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7%

The potential penalty for misusing AI in hiring? As much as 7% of global annual revenue.

71%

That’s how many employees are already using unsanctioned AI tools at work (Microsoft research).

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New laws are increasingly shaping how organisations implement and disclose AI in their hiring processes. That means talent leaders are responsible not only for using the tech effectively, but for making sure it’s applied in a way that’s compliant and fair.

But when governing AI usage in TA processes, compliance and risk mitigation are not the only goals. Because the teams that govern their AI proactively will also shape better processes, create greater transparency, and build deeper long-term understanding.

Here’s what you need to know to both stay in step with the law and build AI governance for TA processes that add value beyond ticking a box.

AI in hiring comes under legal scrutiny

In the race to implement AI across the whole organization, TA has been operating mainly on a ‘do’ mandate, not a ‘design’ one. Top-down pressure from company leaders to drive greater efficiency coupled with a tricky marketplace for hiring has forced many talent leaders to implement before they’re ready.

In many cases, this environment has been great for standing up AI experimentation quickly and discerning what tools add value to talent workflows. But across global markets, the law is finally starting to catch up with evolving AI use, and regulate its implementation.

The EU AI Act, for example, represents one of the most comprehensive attempts of this effort to date, setting the standards for transparency, accountability, and fairness across AI-enabled processes and tools, including resume screening, video interviews, and candidate scoring.

In the US, state- and city-issued laws, such as New York’s Local Law 144 and new additions under California’s Fair Employment and Housing Act legislate on AI transparency and discrimination — requiring employers to audit tools for bias and disclose where and when it’s used in candidate evaluation. 

Together, these new laws mark a shift in employer accountability. The onus for ethical and compliant AI usage is no longer just on AI vendors — but also on any organization that uses these tools to hire their people.

And for talent teams, falling foul of these complex and evolving laws will come with some very big penalties — particularly in multinational workforces, where specific requirements may differ across country borders.

“Only around 7% of UK organizations have a policy on AI usage, whereas research from Microsoft shows that 71% of people are already using [unauthorized] AI at work,” said Siobhan Brunwin, founder of Blossom HR Consulting. “Currently there is very little in our law about AI — even the Employment Rights Bill, dubbed the ‘biggest shift in employment legislation for decades,’ doesn’t include a single mention of it.”

But while the penalties for the misuse of AI in hiring could hit up to 7% of an organization’s global annual revenue, the risks of non-compliance aren’t just financial or reputational. The new laws also signal a broader shift towards how organizations are expected to manage trust and employee rights in their hiring process. 

“Within the next year, I think we’ll see employment tribunals stemming directly from unethical use of AI in organisations,” Brunwin noted. “Trade unions are already drafting bills to protect employees’ rights in terms of their data, surveillance, and job security.”

At this point, compliance isn’t optional. But governance — and building the structures that make AI use fair, transparent, and proactive — will be what separates talent teams that merely meet legal requirements from those who lead.

Turning AI governance into a competitive advantage: 5 key pillars, and what TA teams can do next

AI governance is a system of rules, practices, and processes that help companies make sure their implementation of AI aligns both with legal requirements and ethical standards, as well as their organization’s strategy, values, and objectives. 

As such, it spans the entirety of people, process, and tech infrastructure — from data and explainability to internal policy, company culture, and social impact. 

In talent, there are five key pillars that shape how teams think about AI from a process perspective and deploy it across the hiring lifecycle.

1

Compliance and ethical alignment

The legal landscape for what constitutes compliant organizational implementation of AI in hiring is rapidly evolving — and practices that were fine last year may not be down the line.

TA teams don’t need to be legal experts — but they do need to have an understanding of the regulatory environment they’re operating in, and maintain an active dialogue with legal and compliance to ensure processes stay watertight.

This goes for vendor conversations too — where teams will need to ensure any new and existing tooling meets legal and ethical standards across every operating location. This could mean asking for validation studies or certification such as SOC II compliance to ensure tooling has passed certain checks or standards.

Compliance and ethics governance should also include a little more introspective enquiry as to where your ethical standards lie as an employer — evaluating where using AI could add barriers, unintentionally discriminate, or create a hiring process that falls short of your brand.

What you need to do: Align AI practices with legal requirements and ethical standards.

  • Collaborate with legal and compliance to understand how well existing practices align with evolving laws across all of your hiring locations.
  • Set out ethical standards that outline how you as an employer will discern where and when AI gets used. Ask:
    • Do our current AI-enabled processes align with our company values and the employer brand we want to externalize?
    • Could they create unfair barriers or unintentionally discriminate against different groups of candidates?
    • How comfortable are we with how this tool makes decisions?
  • Conduct an ethical audit of your existing tools to identify whether or not they align with current standards.
  • Create a vendor questionnaire that requests documentation on compliance, adherence to regulations, and impact studies.

2

Transparency and explainability

AI should never have full power to influence who gets hired. But it can shape how hiring decisions get made, and it’s this tricky tension that talent teams will need to navigate successfully if they want to build trust in their hiring process.

Talent teams must also provide full transparency on how they augment human decision-making with AI — especially at critical process milestones like resume screening or assessment. But they also need to be able to understand and explain this logic to candidates — including how and where AI is used, what data systems use, h

What you need to do: Build transparency and explainability into processes by default.

  • Vet all tooling and vendors for explainability, asking each to outline the logic behind how each tool gets to its decisions.
  • Make all talent processes transparent by default, proactively outlining where and when AI-assisted tooling is used, and how it augments your team, in all candidate comms and employer branding.

Map where AI tooling adds value across your hiring process and identify opportunities for deployment — as well as where it doesn’t add value.

3

Accountability

AI systems are often complex and opaque — which makes it hard to determine who’s responsible when things go wrong. Defining accountability is TA’s safeguard, making sure that teams have a clear throughline of ownership over AI tools and processes, so that decisions are transparent, compliant, and — most importantly — human-led.

In practical terms, this means assigning clear roles within the TA team that oversees how AI tooling is used, managed, and audited. But it also means ensuring that there are clear mechanisms for reporting when employees, candidates, or other stakeholders spot problems, or have concerns.

Accountability also involves education and documentation, including training TA team members on how the systems work and maintaining audit trails that document AI-augmented decisions.

What you need to do: Create clear ownership and oversight structures.

  • Define clear owners within the team for overseeing TA processes across all stages of hiring — including tool selection, oversight and management, auditing, and who has the ability to pause or discontinue when tools present problems.
  • Create anonymous and non-anonymous reporting mechanisms for emerging concerns or complaints.
  • Train all team members on how any AI-enabled systems work and their limitations.

4

Privacy and security

In tech- and AI-driven hiring processes, candidates often have little sense of how much data they’re giving away — or how it will be used, stored, or accessed. And when security and privacy protocols aren’t defined, this can be catastrophic.

In 2025, an ATS provider exposed over 26 million resumes during a breach, revealing not just employment histories, but sensitive personal information that candidates had shared in confidence. It highlighted that data privacy and security policy are a critical foundation of any AI governance plan.

TA teams must work with IT, CDOs and CIOs to map what data is collected, where it’s stored, and where data flows across systems — as well as how any vendors comply with regional data laws such as GDPR or CCPA.

What you need to do: Map your data security and privacy strengths and weaknesses — inside and out.

  • Assess vendors for their data security standards. Ask:
    • How does your tool use and store employee and candidate data?
    • Do you use customer data to train your model? Can we opt out?
    • Who can see and access data — and how can we control access?
    • What happens to our data if we cancel our contract? 
  • Audit your own tech stack and existing systems to identify how candidate and TA data flows across your organization. Identify where data has gaps or missing information that could lead to AI errors or bias.
  • Understand and train TA teams on candidates’ rights

5

Bias and fairness

One of the biggest worries about AI implementation in TA processes is bias. Because while we might think of AI as a neutral force — it was made by humans, and humans are inherently biased. And we only need to look at some of the first unintentionally biased attempts at AI hiring tools to see the impact of this in the real world. 

For TA teams, this means interrogating all existing and future tooling — not just at the point of implementation, but continuously throughout its lifecycle. Teams need to have a basic understanding of how models are trained, ask vendors for transparency on their datasets and bias mitigation procedures, and run audits on new vendors to establish any emerging risks.

They should also focus on how this works in their own procedures, setting policy around practices like resume anonymization and demographic data collection to ensure internal processes meet fair hiring standards.

What you need to do: Audit AI hiring tools and processes for bias.

  • Track hiring outcomes, segmenting by demographic group at each stage of your funnel to cross-reference drop-out patterns where AI tooling may be implicated.
  • Ask vendors for how they train their models, and how they mitigate bias within their datasets.
  • Design for accessibility from the get-go, and make sure AI-enabled tooling doesn’t create barriers for candidates from different groups.

Bridging the governance gap

AI governance in talent acquisition processes isn’t a new form of red tape — it’s a reflection of how your organization values its people. Being upfront and transparent on how AI factors into your hiring process, and how it could impact decision-making is essential for building trust in your process and employer brand.

But beyond that, effective TA-AI governance requires the right infrastructure, expertise, and partner. Many TA teams find themselves stuck between chasing the promise of AI-driven efficiency, and getting mired in compliance and legal requirements. 

The reality is that for organizations to capitalize on AI, both need to exist simultaneously. This is why the talent teams implementing AI successfully aren’t necessarily the first ones out of the gate to automate their processes — they’re the ones spending time on building thoughtful governance that helps them scale sustainably.

Because when it comes to AI in hiring, governance isn’t just about avoiding risk — it’s a chance to lead with confidence. At Talentful, we’ve embedded with some of the world’s most ambitious deeptech and AI-native companies, building responsible hiring infrastructure from the inside out. We know that when governance is done right, it doesn’t slow you down — it gives you a foundation to scale smarter, move faster, and build trust that lasts.


Ready to bridge your AI governance gap? Speak to our team.