AI Agents Are Spreading Fast — Is Your Governance Ready?

Agentic AI (or AI agents) are systems that take multi‑step actions across apps and services—drafting contracts, executing purchases, updating customer records, or triaging support tickets—without constant human direction. They power a new wave of AI automation for business, but their ability to act across systems raises legal, financial and security stakes that simple chatbots never had.

What this means for leaders

• Adopt fast, govern faster: Adoption is accelerating; governance must be intentional or value will be lost to errors and incidents.
• Prioritize high‑risk touchpoints: Payments, contracts and customer data need human approval gates now, not later.
• Mix vendor features with internal controls: Don’t outsource trust—combine vendor safety defaults with your own monitoring and audit trails.

How fast adoption is moving — and where governance stands

A recent Deloitte enterprise survey of more than 3,200 business leaders across 24 countries found roughly 23% of companies report using AI agents at least moderately today, and about 74% expect to use them within two years. At the same time, only about 21% of respondents say they have robust safety or oversight mechanisms for agentic AI—creating a clear adoption‑versus‑governance gap.

Other studies show the same pattern: many organizations and employees are running AI tools with little formal policy or training. That mix—rapid commercial push from major vendors, strong productivity promises, and uneven internal controls—creates a brittle environment as agents move from suggestions to actions.

What AI agents actually do (and why it matters)

Agents don’t just generate text. They chain actions across systems. Typical agent capabilities being marketed to businesses include:

• Orchestrating CRM workflows—creating opportunities, updating records, and nudging sales reps.
• Approving or initiating purchases and invoices across procurement systems.
• Drafting, negotiating and even executing contract signatures under delegated authority.
• Handling customer interactions that may access or reveal personal data.

Small automation mistakes at these touchpoints scale quickly. Three short, illustrative examples:

• Sales agent discount leak: An agent auto‑approves a cumulative set of discounts to hit a quota, eroding margins before finance detects the pattern.
• Procurement fraud via prompt injection: A malicious input tricks an agent into changing a vendor’s payment details, and an unattended auto‑pay transfers funds to a fraudster.
• Support bot and PII exposure: A customer service agent pulls sensitive personal data into a public thread due to inadequate data filters.

Key risks to watch

Risks fall into three overlapping buckets:

• Operational & financial: Unchecked automation can sign contracts, make payments, or change pricing—creating direct monetary exposure.
• Security: Agents increase attack surface. Prompt injection (where malicious inputs trick an AI into revealing data or taking unsafe actions) is a particularly practical threat when agents operate across systems.
• Legal & accountability: When an agent acts autonomously, who bears liability? Lack of audit trails and unclear decision ownership create regulatory and litigation risk.

A practical governance framework: Decide, Monitor, Audit

A crisp, implementable control set reduces risk without killing velocity. Treat it like three interconnected layers.

1) Decide: define autonomy boundaries and escalation rules

• Classify actions by risk level (informational, recommend, transact). Require human approval for high‑risk classes—payments, contract signatures, account changes.
• Set explicit thresholds (e.g., any discount >5% needs a finance sign‑off; purchases above $X require procurement approval).
• Embed policy into the agent design: least privilege, read‑only where possible, and explicit approval hooks for write actions.

2) Monitor: real‑time detection and anomaly flags

• Feed agent activity into existing security telemetry—SIEM, DLP and IAM systems—so unusual patterns trigger alerts.
• Instrument behavior baselines: monitor action frequency, destination accounts, and unusual data access. Flag deviations for immediate review.
• Run adversarial tests (prompt‑injection drills) and red‑team the agent to validate defenses on a schedule.

3) Audit: immutable logs and clear retention policies

• Capture a complete chain of actions: inputs, decision logic or model outputs, API calls, and human approvals. These are the audit trails regulators and auditors will want to see.
• Store logs tamper‑resistantly and define retention aligned to legal and regulatory needs.
• Use logs for continuous improvement—post‑incident root cause analysis should be fast and evidence‑driven.

Prompt injection — a quick explainer and mitigations

Prompt injection is when an attacker crafts inputs that trick an AI into revealing secrets or taking actions it shouldn’t. When agents can call services or authorize payments, these attacks become more dangerous.

Practical mitigations (high level):

• Input validation and sanitization before forwarding content to an agent.
• System‑level instructions and hardened prompts that ignore unexpected user content for sensitive actions.
• Sandboxing external data sources and verifying outputs against authoritative systems before executing write operations.
• Human‑in‑the‑loop checks on any action that changes money, legal terms, or critical system configurations.

Vendor safety features vs. internal controls

Vendors such as OpenAI, Microsoft, Google, Amazon and Salesforce ship safety defaults—rate limits, role‑based access, model‑level guardrails and content filters. Those matter, but they are not a substitute for internal governance.

• Vendor features are useful for baseline protections (RBAC, system prompts, data handling assurances).
• Internal controls tie agent actions to your organizational context: approval workflows, finance thresholds, legal sign‑offs, retention policies and SIEM integration.
• Combine both: use vendor safety where it fits, but instrument agent activity in your own logging and monitoring stack so you own the forensics and escalation paths.

Who should own governance? A practical RACI example

Governance is best cross‑functional. Centralize policy and coordinate execution with clear RACI assignments. Example for the workflow “allow an agent to sign a contract”:

• Responsible (R): Product/Engineering — implement the agent, approval hooks and logging.
• Accountable (A): General Counsel or Chief Risk Officer — final sign‑off on legal/contractual risk.
• Consulted (C): Finance, Security, Compliance — provide risk thresholds, fraud controls, and security requirements.
• Informed (I): Business Unit Heads, Internal Audit — kept in the loop on approvals and incidents.

Assign a central AI governance owner (could be a CISO, CRO, or an AI governance lead) to coordinate the RACI and keep policies current as agent capabilities evolve.

Prioritized checklist for C‑suite leaders

1. Inventory current agent usage: Where are agents deployed? Which systems do they touch? Who approved them?
2. Map high‑impact touchpoints: Identify payments, contracts, customer PII, and admin‑level operations as top priorities.
3. Set clear autonomy rules: Define what agents can do without human approval and what always needs approval.
4. Integrate monitoring: Send agent telemetry to SIEM/DLP and define anomaly alerts.
5. Require immutable audit trails: Log inputs, outputs, API calls, and human approvals with retention aligned to legal needs.
6. Vendor assessment: Verify vendor features—RBAC, audit logs, data handling—and ensure they meet your policies.
7. Train employees: Provide role‑specific AI safety and incident response training for staff using or supervising agents.
8. Run red teams: Test for prompt injection and other adversarial scenarios before broad rollout.
9. Legal review: Get General Counsel involved before granting agents authority to sign or transact.
10. Measure and iterate: Use post‑incidents and routine reviews to tighten rules and close gaps.

Quick wins and high‑risk paths

Quick wins: Add approval gates for large transactions, feed agent logs into your SIEM, and require legal sign‑off for any agent that can execute contracts.

High‑risk paths to prioritize: Payments and vendor changes, contract signing, privileged account modifications, and any agent access to customer personal data.

Deloitte warns that rapid adoption of agents without matching safeguards could limit value capture and increase risk.

Adoption of AI agents will only accelerate. The choice facing leaders isn’t whether to use agents—it’s whether to capture the productivity gains while containing the risks. Clear autonomy boundaries, real‑time monitoring, and comprehensive audit trails are not optional; they are the plumbing that lets organizations scale AI automation for business without plumbing catastrophes.

Next step: Start with an inventory, lock down your top three high‑risk workflows, and require human approval for any agent action that changes money, legal terms, or customer privacy. If you want a tailored one‑page checklist for your industry (finance, healthcare, retail, or SaaS), prepare the list of workflows and I’ll outline the prioritized controls to apply first.