal and reputational liabilities.

To safely harness AI, enterprises must deploy dedicated runtime defenses that sit between the user (or autonomous agent) and the LLM. Solutions like Netskope One AI Guardrails analyze the multi-stage intent behind prompts in real time. By integrating with Data Loss Prevention (DLP) and Threat Protection, these enterprise-grade controls actively block prompt injections, jailbreaks, and sensitive data leaks before they happen, ensuring AI usage remains compliant and secure.

Adding a runtime defense layer does not have to slow down your AI's response time, provided you use a solution specifically architected for high performance. While it is true that traditional security tools can sometimes compromise speed and introduce an "AI security latency tax" (which can frustrate impatient users, drive them toward unsecured shadow AI, or even cause systemic failures in high-stakes automated workflows), modern solutions are built to avoid this. For example, the Netskope NewEdge AI Fast Path avoids this security latency tax by efficiently optimizing the network paths between users, autonomous agents, and critical AI destinations when injecting security in-line. It achieves an experience that is virtually indistinguishable from a direct connection through several key architectural advantages:

• Extensive peering and direct connections: The NewEdge Network features over 11,000 network adjacencies and connects directly to more than 750 unique autonomous system numbers (ASNs), including top AI destinations like OpenAI, Anthropic, Google, Microsoft, and AWS. This direct peering eliminates unnecessary traffic hops and reliance on transit providers, establishing a fast, direct path to AI services.
• Global edge compute: With a footprint of more than 120 data centers across 75+ regions, NewEdge processes traffic and runs its complete security stack at the edge. This globally distributed architecture places users and AI agents just milliseconds away from the LLMs, GPUs, and CPUs that power AI.
• Dynamic route control: Using telemetry data to make tens of thousands of route changes per day to identify the absolute fastest path to AI destinations while routing around internet congestion and ISP connectivity issues.
• Fast security at the edge: The NewEdge network powers the Netskope One Platform allowing AI security services to be deployed at the edge, closest to your users where it's needed.

By applying these networking capabilities, the AI Fast Path specifically helps AI workflows by:

• Minimizing "time-to-first-token" (TTFT) for conversational AI, delivering faster inference results from prompt to response.
• Accelerating agentic AI by providing the high-speed processing required for complex, iterative, multi-prompt autonomous workflows.
• Optimizing retrieval-augmented generation (RAG) by speeding up the connectivity between LLMs and external data sources, ensuring higher quality, real-time outputs.
• Enhancing Large Language Model (LLM) performance when accessing massive volumes of distributed data, such as through Model Context Protocol (MCP) gateways.

AI guardrails are an essential requirement for organizations that need to satisfy rigorous regulatory compliance standards, manage data privacy, and mitigate severe legal risks. As organizations adopt AI, integrating dedicated runtime guardrails helps them meet critical compliance and governance objectives in the following ways:

• Enforcing continuous policy compliance: AI guardrails act as an automated moderator for both human and autonomous agent interactions, ensuring continuous policy compliance and maintaining data integrity in real time.
• Mitigating legal and intellectual property (IP) risks: They automatically identify and block the sharing or retrieval of patented and copyrighted data within AI-generated responses. This proactively defends the enterprise against emerging legal liabilities associated with generative model outputs.
• Ensuring brand safety and responsible use: Guardrails automatically filter out harmful, discriminatory, or inappropriate content (such as hate speech or violence), keeping AI usage strictly within your organization's defined risk tolerance.
• Providing audit-ready traceability: Guardrails maintain searchable conversation logs that are matched to policy triggers. Coupled with role-based access control, these logs ensure that only authorized investigators can view sensitive chat histories, which is a key requirement for compliance audits.
• Aligning with industry frameworks: By mapping AI policy violations including content moderation, threats, and Data Loss Prevention (DLP) to recognized frameworks like MITRE ATLAS and the OWASP Top 10 for LLMs, guardrails significantly enhance compliance efficiency and reduce investigation times.