Matthew Solé, founder and CEO of Zeal, is transforming enterprise contract lifecycle management through AI and automation.gettyAI is now deeply integrated into daily organizational operations. Companies have rapidly adopted multiple tools for automation, analysis and document review, often granting broad permissions without fully understanding their implications. This race for efficiency has created very high-risk environments in which numerous third-party tools access, process and sometimes train on proprietary company data.Cautionary tales are on the rise. This year, for example, a misconfigured AI agent deleted PocketOS's entire database within seconds, demonstrating the risks of autonomous tools with unchecked permissions and the level of trust organizations place in such AI.Contracts are often overlooked in this discussion, even though they contain highly sensitive company information. The key issue is not whether AI is involved in your contract workflow, but whether you understand what you have authorized it to do.Why Organizations Keep Outsourcing This ProblemThe legal workload in organizations has outpaced the capacity of legal teams. Often, only legal departments and general counsel recognize this challenge. Tasks such as contract review, renewal tracking, compliance checks and obligation management are increasingly assigned to employees without formal legal training. For example, sales teams at staffing firms focus on closing deals rather than reviewing contract terms.Contract management platforms address this gap by helping legal teams reduce turnaround times, identify risks and make contract analysis accessible without requiring legal expertise from every employee.However, the widespread adoption of these tools has created new risks that many organizations have not fully assessed. These platforms operate under their own contracts and data usage terms, which are seldom audited as rigorously as internal AI policies.Not every contract manager needs technical expertise, but someone in the organization must understand what these tools are authorized to do with company data, including how they process, extract and retain information.​Where Vendor Contracts Expose YouAI in contracts poses four key risks to organizations, none of which require a data breach to affect them.The first risk is errors and misplaced reliance. AI tools for contract analysis are not always accurate; they may hallucinate, overlook obligations or misclassify risks. This can create significant legal liability, especially when nonlegal staff act on AI outputs without review. Vendors rarely assume responsibility for these errors and such liabilities often go unaddressed.The second risk involves interconnected third parties. Most enterprise software vendors rely on subprocessors, API partners and model providers. A contract with one vendor may implicitly grant data access to several others. Organizations seldom track this chain, and vendor agreements often obscure these relationships.The third risk is data protection and confidentiality. Varonis's 2025 State of Data Security report found that 99% of organizations have sensitive data exposed to AI tools. Vendor contracts often permit data processing beyond the original use case, with permissions hidden in the fine print. As a result, exposure extends to every document processed by these tools.The fourth is compliance drift. SOC 2, GDPR, HIPAA and CCPA each impose specific requirements. Vendor contracts signed before recent regulatory updates or without legal review of AI provisions can result in unnoticed noncompliance. Worker records, client contracts, placement agreements, rate cards and non-solicitation clauses all move through contract workflows—often at high volume and under significant time pressure. Turnaround speed is a competitive advantage in staffing. However, permission review tends to get compressed.This increases overall exposure. The tools handling these volumes may ingest placement data, candidate records and client terms for model training or third-party analytics, depending on vendor permissions. Many AI tools used by staffing firms were not designed for contract analysis, so confidentiality is not guaranteed, audit trails are lacking, and there may be no contractual limits on downstream use.AI does improve contract workflow efficiency. However, prioritizing speed over permission review shifts liability into the agreement itself, making risks less visible and more difficult to address.What A Contract Audit Actually Looks LikeAn AI vendor contract audit doesn't require an overhaul of the legal department. It involves a structured review of specific clause categories within current vendor agreements. Explicitly authorized to do with your data, including model training, sublicensing and downstream resale. Subprocessor lists identify who else has access, under what terms and whether those parties are bound by equivalent confidentiality obligations. AI-specific disclosures, which are increasingly common in newer agreements, address whether the vendor uses AI, which models it uses and what data flows through them. Liability and indemnification clauses determine who is responsible in the event of a data breach, model error or compliance violation.The Clause You Didn't NegotiateAI governance maturity requires more than policy documents and approved tool lists. It involves understanding what your vendor contracts actually permit, beyond what is presented in sales materials.The next major AI-related legal or compliance issue for a staffing firm is more likely to result from a vendor agreement that authorized actions no one fully understood at the time of signing. Contracts negotiated before generative AI became a priority often lack the protections now considered standard, as these requirements were rarely requested. With that said, if AI is part of your legal workflow, your next audit might as well already be signed.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?