AI Security in the Enterprise: What Every Tech Leader Needs to Know

LLMs, AI agents, and chatbots come with built-in safeguards—but those guardrails aren’t keeping up with the pace of innovation. As enterprise adoption accelerates, safety and security must evolve just as quickly.

AI is already embedded into many of the tools businesses rely on. For organizations aiming to stay competitive, the appeal is clear: faster outcomes, lower costs, and smarter systems. But those benefits come with risks—risks that demand proactive security thinking.

At SPR, we believe AI is software—and that means it must be developed, tested, and secured with the same rigor as any other critical system.

In software security, an “attack surface” refers to the number of potential points where a malicious actor can attempt to interfere with, extract data from, or manipulate a system. A smaller attack surface usually means a more secure application.

AI, however, introduces entirely new surfaces—and they’re growing.

Unlike traditional software, LLM-based systems process enormous volumes of data and often operate with autonomy. In many enterprise environments, AI tools are third-party black boxes, making it critical to evaluate vendors carefully and manage third-party risks.

Consider recent issues with xAI’s chatbot, Grok, which generated politically charged content, false information, and antisemitic narratives. While government agencies may use such tools under strict controls, consumer-facing brands need to assess reputational and legal risks before diving in.

LLMs are powered by prompts—text, code, or even images—which creates entirely new categories of risk. Here’s what to watch for:

Prompt Injection

• Bypassing safety guardrails using manipulative or obfuscated phrasing
• Revealing hidden system instructions and operational constraints

Malicious Prompts

• Triggering denial-of-service through recursive or intensive requests
• Generating phishing emails or malicious code

Privacy Risks

• Extracting sensitive business or user data
• Leaking internal content
• Example: McDonald’s “Olivia” bot exposed private applicant information

Whether you're building AI tools or integrating them into your stack, these practices are essential:

Sanitize Inputs: Use structured input fields (like dropdowns or forms) to reduce manipulation risk.

Enforce Access Controls: Only authorized users should have access to sensitive LLM functionality.

Log Everything: Audit trails are crucial for tracking and responding to misuse.

Filter Harmful Content: Leverage moderation tools like OpenAI’s Moderation API to flag unsafe outputs.

Avoid PII: Minimize or eliminate the use of personally identifiable information in prompts.

Test Defensively: Intentionally probe with adversarial inputs to identify vulnerabilities.

Validate Outputs: Don’t treat AI outputs as facts—verify them with human oversight or automated validation layers.

• AI Security Posture Management (AI-SPM): Monitoring and protection tools for AI systems
• MITRE ATLAS: Framework of adversarial AI tactics – https://atlas.mitre.org
• MIT AI Risk Database: 1,600+ documented AI risk types – https://airisk.mit.edu
• OWASP Top 10 for LLMs: Key vulnerabilities in language model systems

AI security isn’t just a model problem—it’s a software problem. Enterprises that treat it that way will be better positioned to build trust, scale responsibly, and stay ahead of the next wave of risk.