Artificial intelligence tools have transformed how organizations approach productivity, marketing, software development and data analysis. These platforms can improve efficiency and drive innovation, but they also introduce security risks that traditional cybersecurity strategies may not fully address. AI applications often process sensitive information and integrate with enterprise systems, creating new attack surfaces. The lack of proper oversight can expose businesses to data leaks, unauthorized access, compliance violations and cyberthreats.

Why Securing AI Introduces New Business Risks

Unlike conventional business applications, AI platforms frequently require access to proprietary documents, customer information, source code, financial records and internal communications. Employees may unknowingly upload confidential information into generative AI systems or connect AI applications to enterprise resources without proper security reviews. These tools often operate outside of traditional IT infrastructure, creating visibility gaps that make it difficult for security teams to monitor data flows and detect potential breaches.

The number of AI-related incidents continues to trend upward year over year as adoption accelerates. Despite these growing risks, many corporate boards still lack adequate visibility into their organization’s AI governance practices. One study found that only 14% of boards discuss AI oversight at every meeting. This gap between technology adoption and governance oversight leaves organizations vulnerable to threats that can compromise sensitive data and disrupt operations.

Establishing a Strong AI Governance Framework

Establishing clear rules and oversight mechanisms is the first step to securing AI tools. A formal governance framework represents a strategic necessity for organizations managing the unique risks that AI introduces while enabling teams to leverage these tools effectively.

The NIST AI Risk Management Framework provides a voluntary but authoritative guide for organizations developing AI governance strategies. Its core functions, Govern, Map, Measure and Manage, offer a systematic approach to identifying and mitigating AI-related risks. Multiple government bodies have aligned on the need for strategic AI oversight, with CISA’s strategic roadmap reinforcing the importance of coordinated risk management across enterprise environments.

Creating Clear Acceptable Use Policies

Organizations need explicit guidance for employees on how to use AI tools. Effective acceptable use policies define which AI applications are approved for business use, specify acceptable use cases, outline prohibited activities and establish data-handling requirements. Clear guidance helps people make informed decisions about what types of data they can and cannot enter into public AI tools.

Defining Roles and Responsibilities for AI Oversight

Data privacy violations may expose companies to regulatory penalties and erode stakeholder trust. Undisclosed data breaches can cause intellectual property loss, lawsuits and brand devaluation that extend far beyond immediate financial penalties.

As such, assigning specific roles to manage AI risk rather than leaving oversight to individual departments provides consistent governance across business units. An AI review board or dedicated officers can ensure that security considerations remain central to adoption decisions. Without these structures, enterprises face significant operational and reputational consequences.

Auditing and Mapping All AI Systems in Use

Businesses cannot secure what remains unknown. The discovery phase requires finding and inventorying all AI tools currently in use across the company, revealing both sanctioned applications and unauthorized platforms that employees have adopted without IT approval.

Discovering Sanctioned and Unsanctioned AI Tools

Conducting a comprehensive audit helps identify shadow AI that employees use without formal authorization. Not all AI providers offer equivalent security protections, making it essential to evaluate each platform’s approach to information handling. Key evaluation areas include data retention and deletion policies, transparency regarding model training and data usage, encryption practices and security certifications, and compliance standards. These factors determine whether a particular AI tool aligns with enterprise security requirements.

Classifying Data by Sensitivity Level

Creating a classification system helps employees handle different data types using AI tools. Categories such as public, internal and confidential help teams understand which information they can share with platforms and which they must restrict. Role-based access controls further limit who can access sensitive data, reducing the risk of unauthorized disclosure. Additionally, regular permission reviews and immediate access revocation for departing employees prevent security gaps.

Implementing Technical Controls and Solutions

Governance policies establish the foundation for AI security, and technical solutions enforce them and actively protect data. Technology platforms provide the continuous monitoring and automated controls needed to detect threats and prevent breaches in real time.

Continuously Monitoring for Anomalous Behavior

Security tools that learn an organization’s unique digital environment are some of the best solutions for securing AI tools. They can spot subtle signs of AI-related breaches that traditional signature-based systems might miss. For example, Darktrace’s multilayered AI achieves a self-learning state by developing an understanding of normal behavior for every device, user and interaction within an enterprise. This approach allows the solution to detect novel threats without relying on predefined attack patterns.

A case study with Biomerics shows how the company partnered with Darktrace to stop sophisticated email threats that were evading traditional filters. The platform’s Cyber AI Analyst can investigate every alert and determine whether it represents part of a wider cybersecurity incident.

This capability reduces security team workload by escalating only critical incidents, turning 100 potential alerts into just two or three verified threats that require immediate attention. Automating investigation and prioritization delivers both comprehensive threat detection and operational efficiency.

Preventing Data Leaks With Security Platforms

AI tools often process confidential business records, including financial data, customer details, intellectual property and internal communications. Protecting sensitive content requires proactive measures that prevent unauthorized disclosure before it occurs. Best practices include avoiding the entry of confidential or regulated information into public AI tools, limiting data sharing between integrated applications and classifying company records by sensitivity level.

Specialized security solutions have emerged to address AI-specific data protection challenges. Companies use tools like Knostic to prevent leaks by monitoring and controlling what flows into AI applications. These solutions provide visibility into data movement and enforce policies that keep sensitive content within approved boundaries.

Mitigating Risks From Third-Party AI Services

AI tools rarely operate in isolation. They frequently connect with customer relationship management systems, cloud storage solutions, collaboration tools, databases and enterprise applications. Each integration creates another potential attack surface that requires security oversight. Businesses must evaluate both the tool itself and the entire ecosystem of connected services that can access enterprise data.

For example, a case study showed how companies can accelerate AI adoption while maintaining strong data protection. Advisor360 partnered with Harmonic Security to secure third-party AI integrations and infrastructure. This approach allows businesses to leverage AI capabilities without compromising the security controls needed to protect sensitive content across complex technology environments.

Fostering a Long-Term Culture of Secure AI Adoption

Securing AI tools requires ongoing commitment across three key pillars: governance that establishes clear policies and oversight, mapping that identifies all systems and data flows, and technical controls that enforce security requirements and detect threats. Treating AI security as a continuous priority rather than a one-time project allows businesses to balance innovation with responsibility.

As AI capabilities continue to evolve, businesses that build security into their adoption strategies from the start will be better positioned to leverage these tools while protecting the data and systems that drive their operations.