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The advent of AI-powered applications brings unparalleled efficiency and innovation, yet simultaneously ushers in a new landscape of security vulnerabilities. These threats stem from external malicious actors, inadvertently arise from employees using unsanctioned tools, or even within the technology itself.
For senior IT and finance leaders in multi-location enterprises, the challenge isn't deciding whether or not to adopt AI technology. It’s determining how to integrate AI applications without exposing the organization to unacceptable or unnecessary risk.
The answer lies in leveraging future-ready AI security strategies that enable teams to harness AI’s power safely and sustainably. This article explores such strategies, unpacking each one in detail and offering actionable implementation insights.
How Can Businesses Protect Against AI Vulnerabilities?
Enterprises must move beyond traditional perimeter-based security to a more dynamic, data-centric approach—one that keeps pace with the evolution of AI itself.
This approach requires leaders to embed adaptive security measures throughout the organization’s entire framework, at every stage of the technology lifecycle. By cultivating agile end-to-end protection, from procurement to everyday use, enterprises protect their data while simultaneously empowering their people.
Teams can execute this future-ready vision by implementing six straightforward AI security strategies that unlock AI’s massive potential while minimizing its risks.
Let’s take a closer look at each tactic.
Establish Clear AI Governance and Acceptable Use Policies
Cultivating concise, well-defined AI governance and acceptable use policies is the foundational step for any effective AI security strategy. It helps leaders manage tech lifecycles effectively, support innovation, and safeguard sensitive data.
Given the ethical concerns that accompany the use of AI, there must be guardrails in place that protect companies against intangible threats as well. A secure enterprise infrastructure won’t mean much if you’re operating with biased information and lack transparent decision-making.
Establish a practical and scalable framework that keeps AI in check with the following steps:
1. Identify Approved Applications
Approved tools are identified by defining core criteria and continuously updating a registry of authorized software. This collaborative effort involves IT, compliance, and strategy teams working together to ensure all sanctioned tools fulfill security and performance standards.
2. Create Clear Guides for Data Input
Formulating easy-to-follow rules is key. Examples include:
- Never interact through unsanctioned tools
- Never put proprietary data or Personally Identifiable Information (PII) into public large language models (LLMs)
- Always use encrypted channels
- Always standardize input formatting
3. Outline Processes For Requesting And Adopting New Tech
Establish seamless, secure workflows by creating intake forms, request protocols, and standardized testing processes. This step optimizes operations, enables accurate risk assessment, and streamlines technology implementation.
Governance protocols are the first line of defense against shadow IT, or the unauthorized use of IT resources that can lead to accidental data exposure. IBM found that a third of data breaches in 2024 involved shadow data, underscoring the significant security and compliance risks of shadow IT.
By establishing transparent governance, organizations can reduce this liability and proactively prevent unsanctioned tool usage.
Thoroughly Assess AI Security Risks During Procurement
As AI systems often rely on external providers, they present serious supply chain risks. One example is model poisoning, where adversaries intentionally tamper with training data to degrade performance or compromise system integrity.
Other vulnerabilities that enable internal system infiltration through third-party providers are:
- Insider AI cybersecurity threats
- Misconfigured APIs
- Mismanaged dependencies
- Weak access controls
- Inadequate patch management
To effectively mitigate these vulnerabilities, enterprises must rigorously assess vendor security during the procurement process. Leaders should consider:
- Overall security posture
- Data handling, privacy processes, and compliance
- Data residency
- Model integrity
- Incident response and other Service Level Agreement (SLA) terms
By accurately evaluating the security risks associated with AI tools, enterprises avoid costly procurement mistakes and safeguard their internal systems.
Gain Visibility And Control Over AI Applications
Enterprises can't protect what they can't see, which makes augmenting visibility one of the most crucial AI security strategies. Methods for enhancing oversight include:
- Conducting audits
- Mapping data flows
- Implementing extensive monitoring protocols
Visibility alone isn’t enough. Gaining control over AI applications is just as critical to reducing risk exposure. Teams must take command of AI operations with:
- Usage enforcement mechanisms
- Automated blocking protocols
- Robust access controls
The ultimate goals of this AI security strategy are to develop full situational AI awareness, enforce governance policies, and centralize security management.
Secure Enterprise Data With A Zero-Trust Mindset
The “zero-trust mindset” is an overarching approach that assumes every access attempt could be a threat and follows the core principle: never trust, always verify. It leverages rigorous access controls and Zero Trust Architecture (ZTA)—a critical foundation for securing AI.
Implementing zero-trust security limits an attacker’s ability to move laterally across the network and access sensitive data, even when approved tools are compromised.
To prevent unauthorized data exposure with a zero-trust strategy, leverage the following:
- Micro-segmentation
- Robust identity and access management (IAM)
- Policy-based enforcement protocols
- Regular permissions reassessments
- Advanced threat detection
- Continuous auditing
Implement Continuous Monitoring And Threat Modeling
As AI is rapidly evolving and AI-related security risks continue to threaten enterprises, AI security is an ongoing process that demands a dynamic approach. Cisco’s 2025 Index highlights this reality, revealing that 86% of business leaders with cybersecurity responsibilities experienced at least one AI-related incident within the past year.
These contexts make continuous monitoring, or using security tools to consistently observe AI applications and scan for anomalous behavior, essential. They also necessitate threat modeling, a risk assessment tactic for uncovering and preparing defenses against potential vulnerabilities within AI implementations.
Educate Employees on Safe AI Practices
The human element remains the most critical—and often most vulnerable—part of the AI security chain. Therefore, teaching employees how to use AI safely and strategically helps enterprises close massive security gaps.
Leaders should educate their internal teams about the dangers of sharing sensitive information with public AI programs and engaging in shadow IT.
They should also implement training initiatives enabling employees to:
- Identify AI-generated phishing emails and AI-manipulated content
- Follow company AI policies
- Manage AI-generated data per compliance standards
- Adhere strictly to data privacy standards
- Notice and report potential AI-related security issues
Refer to national recommendations, such as the NIST's AI Risk Management Framework, for additional guidance on safe AI usage.
Conclusion: Build A Secure Framework For AI Innovation
While AI has incredible transformative potential and impactful benefits, it also introduces major risks that demand proactive AI security strategies.
Practical tactics include establishing governance policies, adopting a zero-trust mindset, enhancing visibility, and educating users.
Effectively implementing these strategies is a massive challenge for multi-location enterprises. Cultivating the defenses necessary to mitigate AI cybersecurity risks is even more difficult for small in-house teams with limited resources.
At Advantage, we assist you from start to finish — planning, procuring, implementing and maintaining robust enterprise networks that meet the demands of an AI-prevalent world.
Ready to ensure your AI adoption is innovative and highly secure? Contact the experts at Advantage for a comprehensive review of your connectivity infrastructure.