LLMs have quickly shifted from experimental tools to core components of business operations. Today, about 71% of organizations are regularly using generative AI in at least one business function. That level of adoption creates a new and very real attack surface.

When LLMs are fine-tuned with proprietary data and connected to internal systems, they can:

• Leak sensitive data if training or prompts include private information.
• Be manipulated through prompt injection, where attackers trick the model into bypassing safeguards.
• Spread corrupted outputs if training data or model inputs are poisoned.
• Trigger downstream damage when applications consume AI output without validation (e.g., executing generated code).

For CIOs, this means LLM security can’t sit on the sidelines. It needs to be:

• Operationalized – embedded into day-to-day processes, not handled as a one-off project.
• Integrated – connected to your existing security stack (SIEM, GRC, cloud, network, identity, data).
• Continuous – monitored and updated as threats evolve, not just at deployment.

Platforms like Prisma AIRS Runtime help by discovering shadow AI usage, inspecting prompts and responses in real time, logging rich context (who used what model, when, and how), and feeding that data into your SIEM. This turns LLM security from a theoretical concern into a managed, measurable part of your cybersecurity program.

Enterprises can materially reduce LLM risk by focusing on a few concrete practices that fit into existing security operations.

1. Get ahead of shadow AI

• Employees are now building and using unsanctioned AI apps and agents, often outside IT’s visibility.
• This can expose intellectual property, introduce unvetted code, and bypass governance.
• Tools like Prisma AIRS Runtime can discover this usage by analyzing network traffic and intercepting API calls, then:
• Surface where unapproved LLMs and agents are in use.
• Inspect prompts and responses in real time for sensitive data exposure and malicious content.
• Log every event with context and forward it to your SIEM.
• From there, you can enforce policies to block risky behavior and offer secure, sanctioned alternatives.

2. Build AI-specific security skills and governance

• Traditional security skills don’t fully cover model-level threats like prompt injection, model manipulation, and data leakage.
• Many organizations are creating an AI Security Center of Excellence (CoE) to:
• Run continuous red teaming against LLMs and agents.
• Set guardrails and policies for secure AI adoption across business units.
• Act as both enforcer and advisor for new AI initiatives.
• External partners and resources (such as Palo Alto Networks Cyberpedia) help teams keep pace with a fast-moving threat landscape.

3. Integrate and automate AI security signals

• Log all AI-related activity in a standardized format: prompts, failed injections, sensitive data exposure, API usage.
• Feed these logs into your SIEM and GRC tools for a unified view of risk.
• Use automation to connect AI events with broader enterprise signals and trigger policy-aligned responses.
• Prisma AIRS Runtime supports this with API and Network Intercept, generating rich logs that integrate into Strata Cloud Manager or your SIEM.

4. Continuously red team your AI systems

• Because the LLM threat landscape changes daily, one-time testing isn’t enough.
• Automated red teaming agents can continuously test for:
• Prompt injection and jailbreaking.
• Data exfiltration and leakage.
• Output manipulation and insecure responses.
• Prisma AIRS Red Teaming Agent simulates attacker behavior at scale and feeds findings into Runtime Security, turning test results into actionable policies.

5. Implement runtime security as a 24/7 guardrail

• Runtime security continuously inspects prompts, responses, and system activity as they happen.
• It detects and blocks threats like prompt injection, data exfiltration, and anomalous behavior before they cause damage.
• Prisma AIRS Runtime captures full context (who accessed what, when, and how) and standardizes events into logs and metrics for correlation with the rest of your telemetry.

Together, these steps help you move from ad hoc controls to a repeatable, operational framework that keeps AI innovation aligned with your risk appetite.

Long-term AI security depends on how you design your systems and who you partner with, not just on point controls.

1. Design with “secure AI by design” in mind

• Build security into every stage of the AI lifecycle, from data collection to deployment.
• Treat LLMs as permanent capabilities, not short-term experiments.
• Plan for continuous validation (via red teaming and runtime monitoring), not one-time certification.

2. Use Retrieval-Augmented Generation (RAG) to reimagine how models access data

• Instead of constantly fine-tuning and retraining models, RAG connects the LLM to a private, verifiable knowledge base (e.g., company PDFs, internal reports, live feeds).
• When a user asks a question, the system:
• Retrieves the most relevant documents.
• Packages them as context for the LLM.
• Generates an answer using both general knowledge and your specific data.
• RAG helps address three key challenges:
• Hallucinations – grounding responses in trusted sources reduces incorrect answers.
• Outdated knowledge – pulling fresh information at query time keeps outputs current without frequent retraining.
• Data security – sensitive data stays in your databases and is retrieved only when needed, reducing memorization and unintended leakage.

3. Recognize and secure LLM agents as a new risk class

• LLM agents don’t just answer questions; they observe, think, and act in a loop to complete tasks.
• They can connect to tools (payments, calendars, CRM), access data, and execute code, which reshapes the risk profile.
• Key agentic threats include:
• Supply chain risk from external tools and libraries.
• Excess permissions that give attackers broad access if the agent is compromised.
• Memory poisoning, where bad data is stored and reused later.
• Goal hijacking and planning manipulation, where attackers redirect the agent’s tasks.
• Mitigation requires tighter permissioning, tool vetting, memory controls, and continuous monitoring of agent behavior.

4. Choose a security platform rather than building everything in-house

• Building your own AI security stack can be long, expensive, and hard to maintain at attacker speed.
• A unified platform like Prisma AIRS by Palo Alto Networks is designed to:
• Provide breadth across cloud, network, identity, and data for end-to-end visibility.
• Offer proactive defense with built-in AI red teaming and runtime protection.
• Integrate natively with enterprise systems, normalizing logs and aligning with SIEM and GRC workflows.
• Future-proof your investment by absorbing the cost of ongoing threat research and product evolution.

By combining secure-by-design principles, RAG-based architectures, careful handling of agents, and a capable security platform, CIOs can scale AI in a way that balances innovation with disciplined risk management.