Nine trends defining AI governance in 2025

Last week, I spoke about my predictions for 2026, but they were based on patterns across regulatory developments, enterprise deployments, and legal precedents from the past year. Nine trends stood out.

Implementing AI governance today is like building a highway system while the cars are already driving at 100 mph. You cannot stop traffic to lay the pavement, so you must build guardrails (audit trails and human-in-the-loop checkpoints) and signs (transparency and nutrition labels) while vehicles are in motion to prevent a systemic pile-up.

The fundamental shift: From tools to actors

We crossed a threshold in 2025. AI systems are no longer just responding to prompts—they're setting goals, planning multi-step tasks, and executing actions autonomously. This transition from deterministic automation to probabilistic autonomy reshapes the entire risk landscape. Agents now book travel, navigate websites, initiate financial transactions. That's not hypothetical. It's happening in production environments today.

When law moves too slowly, organizations build their own rules

Companies aren't waiting for the EU AI Act or NIST frameworks to be finalized. They're building internal governance systems now—Trust Councils with authority to pause releases, escalation protocols that don't exist in any regulation yet, accountability structures that map to how their systems actually work. The autonomy gap is getting filled by internal policy, not external mandates.

Courts are done being patient with AI hallucinations

2025 marked the end of "AI made me do it" as an excuse. Judges moved from modest fines to disqualifying attorneys entirely when they failed to verify AI-assisted filings. Multiple courts found the conduct "tantamount to bad faith." The message is clear: hallucinations aren't technical glitches. They're professional responsibility failures. Attorneys are personally accountable for every word that goes into a filing, regardless of how it was generated.

Security is the crisis no one's talking about enough

Non-human identities—AI agents and API tokens—now outnumber humans 80 to 1 in many organizations. These agents often have broad permissions and static credentials without the onboarding or offboarding protocols we apply to human employees. The result: zero-click vulnerabilities where agents can be tricked into exfiltrating sensitive data via a single malicious email. This is an architectural problem masquerading as a feature deployment.

Legal business models are being rebuilt from scratch

AI-native law firms are emerging with full-stack legal models delivering services at a fraction of legacy firm costs. The billable hour is under pressure it's never faced before. Flat-fee arrangements, proprietary data platforms, outcome-based pricing—these aren't experiments anymore. They're how new entrants are competing. Traditional firms that haven't started this transition are running out of time to begin.

Testing fails for autonomous systems

Traditional software testing doesn't work for agents because their behavior is non-deterministic. The new standard is continuous observability using the MELT framework: Metrics, Events, Logs, and Traces. You can't "test" an agent running a 30-step workflow the way you test a function. You have to review it like a human employee's work—after the fact, with full visibility into what it did and why.

Law schools finally mandate AI fluency

Programs at Yale, Penn, and USF now require students to build models, hunt for hallucinations, and use AI to analyze evidence. The goal isn't teaching students to use AI. It's teaching them to supervise it. Judgment remains a uniquely human responsibility, and legal education is catching up to that reality.

AI adoption is stalling because of identity, not technology

Thirty-one percent of employees admit to sabotaging AI strategies because they fear using the tools makes them appear less competent. This isn't about technical failure. It's about existential threat. People asking "If AI can do this, who am I?" That's a harder problem than any deployment challenge, and most companies haven't begun addressing it.

Specialization is replacing the general-purpose model race

The industry moved past "winner-take-all" foundation model wars toward specialist tools. Companies are focusing on agent skills—folders of composable procedural knowledge that equip a single general agent with deep expertise in accounting, legal research, specific domain work. That's more practical than deploying dozens of fragmented agents, and it's what's actually getting built in 2025.

What this means for legal and product teams

These aren't isolated developments. They're connected. The shift to autonomous systems created the autonomy gap. The autonomy gap drove companies to build internal governance. Internal governance exposed security risks. Security risks intersect with hallucination liability. And throughout, business models and educational requirements adapted to reality faster than regulations could.

The question for your team: which of these trends are you designing for now, and which are you hoping to retrofit later? Because retrofitting governance after deployment costs 10x more than building it in from the start.

This is issue-spotting and strategic analysis, not legal advice for your specific situation.

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