Beyond mounting geopolitical and economic pressures, the growing threat of AI-linked cyberattacks and increased regulatory scrutiny has put organisations on high alert. This is prompting reviews of whether existing data protection tools, processes, and standards can truly support responsible AI use, safeguard sensitive information, and meet evolving regulations.
As AI advances faster than regulation can keep pace, compliance anxiety is rising alongside its sophistication, prompting businesses to embrace innovation while future-proofing their operations. Unsurprisingly, this tension is creating new challenges: 40% of UK companies say AI will present the most significant privacy and security risks in the years ahead.
While the pressure to demonstrate strong compliance is real, organisations that already prioritise it are better positioned than they might think. Extending those standards to cover AI, automation, and emerging technologies doesn’t mean reinventing the wheel – it means evolving existing frameworks to ensure data privacy and security remain front and centre.
New technologies don’t need new frameworks
AI is rapidly evolving, but that doesn’t mean businesses need to invest in new tools every week. The smarter first step is assessing where pre-existing data governance policies and technologies can be updated. Many companies are already taking this approach – 36% of UK businesses have updated their privacy policies in just the last six months.
In parallel, companies can invest in upskilling data protection officers and privacy advocates to help champion AI policy implementation across the workforce. Existing training programmes can also be refreshed with real-world AI scenarios, giving employees the practical context they need to navigate new technologies responsibly.
From a process standpoint, auditing existing data flows can reveal where AI is already interacting with sensitive information. With that visibility, organisations can begin to embed AI-specific compliance checks into established workflows like onboarding, access management, and incident response. Reviewing vendor policies is also key to keeping their supply chain compliant.
Some of these steps are simple, but incredibly effective. Still, businesses must recognise exactly how AI reshapes their understanding of risk, while ensuring employees are educated accordingly. Without that foundation, even well-intentioned updates may fall short of their full potential.
Redefine the concept of risk
Risk looks different for every business. It depends on factors like the industry, business model, and the type and volume of data handled. The size of a company is less relevant than the sensitivity of the data it manages – organisations handling large volumes of personal or sensitive data face higher risk, regardless of how many employees work there.
Whilst the general idea that AI can create risk rings true, the nature and severity of those risks are inherently contextual. Regulations, and even more so international standards like ISO, can help define what constitutes high, medium, and low risk, but the onus is ultimately on businesses to define their own AI risk strategy and use cases. Once defined, these use cases should be communicated throughout the organisation to ensure understanding at every level.
A strong risk-based approach should also strike a balance between compliance and operational agility. Risk management is most effective when it proactively identifies and mitigates issues. But if it overly constrains day-to-day work, it becomes a blocker. That’s why communication is critical: policies only work when they’re understood and embraced across teams.
Offer control and remain transparent
AI comes with risks, but also enormous potential. For a business to prepare for both, it must be clear about how it collects and uses data, and should offer individuals meaningful control. This includes explaining when data will be used, how much will be collected, and where it will be applied.
In practice, this might be as simple as an opt-out pop-up or a clearly written policy page. Giving users the ability to review a company’s approach to data, and decide whether they are comfortable with it, signals transparency and builds lasting trust.
But transparency isn’t a one-and-done exercise; data practices, user expectations, and regulations are always evolving, and companies must revisit permissions and notices regularly. Businesses should issue reminders about users’ current data sharing preferences and provide a simple way for people to revisit their choices, such as through a dedicated webpage or a survey.
Empowering customers to own the decisions around their data should be a non-negotiable for every single organisation. Equally important, though, is maintaining strong human oversight within the business. Without it, practices can miss the mark.
Embed human oversight into all AI operations
Technology should never run amok, and this is particularly true of AI. It’s well-documented that AI can be prone to bias, hallucination, and privacy challenges, so reaping the benefits whilst remaining compliant requires constant human oversight. With humans cross-checking and reviewing automated outputs, businesses can stay accountable and intervene when necessary.
But effective oversight is more than a final check. Businesses should assemble diverse review teams equipped to spot a wider range of potential issues, from technical errors to subtle biases. They should also define clear escalation paths for when outputs feel off, inaccurate, or inappropriate.
Of course, human reviewers are only as good as their training, and that training needs to be continuous. Everyone involved in AI, whether they build, review, or use it, should understand the risks, limitations, and ethical implications as well as their own company’s risk strategy. This knowledge allows them to intervene with confidence and make meaningful changes.
Ultimately, humans, not machines, should (and must!) make the final decision in every circumstance. Oversight ensures AI works for the business, not the other way around. When governance is embedded across systems, AI can drive innovation without sacrificing trust or compliance.
AI compliance doesn’t have to be intimidating. With the right mindset and structure, businesses can turn it into a competitive advantage. The key is to keep things simple, proactive, and people-centred. When AI is grounded in accountability and transparency, it supports businesses, customers, and employees alike, ethically and effectively.
