December 4, 2025

Large language models (LLMs) have moved from the experimental edge into the operational core of modern enterprises. They are powering customer service chatbots, enabling knowledge search across internal systems, and accelerating software development. For many organizations, LLMs are no longer “nice to have”; they’re essential infrastructure.

But with this rapid adoption comes a growing challenge: how do we secure them?

The 2025 SANS Security Awareness Report makes clear that human risk remains the primary attack vector. Social engineering continues to top the list of organizational concerns, while “incorrect use of AI at work” has now emerged as a leading human risk category. These findings highlight a new reality: securing LLMs isn’t just about technical guardrails. It’s about equipping people with the knowledge, behaviors, and culture to use AI responsibly.

The Human + AI Threat

LLMs amplify both the capabilities of organizations and the capabilities of attackers. On one hand, they automate tasks, accelerate decision-making, and improve productivity. On the other, they can be weaponized to craft highly personalized phishing messages, generate malicious code, or impersonate voices and identities with alarming realism.

But the more immediate risk for most enterprises is internal misuse. Employees may:

• Paste sensitive data into public LLMs without realizing it creates exposure.
  
• Blindly trust AI-generated outputs, introducing errors into business processes.
  
• Circumvent security policies because AI tools feel “easier” or “smarter.”
  

These aren’t malicious acts. They’re human ones, and that’s what makes them so dangerous. Attackers know the weakest link isn’t the model itself, it’s the workforce using it.

Why Technology Alone Isn’t Enough

Enterprises are investing heavily in technical controls to secure AI platforms: AI governance, access management, auditing, and red-teaming. These are necessary steps. But as the SANS report points out, technology alone cannot solve human risk.

Consider the parallels to phishing. Despite decades of investment in email security gateways, phishing remains the top human risk because attackers exploit human judgment, not just technical vulnerabilities. LLMs are no different. Even with the strongest guardrails in place, an uninformed user can undermine security in a single prompt.

That’s why training is no longer optional. It’s essential.

Training for LLM Security: A Two-fold Challenge

Securing LLMs is not a single skillset. It requires training on two distinct but interconnected fronts.

First, IT and security teams that implement LLMs must understand how to properly secure them against AI-specific threats. This includes hardening models against prompt injection, ensuring strong access controls, monitoring for misuse, and designing guardrails that prevent data leakage or malicious code generation. Technical teams need a clear framework for identifying where LLMs create new attack surfaces and how to mitigate them with sound engineering practices.

Second, security teams themselves must undergo a cultural shift. To be effective defenders, security professionals must recognize that the same AI-driven manipulation techniques targeting the broader workforce (deepfakes, voice cloning, AI-crafted phishing) can just as easily be aimed at them. Training isn’t just about configuring models correctly; it’s about preparing people inside security functions to question what they see and hear, validate information, and model the resilient behaviors they expect from the wider workforce.

Without both dimensions, technical mastery and cultural adaptation, LLM security programs will remain incomplete. Organizations that invest in this two-fold approach equip their teams not only to secure the technology but also to withstand the AI + human threats that will define the next decade of cyber risk.

The Business Case for LLM Security Training

The risks of insecure LLM use are not hypothetical. For security teams tasked with building and securing enterprise LLMs, these scenarios illustrate the stakes:

• Data exposure through uncontrolled inputs. When employees paste proprietary source code or sensitive records into an enterprise LLM without proper guardrails, the organization risks losing intellectual property and triggering regulatory violations. Security teams must be trained to implement controls such as prompt filtering, data classification enforcement, and usage monitoring. With the right safeguards in place, organizations can allow broad adoption of LLMs without sacrificing confidentiality or compliance.
  
• Propagation of misinformation. LLMs can generate outputs that appear authoritative but contain factual errors, biased reasoning, or even adversarial manipulation. If unchecked, these outputs can influence strategic decisions, undermine customer trust, or expose the company to legal and financial risk. Training equips security teams to design validation pipelines, establish human-in-the-loop review, and integrate monitoring that prevents flawed outputs from reaching critical business processes. When done well, LLMs enhance productivity without compromising accuracy.
  
• Shadow AI as a symptom of inadequate enablement. When employees feel official LLM solutions are too restrictive or lack functionality, they often turn to unsanctioned tools. This creates uncontrolled data flows and erodes visibility for the security team. Proper training enables security and IT professionals to anticipate these behaviors, design enterprise LLMs that balance usability with control, and provide sanctioned alternatives that employees actually want to use. The result is higher adoption of secure platforms and fewer unmanaged risks.

Each of these risks presents financial, legal, and reputational consequences. Regulators are also beginning to scrutinize AI governance, meaning organizations that lack clear training and policy frameworks may soon face compliance exposure.

The return on investment for LLM security training is simple: one prevented breach, one avoided compliance penalty, or one averted reputational crisis more than pays for the program.

From Risk to Resilience

Ultimately, securing LLMs is about securing trust: in the data, in the outputs, and in the workforce. Organizations that treat training as an afterthought will find themselves constantly playing catch-up against increasingly AI-powered attackers.

Those that embed LLM-specific training into their culture will not only reduce risk but also unlock AI’s true potential. When employees know how to use these tools safely, they become innovators, not liabilities.

Preparing for the Future

GuidePoint Security’s AI Security for Large Language Models training course was built with this exact challenge in mind. It combines theoretical knowledge with practical applications. Participants will gain in-depth understanding of AI fundamentals, security implications and the unique challenges posed by LLMs.

The convergence of human and AI risk is the defining security challenge of the decade. Training your people to secure the LLMs your organization uses is one of the fastest, most effective ways to reduce risk today while preparing for an AI-driven future.

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