Your AI models just recommended approving a $50,000 fraudulent transaction because someone changed three pixels in an invoice image. Meanwhile, your sales team is unknowingly feeding sensitive customer data into a free ChatGPT tool to speed up their reporting. Sound familiar?
AI security isn't just another IT checkbox; it's what stands between your competitive advantage and a costly breach that could derail your entire AI strategy. This guide shows you exactly how to protect your AI systems and data without slowing down progress, plus the frameworks and tools that actually work in practice.
What is AI security?
AI security is a dual approach that uses artificial intelligence to defend against cyber threats while also protecting AI systems from being attacked. As you integrate AI into your business operations, you create new pathways for data breaches if these systems aren't properly secured.
Here's what AI security looks like in practice:
Strengthening your cyber defenses: AI-powered security tools analyze billions of events in real time to spot suspicious activity that human analysts might miss
Securing your AI systems: Your AI models become valuable targets vulnerable to unique attacks like data poisoning or manipulation
Connecting governance to intelligence: Strong data governance ensures you know exactly who can access your data and how AI systems use it
When you choose an AI security platform built with governance in mind, like Spotter, you get transparent, explainable answers from your data because security and governance are foundational, not afterthoughts. This AI analyst provides contextual insights while maintaining strict access controls and audit trails, so you can trust both the process and the results.
Why AI security actually matters to you
Putting off AI security isn't a calculated risk. Ignoring AI-specific controls leaves your systems exposed to leaks, breaches, and attacks that can disrupt operations, damage trust, and slow your progress.
The consequences of this gap affect you in three ways:
Immediate financial impact: Regulatory fines, operational downtime, and breach remediation costs
Trust erosion: AI-related breaches feel more invasive and unpredictable to customers than traditional data breaches
Competitive disadvantage: Peers who securely adopt AI operate with more speed and confidence
As Ashwin Sinha, Chief Data Officer at Macquarie Bank, notes in a recent Data Chief podcast episode,
"There is always a big backlog in most organizations, which you cannot get done just because you do not have enough capacity."
An AI-related security incident can derail your roadmap for months, turning a manageable backlog into a crisis.
What are the top AI security risks?
1. Data poisoning and training data attacks
Data poisoning occurs when attackers secretly feed your AI model corrupted data during its training phase. The goal is to teach the AI to make specific mistakes later, like misclassifying legitimate transactions as fraudulent.
2. Prompt injection vulnerabilities
Think of prompt injection as social engineering designed specifically for AI systems. An attacker crafts a deceptive prompt that tricks the AI into ignoring its original instructions and performing harmful actions instead. A cleverly worded customer support query could make your chatbot leak sensitive pricing data or internal information.
3. Model manipulation and theft
Your trained AI models represent valuable intellectual property that can be stolen or manipulated through adversarial attacks. Attackers make tiny, often invisible changes to input data that cause the AI to make major errors. Your fraud detection model might approve a fake invoice because a few pixels in the image were strategically altered.
4. Shadow AI proliferating in your organization
Shadow AI refers to employees using unauthorized public AI tools for work tasks, often with good intentions to increase productivity. When your team members paste sensitive company data into these free tools, that information is no longer under your control, creating massive security blind spots.
💡 Tip: Provide a secure alternative.<br>Instead of banning all external tools, offer a governed platform that gives your teams the power they need without the risk. Platforms like ThoughtSpot Embedded let your teams access analytics inside the applications they already use.
Take Act-On for example. Burdened by scattered customer data and clunky, slow-to-load reports, their teams struggled to deliver secure, customizable insights. But once they embedded ThoughtSpot directly into their SaaS platform, the shift was immediate: monthly customer report usage jumped 60% while keeping sensitive data safely under wraps.
How to implement AI security controls that work
1. Establish comprehensive data governance
You can't protect what you don't understand. Start with strong data governance. This means creating clear, enforceable rules for how your data is handled, who can access it, and for what purpose.
Key actions for implementing AI in data security include:
Data classification: Identify your most sensitive data so you can apply the strictest protections
Access policies: Define roles and permissions to ensure people only see the data they absolutely need
Usage monitoring: Keep an audit trail of how data flows into and out of your AI models
2. Deploy continuous monitoring systems
Think of this as a security camera for your AI systems, constantly watching for abnormal behavior. These systems track key signals that could indicate an attack is underway, including unusual query patterns, spikes in data access from single users, or sudden drops in model accuracy.
3. Implement granular access controls
Basic passwords aren't enough when artificial intelligence security is at stake. Granular access controls make sure that even authorized users can't access data they don't need for their specific role. This includes multi-factor authentication (MFA), role-based access control (RBAC), and always applying the principle of least privilege.
Modern platforms like ThoughtSpot Analytics demonstrate how security can be built into every interaction. The platform provides row-level and column-level security controls that automatically enforce your access policies, whether users are exploring data through natural language search or viewing pre-built Liveboards. This means your sales team can analyze customer trends without seeing individual customer details, while your finance team gets the granular data they need for compliance reporting.
How to choose AI security frameworks that work
1. NIST AI Risk Management Framework
The National Institute of Standards and Technology (NIST) AI Risk Management Framework is widely considered the gold standard for AI security frameworks. It provides a flexible structure that helps you think through AI risks systematically through four core components:
Govern: Create a culture of risk management and accountability across your organization
Map: Identify the context and risks for each of your AI systems
Measure: Use qualitative and quantitative methods to analyze and track AI risks over time
Manage: Allocate resources to treat identified risks and monitor their effectiveness
2. Zero-trust architecture for AI systems
The zero trust model operates on a simple principle: never trust, always verify. Applied to AI, this means you assume every user and query could be a threat until proven otherwise. This approach requires you to verify every request, design systems to limit the "blast radius" of potential breaches, and segment your networks to prevent attackers from moving freely.
3. Industry-specific compliance standards
Different industries have different rules for handling data, and your secure AI strategy must account for them. Failing to meet these standards can result in steep fines and loss of your customers' trust.
|
Industry |
Key AI Security Requirement |
Primary Concern |
|
Healthcare |
HIPAA compliance for AI-processed patient data |
Patient data privacy, mitigating bias in diagnostic AI |
|
Financial Services |
Model risk management, fair lending rules |
Fraud detection accuracy, regulatory transparency |
|
Retail |
PCI DSS for AI systems handling payment data |
Protecting customer data, ethical personalization |
How to build AI security without sacrificing progress
You might worry that strict security protocols will stifle creativity and slow development. In reality, a strong security posture builds the trust and confidence needed to move forward faster and more confidently.
As Captain Brian Erickson of the U.S. Coast Guard puts it in a Data Chief podcast episode,
"I think that technology has helped us along the way to visualize data that otherwise would be difficult and time-consuming to conceptualize and understand."
That trust in technology only comes when you know it's secure.
You can achieve this balance with key practices:
Security by design: Build security into your AI development lifecycle from day one, not as an afterthought
Rapid prototyping with guardrails: Allow your teams to experiment in secure sandbox environments where they can't accidentally expose sensitive data
Automated compliance: Use tools that automatically enforce security policies, reducing the manual burden on your developers
What to look for in AI security tools and platforms
The right AI security tools turn strategy from paper into practice. These tools generally fall into key categories, each addressing a different aspect of AI security.
AI-powered security platforms use machine learning to automate threat detection and response, spotting anomalies in user behavior or network traffic that signal an attack.
AI governance solutions help you create an inventory of your AI models, track their performance, and automate compliance checks against frameworks like NIST.
Data protection for AI includes tools for encrypting data both at rest and in transit, plus advanced techniques like differential privacy.
When evaluating an AI security platform, look for these features:
Integration ease: Works seamlessly with your existing security infrastructure
Scalability: Grows with your AI adoption without performance degradation
Visibility: Provides clear insights into your security posture across all AI systems
Automation: Reduces manual security tasks while maintaining human oversight
Put your AI security strategy to work
AI security isn't just about preventing breaches. It's about building the trust needed to make AI a core part of how you operate. When your teams and customers are confident that your AI systems are secure, governed, and transparent, adoption follows naturally.
Whether you're defending against AI security risks or building secure AI solutions from scratch, the path forward is clear. You can move forward with AI's potential while protecting what matters most: your data and your customers' trust.
A secure, governed AI analytics platform, like ThoughtSpot, can give you trusted answers from your data. Start your free trial today.
Frequently asked questions on AI security
How much should I budget for AI security implementation?
A good rule of thumb is to allocate 15-20% of your total AI project budget toward security measures. This covers the cost of specialized tools, training for your teams, and ongoing monitoring systems.
Can I retrofit security into existing AI deployments?
Yes, but it's significantly more difficult and expensive than building security in from the start. If you need to retrofit, begin with your highest-risk systems first and implement new controls incrementally to minimize disruption.
What are the warning signs of an AI security breach?
Watch for sudden changes in your model's behavior, unexpected or illogical outputs, unusual data access patterns, or a drop in performance. A continuous monitoring system is your best defense for catching these issues early.
How can I create an AI security culture across my teams?
Start with education about AI-specific risks and establish clear policies for AI usage. Build on that by using easy-to-use security tools and providing regular training to keep awareness high across your whole company.
