Cybersecurity teams have always raced to stay ahead of novel exploits—and with Large Language Models (LLMs) and agents rapidly woven into everything from chatbots to code generators, the margin for error is razor-thin. Yet LLMOps/AgentOps efforts traditionally focus on end-user experience, scalability and cost savings rather than risk reduction. As a lead Everforth Apex cybersecurity consultant says, “It is very easy to deploy any given technology, but it is very hard to deploy any given technology securely. Getting it right is not enough, you must make sure you cannot get it wrong.”  

We are at a critical point right now with the advent of LLMs and agents. A new technology that provides tangible benefits has emerged, gained traction, has been quickly adopted by organizations everywhere, and some enterprising folks have found ways to misuse it. There is, however, a period of time between vulnerabilities being introduced by a new technology and the establishment of best practices that would “solve” the problem. We are still at the phase where the body of knowledge of what can go wrong is incomplete. Bad actors and researchers alike are still poking and probing the technology to find ways of abusing it for fun and/or profit. We have seen malicious actors trick chatbots powered by LLMs into disclosing sensitive information, performing restricted actions, and even promising things the company cannot deliver (let’s not forget the customer who used a ChatGPT hack to negotiate a new car for $1 in 2023). As such, we must rely not only on our data scientists, but our cybersecurity teams to ensure LLM and agent-based applications are analyzed thoroughly and risks mitigated according to protocol before we push to production.  

Embed Security and Risk Management Across Your LLMOps Pipeline 
By folding cybersecurity into each phase: asset identification, threat enumeration (STRIDE), risk assessment, automated testing and continuous monitoring—you not only harden deployments against known and emerging threats but also build the governance confidence your enterprise and clients demand.  For risk assessment, we leverage the NIST AI RMF, which offers an outcome-driven overlay that aligns perfectly with a security-centric LLMOps lifecycle. Its four core functions map directly to the phases above: govern, map, measure, and manage. Underpinning these functions are seven “trustworthy AI” attributes—valid & reliable, safe, secure & resilient, accountable & transparent, explainable & interpretable, privacy-enhanced and fair—that should guide every control you deploy. Together, these methodologies offer a comprehensive approach to AI risk management and governance. 

Why Now Matters 
Just as unvalidated input once led to SQL injection and XSS exploits, prompt injection and context poisoning are now emerging as new vulnerabilities. Early PoCs have shown chatbots disclosing internal policy documents, proprietary product development plans, attackers hiding malicious prompts in user uploads, and even automated code generators inserting backdoors. Until hardened best practices and native platform safeguards become widespread, every AI rollout carries a fresh attack surface. 

AI as a Cyber-Force Multiplier 
Paradoxically, the same AI technologies you’re securing can supercharge your defenses: 

• Log Analysis: ML models sift through overwhelming amounts of log, traces, and telemetry data to identify trends and anomalies.
• Cyber Analysis Acceleration: Assist with strategic planning, performing risk assessments, threat modeling, identity management, incident response, vulnerability management and research, baselining, and detection.
• Automated Response: AI agents orchestrate containment workflows at machine speed, freeing analysts for strategic work. 

Secure Your Generative AI Journey 
Don’t treat AI like just another application—treat it as a new attack surface that demands a tailored risk-management strategy. Everforth Apex helps you design and implement comprehensive AI governance frameworks—from secure prompt patterns and automated content moderation to continuous monitoring and incident-response playbooks—so you can deploy cutting-edge LLM applications with confidence. 

Bruno Cardoso, Lead Consultant, also contributed to this article.