Artificial intelligence (AI) is evolving fast, and its rapid integration into everyday systems combined with the investment companies are making in adoption have resulted in cybersecurity teams hitting the brakes hard – sometimes a little too hard. By overthinking AI risks without proper context, these teams risk stifling adoption and leaving their companies at a competitive disadvantage. In this blog, we’ll explore how failing to break down AI into specific contexts can lead to unnecessary panic and global restrictions. We’ll also draw parallels between AI risks and familiar cybersecurity challenges, while highlighting the truly novel threats that deserve attention.
The Pitfall of Treating AI as a Monolith
One of the biggest mistakes cybersecurity teams make is viewing AI as a single entity rather than dissecting it into distinct contexts: AI engineering, AI development, and AI application usage. This lack of nuance often results in knee-jerk reactions, like imposing broad bans on AI tools, which can paralyze innovation without meaningfully reducing risk.
- AI Engineering: This involves building and training foundational models, such as large language models (LLMs) or custom neural networks. It’s the “heavy lifting” phase, often handled by specialized teams with access to vast datasets and computational resources. Risks here are high-stakes, like data poisoning during training, but they’re also contained within controlled environments.
- AI Development: Here, developers integrate pre-trained models into software, fine-tuning them for specific tasks. Think of creating a chatbot using an API from OpenAI or Google. This stage introduces risks like insecure API calls or unintended model behaviors, but it’s more about secure coding practices than reinventing the wheel.
- AI Application Usage: This is the end-user level, where employees leverage off-the-shelf AI tools for everyday tasks. The risks are often lower and more manageable, focusing on data privacy and output verification.
When security teams don’t differentiate these layers, they tend to panic and enforce blanket policies. For example, in 2003, a number of organizations restricted employee use of ChatGPT amid fears of confidential data exposure. While reasonable at the time when organizations were still getting their thoughts together, these rules are still in place two years later which can slow down non-sensitive workflows, like research or code assistance, leading to frustration and shadow IT – where employees sneak in tools anyway, creating even bigger risks.
By breaking AI down into these contexts, teams can apply tailored controls: strict governance for engineering, code reviews for development, and user training for app usage. This fosters adoption while keeping security intact.
AI Risks: More Familiar Than You Think
Much of the hysteria around AI stems from treating it as an entirely new beast, but many risks are simply evolutions of existing cybersecurity challenges. Recognizing these similarities can help teams avoid overreaction and leverage tried-and-true mitigation strategies.
For starters, data leakage in AI – such as accidentally feeding proprietary information into a public model – is similar to the classic insider threat or misconfigured cloud storage. Just as we’ve long advised against emailing or uploading sensitive files unsecured, the same principle applies – train users on what not to input into AI tools. Similarly, prompt injection attacks, where malicious inputs trick an AI into revealing secrets or executing harmful actions, mirror SQL injection or cross-site scripting (XSS) vulnerabilities in web apps. Developers have been patching these for years; applying input sanitization and validation to AI prompts is a logical extension.
Another parallel is malware distribution. AI-generated code or content could hide malicious payloads, much like phishing emails or trojan horses. But antivirus tools and sandboxing environments already handle this well – why reinvent the wheel when you can scan AI outputs just as you would any other download?
In essence, these overlaps mean cybersecurity teams don’t need to start from scratch. Frameworks like NIST’s Cybersecurity Framework or OWASP’s guidelines for secure software can be adapted seamlessly. Overthinking AI as “unprecedented” ignores this, leading to paralysis. Instead, map AI risks to your existing risk register: if it’s like a known threat, treat it accordingly.
The New Frontiers: Identifying Truly Novel AI Risks
That said, AI does introduce some genuinely new risks that warrant fresh scrutiny – but not blanket bans. Focusing on these can help security teams prioritize without crippling progress.
One key novelty is model inversion and adversarial attacks. Unlike traditional software, AI models can be probed to reconstruct training data (inversion) or fooled with subtle inputs (adversarial examples). These aren’t direct analogs to existing hacks; they exploit the probabilistic nature of machine learning. Mitigation might involve robust training techniques, but overthinking could lead to banning all external models when in-house fine-tuning would suffice.
Another is hallucinations and bias amplification. AI can confidently output false information or perpetuate biases from its training data, leading to reputational or ethical risks. This is new because it’s not just about security breaches but about trustworthiness. Security teams should collaborate with the ethics boards here, implementing output filters or diverse datasets, rather than blocking AI outright.
Supply chain risks in AI are also amplified – relying on third-party models means potential backdoors or poisoned updates, similar to the SolarWinds hack but embedded in algorithms. A 2024 incident involving a popular open-source AI library with hidden malware highlighted this – teams responded by vetting dependencies more rigorously, not by ditching AI altogether.
To address these, adopt a risk-based approach: conduct AI-specific threat modeling per context. For app usage, simple guidelines like “verify AI outputs” work. For development, use tools like adversarial robustness testing. And for engineering, invest in secure data pipelines. This way, you identify and mitigate novelties without overgeneralizing.
Striking the Balance: Empowering Adoption Through Smart Security
In conclusion, cybersecurity teams play a vital role in safeguarding against AI risks, but overthinking without context can turn guardians into gatekeepers, crippling adoption and innovation. By dissecting AI into engineering, development, and usage layers, recognizing familiar risks, and zeroing in on true novelties, organizations can embrace AI responsibly. Remember, the goal isn’t zero risk – that’s impossible – but managed risk that aligns with business objectives.
