What is AI Safety and Security?

Intro: AI Safety beyond just best practices 

The generative AI (GenAI) boom is reshaping industries and daily life. As businesses rush to leverage AI’s transformative potential, many overlook or underestimate the risks associated with this technology. Being unaware, or ignoring these risks can lead to serious consequences, from business losses and brand damage to privacy breaches and even real-life security threats.

While embedding AI into operations has never been easier, the real challenge is ensuring its deployment doesn’t backfire. It’s not about asking if AI can be implemented, but how to do it right. As AI develops rapidly and the market frenzy intensifies, the path to safe implementation remains unclear, particularly as AI grows more autonomous and capable. 

In a world where GenAI can be easily misused or even weaponized, AI safety and security aren’t just best practices—they’re the fundamentals on which operational and trustworthy AI must be built.

Defining AI Safety and AI Security 

AI Safety and Security are two interconnected goals that ensure AI systems align with human values and intentions (safety) while safeguarding them from exploitation, manipulation, or malicious use (security). In simple terms, safety protects users from the system, while security protects the system from users.

AI Safety aims to ensure that AI technologies benefit humanity, minimizing harm by aligning their behaviors with human values and ethical standards. AI Security, on the other hand, protects systems and the vast datasets they process, preventing bad actors from hijacking these powerful tools for abuse, misuse, or exploitation and averting breaches, unauthorized access, and manipulation.

While we often focus on protecting AI systems from human exploitation, it’s equally important to address the risks posed by AI itself, or AI agents. These systems may unintentionally produce harmful outputs due to flaws in their design or programming. Ensuring robust security means accounting for both human-driven threats and the unintended consequences that can arise from within the AI’s own operations.

Although distinct, these two aspects are closely connected: A security breach can compromise an AI system’s safety, while weak safety protocols can create vulnerabilities that attackers might exploit.

The fact that these two elements complement each other is why a comprehensive approach is essential. Together, they form the foundation for building trustworthy AI systems.

Why AI Safety and Security Can’t Wait

We know that GenAI is moving fast—faster than most organizations can keep up. Every week brings a new model, a new capability, or a new way to integrate AI into daily workflows. While the innovation is impressive, and often surprisingly easy to implement, the breakneck pace leaves little time to pause and ask: Are we doing this safely?

The reality is that many teams are racing to launch AI features without fully understanding the risks. It’s not negligence but merely moving fast without a clear playbook or real safeguards in place. However, losing focus on safety or security today can lead to far more serious consequences tomorrow.

From reputational damage to regulatory blowback, unsafe or insecure AI can cause long-term harm that’s hard to undo. And as GenAI systems become more powerful and autonomous, the ripple effects of a single mistake can scale quickly.

That’s why AI safety and security can’t wait. They’re not just technical considerations—they’re business-critical. When something goes wrong, whether it’s a toxic output, a data leak, or a compromised model, the damage can be immediate and far-reaching.

“AI Gone Wild”: Real Risks from Unsafe or Insecure GenAI 

When GenAI models are deployed without proper safety and security guardrails, the risks aren’t theoretical—they’re already playing out in the real world, with very real human costs.

In late 2024, an AI-powered assistant designed for casual conversations allegedly encouraged a teenage boy to self harm. The family has filed a lawsuit against the platform, claiming the model’s guidance played a direct role in the tragedy. In another case, a prominent LLM came under fire after generating racially and historically inaccurate images based on misleading user prompts, raising urgent concerns about AI bias and content oversight. Meanwhile, in New York City, a GenAI tool piloted by the municipal government was found to give small business owners illegal advice, putting them at actual legal risk.

These incidents are drops in an ocean of publicly covered tales and PR crises now popularly known as “artificial intelligence got it catastrophically wrong.” But they’re more than just headlines, they reflect a rapidly growing pattern of misuse and failure. At ActiveFence, our threat intelligence teams have seen firsthand how quickly things can spiral when safeguards are weak or missing. In several cases, our researchers uncovered coordinated efforts by child predators to generate synthetic explicit content, including child sexual abuse material (CSAM), by manipulating widely used GenAI systems to bypass their built-in protections. Similarly, we’ve seen how terrorist groups exploited these systems to create and distribute terror propaganda and false narratives. For example, a fake image of an explosion near the Pentagon went viral. This caused panic briefly, until it was confirmed as fake.

These cases demonstrate how model vulnerabilities are being actively and repeatedly exploited across  different use cases and industries. The tactics are evolving rapidly, and without proactive defenses, the risks will only continue to escalate.

From Principles to Practice: What Operational AI Safety Looks Like

Turning AI safety principles into action requires a proactive, ongoing effort. Here are some of ActiveFence’s best practices for operationalizing AI safety and Security:

• Proactively Red Team: Stress-test your model by simulating real-world attacks to identify weaknesses before they can be exploited by bad actors. Performing these exercises regularly helps uncover and adapt to new vulnerabilities.
• Continuously Update Data Sets: Use a mix of high-quality synthetic and organic data that’s regularly updated, labeled for future use, and prioritized by recency. This keeps models aligned with emerging threats and ensures they stay agile.
• Leverage Guardrails: Install tailored controls based on your specific use cases to prevent harmful or undesired outputs. Guardrails should address factors like languages, abuse areas, and align with different modalities.
• Enable Observability: Visualize and track guardrail performance to identify areas for improvement and adjust models and datasets as needed. This ensures continuous refinement and optimization.

Rethinking AI Risk, Reimagining AI Safety

As GenAI continues to evolve, many organizations are still trying to understand what AI Safety and Security truly mean for their operations. With the rapid pace of development, it’s easy to feel uncertain about where to begin or what steps to take. The real challenge isn’t just about implementation—it’s about fostering a mindset of constant adaptation and vigilance.

At ActiveFence, our goal is to raise the bar for how the industry thinks about AI risk. We focus on building AI safety frameworks that are adaptable, ensuring compliance at speed and scale. Our approach is rooted in practical application so that AI systems remain resilient in the face of emerging threats.

With deep understanding of abuse areas, bad actors, and their tactics, techniques, and procedures, we provide insights that transform how AI safety and security should be approached. We don’t just help organizations understand the complexities of AI risk; we help them design systems that scale securely and responsibly. It’s not about reacting to threats when they emerge; it’s about anticipating and preventing them before they have a chance to cause harm.

Conclusion: Better Safe Than Sorry

Having a safe and secure AI model is the very foundation of deploying it. Safety is not a blocker, but a pillar and a catalyst for success. In the past year, we have partnered with leading GenAI developers worldwide to help them understand AI risks and guide them in building robust systems that are both innovative and secure. By embracing best practices, maintaining constant vigilance, and staying ahead of emerging threats, organizations can harness AI’s potential without compromising safety.

The future of AI depends on the steps we take today to safeguard it for tomorrow. Without proactive measures, vulnerabilities multiply and misuse can lead to lasting harm—from reputational damage to regulatory consequences. By anticipating and preventing risks rather than reacting after the fact, we create an ecosystem where AI continues to benefit humanity responsibly. Better safe than sorry.