When AI systems handle sensitive data, drive important decisions, or power customer experiences, security becomes absolutely essential. Companies that overlook the implementation of robust AI security and fail to use secure AI tools can face steep consequences: amongst them, regulatory penalties, reputation damage, and financial losses from breaches or model exploitation.

This is precisely why leading organizations integrate comprehensive security measures into their AI strategy from the outset, treating it as a foundational element rather than a final checkbox.

What is secure AI for business and why is it important?

Secure AI means protecting artificial intelligence systems at every stage, from initial design to everyday operation. These protections help to shield enterprise AI from threats like deliberate manipulation, unauthorized use, and data breaches.

What makes securing AI uniquely challenging is its dynamic nature. Unlike traditional software that remains relatively static, AI platforms constantly evolve as they process new information, creating security challenges that shift over time. For businesses deploying these technologies, robust yet flexible security is critical protection for the sensitive data and decision-making processes that drive core business operations.

Secure AI: What to watch out for

AI systems are vulnerable to a range of threats that can degrade performance, expose sensitive information, or enable attackers to manipulate outputs. Here are the critical risks that should be on every business leader's radar.

Data poisoning

Attackers deliberately contaminate training datasets by inserting incorrect, biased, or harmful examples. For example, a fraud detection system trained on poisoned data might fail to identify actual fraudulent activities because it was taught to classify suspicious transactions as legitimate. These vulnerabilities are particularly insidious because they're embedded deep within the model's learning foundation.

Prompt injections (direct and indirect)

These attacks target large language models by exploiting how they process instructions. In direct injections, harmful commands are cleverly embedded within seemingly innocent inputs. Indirect injections are even more sophisticated, hiding malicious instructions in external references (such as URLs or documents) that are accessed through integrated systems like retrieval-augmented generation (RAG) pipelines or plugins. These vulnerabilities represent one of the most significant challenges in LLM security, making secure generative AI a priority for organizations deploying these technologies.

Emerging risks

Novel threats are emerging across the AI supply chain. It’s particularly concerning that as increasingly autonomous AI systems are introducing expanded attack surfaces, which brings more opportunities for attackers to exploit them.

For instance, imagine an AI agent designed to access multiple business systems (such as customer databases, financial records, and operational controls) to perform legitimate automation tasks. If an attacker compromises these secure AI agents, they could use existing permissions to move freely between otherwise separate systems. This turns what might have been an isolated breach into a widespread security incident affecting multiple parts of the organization simultaneously.

Understanding these threats is the first step in learning how to secure AI systems. The next section explores how businesses can address them through practical solutions and industry-backed frameworks.

Securing AI: solutions and frameworks for businesses

To properly secure AI systems, you need multiple layers of protection: strong processes, clear governance rules, and practical technical safeguards. The good news is you don't have to build this security architecture from scratch; reputable organizations have created starting blocks already.

Below are some proven frameworks that organizations of any size can look to adapt to their own unique needs.

ISO/IEC 42001 AI Management System

Think of this international standard as a global playbook for responsible AI governance. Unlike region-specific regulations, ISO/IEC 42001 provides a management system that works across international boundaries. This makes it invaluable for multinational companies that need consistent AI oversight across offices operating under different legal and regulatory frameworks.

NIST AI Risk Management Framework (AI RMF)

This practical framework from the U.S. National Institute of Standards and Technology breaks AI risk management into four interconnected and iterative steps:

Map (identify context)
Measure (assess risk)
Manage (respond to risk)
Govern (monitor and improve)

It provides a functional structure to evaluate AI risks without prescribing one-size-fits-all solutions. Healthcare and financial institutions find it especially useful because it emphasizes transparency and accountability, helping them address, for example, specific security in healthcare AI requirements and meet their strict regulatory obligations.

Secure AI lifecycle management

Security is a continuous process spanning your AI system's entire lifespan. Effective lifecycle management treats security as essential at every stage: when collecting training data, building models, deploying solutions, through regular usage and eventually retiring outdated systems. In practice, this means implementing strict data governance during development (like controlling who can access training datasets), and maintaining vigilant monitoring after deployment to catch any performance drift or potential misuse before it becomes problematic. Even retired systems can be vulnerable to attacks, and good lifecycle management should ideally include secure decommissioning or model disposal procedures.

For a review of Cohere’s approach to managing and mitigating AI risks throughout the development lifecycle, please refer to our Cohere Secure Model Framework.

AI security audits and red teaming

Through structured security audits, organizations can systematically evaluate how well their AI systems resist various threats. Red teaming is a good example of this approach: Dedicated teams roleplay as attackers, actively trying to break or manipulate AI systems to find weaknesses before real adversaries do. This process can uncover vulnerabilities that standard quality testing often miss, especially those related to how systems respond to unexpected or malicious inputs.

AI incident response and threat monitoring

Even the best defenses can be breached, so preparation is essential. Any effective incident response combines real-time monitoring systems that flag unusual AI behavior, logging that enables investigation when something goes wrong, and detailed response plans designed specifically for AI-related incidents like prompt injections or model inversion attempts. Industries managing important infrastructure, like manufacturing and utilities, need these specialized monitoring capabilities integrated with their broader security operations.

Secure AI best practices for enterprises

Building and maintaining truly secure AI demands cultural change and strategic investment in people, processes, and policies. Here are some proven approaches that can help organizations to create AI systems that are both secure and aligned with business objectives.

Adopt a holistic approach to AI security

Effective AI security combines diverse expertise across security professionals, engineers, data scientists, legal counsel, and compliance officers, all working in concert on policy and practical implementation. Rather than treating security as a final checkbox before launch, this approach integrates security as a fundamental ongoing component of your AI development and operations process.

A real-world application of this collaborative model is the AI governance committee. These cross-functional teams meet regularly to evaluate model performance, review security incidents, and assess emerging threats. They can develop tools such as security assessment frameworks for pre-deployment evaluation, incident response plans tailored to AI-specific threats, and protocols that integrate security considerations from the earliest design phases instead of bolting them on later.

Implement clear policies, training, and governance

Document your expectations for secure AI development. Well-crafted internal policies should serve as guidelines for secure AI development covering the entire AI lifecycle: from data collection standards to model development practices, deployment requirements, and acceptable use guidelines. Back these policies with targeted training programs that help your team understand risks and their responsibilities. Regular training sessions on secure data handling, model behavior patterns, and ethical AI implementation build a culture where security becomes everyone's responsibility.

Establish strong internal controls and deployment guidelines

Put technical guardrails in place to prevent security lapses—including access control systems limiting who can modify critical AI components, version control protocols that track changes, and formal sign-off requirements before production deployment. Every model and component should undergo a thorough security review before going live.

After deployment, maintain vigilance through processes including automated monitoring dashboards that track performance metrics (these should be carefully configured to capture security-relevant anomalies), comprehensive audit logs that record all system interactions, and ready-to-activate rollback capabilities that can restore earlier versions if problems emerge.

Prioritize proactive risk assessment

Regularly probe your AI systems for weaknesses before problems occur. Conduct thorough vulnerability testing before deployment, then continue monitoring for model drift, bias, and anomalous behavior once systems go live. Build security checkpoints directly into your development sprints and quarterly business reviews to ensure security considerations never slip through the cracks.

Promote cross-functional collaboration

Effective AI security demands input from multiple perspectives: security teams understand threats, engineers know system architecture, legal counsel navigates compliance requirements, and data scientists recognize model vulnerabilities. Form dedicated cross-functional teams to address emerging threats collectively, leveraging this combined expertise to stay ahead of evolving risks.

Ensure transparent model use and documentation

Document everything about your AI systems, including training methodologies, system configurations and output generation processes. This transparency serves multiple purposes: it enables effective internal audits, provides critical context when troubleshooting issues, and satisfies compliance requirements in regulated industries, among others. Clear documentation helps to transform AI systems from black boxes into accountable tools.

As AI becomes central to your business strategy, security must be woven into its very fabric. Forward-thinking organizations now embed security-by-design principles throughout their AI development lifecycle. When effective governance combines diverse expertise and clear transparency, secure AI can deliver concrete advantages: protected data, preserved customer trust, and the confidence to innovate responsibly.

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Secure AI: How to safeguard your AI systems