Without strong safeguards against threats like adversarial attacks, data misuse or intellectual property theft, large-scale adoption becomes difficult. No one wants to deploy an AI tool only to discover later that it leaked sensitive data, exposed proprietary IP or became a new attack surface for adversaries. Beyond the immediate operational and security fallout, there’s also the risk of lawsuits over data misuse, regulatory penalties or contractual breaches. For many organizations, the uncertainty of those risks is enough to slow or even halt adoption until stronger safeguards are in place. In fact, a recent Forrester report shows that data privacy and security concerns remain the biggest barrier to generative AI adoption. Building trustworthy AI requires attention to privacy, cybersecurity and AI governance.
AI isn’t just a race for speed; it’s a race for trust
AI isn’t just about faster chips, bigger models or who gets to market first. It’s about whether enterprises, governments and individuals feel confident enough to use it in the first place. The hesitation around DeepSeek, a Chinese artificial intelligence system, illustrates this point, as many potential users and governments remain wary due to unresolved privacy and cybersecurity concerns that undermine trust in the system and threaten national security.
We don’t have to speculate about what happens when trust is ignored. The crypto industry offers a cautionary tale for revolutionary technologies. Without regulation tailored to the unique nature of blockchain, the space was plagued by cyberattacks, privacy failures, security breaches and widespread illicit use. Now, as regulators begin clarifying the legal landscape, such as by requiring regular public disclosures and compliance with anti-money laundering and export control laws, many in the industry argue that digital assets can finally gain legitimacy and move into the financial mainstream.
