A Modern Approach to Generative Technology and Compliance

The digital world is transforming at an unprecedented pace. Generative AI introduces groundbreaking opportunities for innovation; alongside serious cybersecurity challenges. While embedding AI across business operations increases efficiency, it also exposes systems to new forms of risk. To stay resilient and secure, organizations need to tailor their cybersecurity strategies to these evolving technologies.

Why Generative AI, Why Now?

The simple answer is this: the productivity gains are game-changing. In the past, implementing new tools could take years and bring only marginal benefits—often less than a 5% boost. Generative AI is different. It’s inexpensive (sometimes even free), easy to adopt, and in many cases can deliver productivity increases of 15% or more. This technology is now a key differentiator in the marketplace, making it a critical part of strategic growth. As with any innovation, security and compliance must be part of the conversation.

Getting a Grip on AI Basics

Before building any security framework, businesses need to understand how generative AI works. Key concepts include:

• Data Sets: These train the large language models (LLMs). The quality and trustworthiness of the data are vital. Businesses should avoid using sensitive information—such as Protected Health Information (PHI) or Personally Identifiable Information (PII)—in training data.
• Prompt Engineering: This is the art of writing clear, targeted inputs to generate accurate and relevant outputs. A vague prompt about “bears” will yield different results than a specific request for a scientific article on bear behavior.
• Temperature: This setting affects the randomness of responses. A lower temperature leads to more consistent answers, while a higher one creates more varied (and sometimes unreliable) outputs, often referred to as “hallucinations.”

Knowledge and training are foundational steps before diving into AI-related security planning.

Building an AI Security Approach

Once the fundamentals are clear, organizations can begin defining appropriate protections. Policy development is now a must. Not long ago, many companies considered banning AI use altogether. But as productivity benefits became clear—and as employees adopted AI informally—many shifted focus. Today, formal AI usage policies are common, and internal committees are often formed to guide responsible use.

Key focus areas include:

• Acceptable Use: Outlining how AI tools should be used in line with company values and standards. This includes quality checks and clearly defined limits on inappropriate use.
• Ethical Boundaries: Preventing misuse of AI for biased, unsafe, or discriminatory practices. This includes restricting the creation of content that infringes on intellectual property or violates ethical norms.
• Security Awareness: Training staff to avoid entering confidential data; like PII or trade secrets, into public AI systems. Adopt a “trust after verification” mindset.

With the right policies, companies can foster a transparent, risk-aware environment that encourages smart AI adoption.

Strengthening Technical Safeguards

From a technical perspective, the most powerful defenses remain Multi-Factor Authentication (MFA) and active monitoring. As deepfake technology becomes more accessible, confirming someone’s identity through a shared phrase or known cue can help verify legitimacy.

In addition, AI systems should be monitored frequently for patterns of use, output quality, and how closely results align with expectations. By collecting meaningful data—like frequently used prompts or the cleanliness of input data; organizations gain a clearer picture of how their AI tools are performing.

Core Elements of an AI Policy

A well-rounded AI policy should address:

• 1. Approved vs. restricted data, use cases, and tools
• 2. When and how to seek support or guidance
• 3. Transparency requirements around AI-generated content
• 4. Procedures for vetting and approving new AI tools
• 5. Ethical guidelines
• 6. Oversight mechanisms, such as regular usage audits

Compliance Requirements and AI

Finally, AI use must align with relevant regulations. Here are a few key frameworks to keep in mind:

• GDPR (General Data Protection Regulation): Companies working with data from EU citizens need to make sure AI systems handle personal data responsibly. Individuals must also be given the option to have their data deleted from AI models.
• HIPAA (Health Insurance Portability and Accountability Act): In healthcare, AI tools must protect sensitive health data and prevent any potential misuse or exposure of PHI.
• EU AI Act: This regulation requires businesses operating in or with the EU to assess AI systems based on their level of risk and follow more stringent rules for high-risk applications.
• U.S. Frameworks: While there’s no overarching federal law governing AI in the U.S. just yet, organizations can look to the NIST AI Risk Management Framework and ISO/IEC 42001 for guidance. These standards help companies adopt responsible practices and prepare for future regulation.

Hurricane Labs | Managed Cybersecurity Services

• At Hurricane Labs, we specialize in providing top-tier managed cybersecurity services with a strong focus on Splunk. Since 2003, our team of experts has been helping organizations strengthen their security postures through 24/7 Security Operations Center (SOC) management, Splunk Enterprise Security support, penetration testing, and consulting services. As official partners of both Splunk and CrowdStrike, we deliver tailored solutions that align with our clients’ unique needs and environments. Our mission is to work closely with every organization we serve, delivering customized, proactive strategies to stay ahead of evolving cyber threats.