The reality is that cybercriminals are also adopting AI tools to scale attacks, launch flawless phishing campaigns, and systematically harvest corporate credentials.  

Gartner predicts that, by 2027, the improper use of generative AI will account for over 40% of AI-related data breaches. For organizations, this means that ensuring AI security should be on their priority list.

In this guide, we will break down the key AI risks and AI security best practices your business needs to stay protected. 

What is AI security?

AI security is the practice of safeguarding artificial intelligence systems, data pipelines, and infrastructure from exploitation while using those same AI tools to enhance an organization’s overall security stance. It is a dual-purpose discipline that allows businesses to automate the detection, prevention, and remediation of AI threats. 

What is the most important benefit of AI security for organizations? It enables them to maintain visibility over their security operations and use machine learning algorithms to adapt to new cyber threats. In practice, genAI tools can help with everything from preventing malware attacks by identifying and isolating malicious software to detecting brute force attacks by recognizing and blocking repeated login attempts. 

AI security vs. AI for cybersecurity

To effectively manage threat exposure, organizations must distinguish between the protection of AI and the deployment of AI. 

Traditional cybersecurity remains the overarching discipline for protecting an organization’s environment—systems, networks, identities, endpoints, data, and users.

An image that shows key differences between AI security and AI for cybersecurity

Artificial intelligence splits into 2 distinct operational areas:

  • AI security (or security for AI). This is about protecting the AI ecosystem itself. Organizations must secure large machine learning models, training datasets, and API integrations from malicious tampering, data leaks, or vulnerabilities like data poisoning and prompt injection.

  • AI for cybersecurity. This involves using machine learning (ML) technologies to strengthen a company’s defenses. By automating threat analysis and detection, incident response, and vulnerability management, AI augments traditional security tools and performs the tasks that are either too complex, too huge, or too fast for human analysts. 

In short, while AI security prevents your proprietary AI workflows from being compromised, AI for cybersecurity uses AI-based automation to give your team an advantage against attackers.

Why AI security is critical for your business

AI tools process highly sensitive business data. Without proper safeguards, employees may accidentally paste confidential information into public AI models, exposing it to public training sets and causing data leaks. Modern AI systems are connected to business apps, internal documents, cloud environments, and sensitive customer data. This creates an interconnected web of new entry points that cybercriminals can exploit.

They can also use AI to automate malicious workflows, execute targeted phishing campaigns, perform high-velocity password guessing, and develop adaptive malware variants on an unprecedented scale.

Ultimately, deploying AI security is critical because it protects the core pillars of business trust:

  • Sensitive data protection. AI systems ingest large amounts of proprietary corporate code, financial metrics, and customer identifiers. Strong security measures help protect sensitive data from unauthorized access and exposure.

  • System integrity. AI models are vulnerable to tampering. Unchecked software vulnerabilities can lead to compromised training models or corrupted pipelines, causing the system to yield inaccurate, biased, or harmful outputs.

  • Safeguarding service availability. As organizations build AI agents directly into their daily operations, these systems become their critical infrastructure. Security boundaries minimize the risk of malicious downtime, keeping automated services consistently operational.

  • Accountability. For AI technologies to be successfully adopted, organizations and users must have absolute confidence that the underlying data systems are secure and reliable.

The key advantages of AI security

Deploying AI models within your security operations allows organizations to:

  • Accelerate threat detection. Machine learning algorithms parse massive datasets in real time to isolate sophisticated attacks that traditional perimeter security measures miss.

  • Scale your operations. Automating routine workflows cuts costs and reduces human error.

  • Improve user experience. AI-powered authentication, like biometric recognition, removes friction while strengthening identity security.

  • Automate compliance. AI continuously monitors data protection baselines, which streamlines regulatory reporting.

Top AI security risks for businesses 

Despite several advantages, AI adoption comes with quite a few risks for organizations. According to IBM’s 2025 Data Breach Report, 13% of organizations have suffered a breach that involved their AI models. Of those AI-related breaches, 97% happened because the company lacked sufficient access controls. 

So, because data is the foundation of every AI system—shaping how models learn, behave, and make decisions—any compromise during collection, storage, or execution can ruin your model’s accuracy and destroy customer trust. 

Here are the top risks related to AI: 

Shadow AI

Employees can bypass official channels to paste proprietary code, financial files, or sensitive client information into public AI tools. This creates blind spots, and it’s costly. In fact, 1 in 5 organizations (20%) has already suffered a data breach caused by an unmanaged shadow AI security incident.

Input manipulation

  • Prompt injection. Attackers manipulate AI prompts or inputs to trick the system into ignoring its original instructions, revealing sensitive data, or performing unintended actions. This is a huge risk for user-facing applications like AI chatbots, copilots, and autonomous AI agents.

  • Adversarial attacks. Threat actors compromise the AI system’s outputs by intentionally feeding the model bad or manipulated training data. This manipulation of training data, fine-tuning data, or internal knowledge bases causes the AI system to produce unreliable, biased, or harmful outputs.

Data risks

  • Data leakage. Involves the unintentional disclosure of sensitive information, customer records, credentials, or proprietary source code through model outputs, system logs, or poorly managed shared datasets. 

  • Re-identification. Attackers can mathematically reconstruct personal or proprietary raw information from anonymized or aggregated datasets, completely undermining data privacy guarantees. 

AI-powered identity attacks

  • AI-powered phishing. Bad actors can use AI to create phishing emails, realistic impersonation videos, deepfake voice messages, and fake login pages that can easily deceive users.

  • AI-powered password guessing. With AI, attackers analyze leaked password databases and personal info, spot predictable patterns, and launch high-velocity brute force attacks. By studying online breach data, AI generates highly accurate password predictions to systematically target employee accounts. The only way to neutralize AI-powered password guessing is to take password creation out of human hands (and let a credential manager, like NordPass, do it).

Excessive privilege for autonomous AI agents

Connecting AI agents to internal apps creates a high-risk blind spot if they have too many permissions. Without strict boundaries, a single malicious prompt injection can cause an AI agent to automatically delete files, leak data, or alter account settings.

How cybercriminals use AI 

Instead of writing custom malware from scratch, threat actors use AI to accelerate their attacks and target the weakest link in any organization—human behavior. 

Here are the top AI-driven attack tactics:

  • AI-crafted phishing (37% of AI attacks). GenAI has completely eliminated traditional warning signs like poor grammar, bad formatting, or generic messaging. Attackers use language models to scrape public data and automatically create thousands of personalized emails that appear to be sent by real colleagues or trusted vendors.

  • Deepfake impersonation (35% of AI attacks). Bad actors use AI to clone voices and generate highly realistic videos that mimic corporate executives or business partners. These videos are used in social engineering scams to trick employees.

  • Automated password guessing. Traditional credential stuffing relies on robotic, static lists that are easily blocked by IT firewalls. However, AI can analyze historical data breaches to learn subtle human password habits. It predicts pattern updates—like seasonal changes or character substitutions—to accurately target employee accounts.

  • Mimicking real user behavior. Engineered to blend in, malicious AI agents randomize their login timing, shift their locations across extensive proxy networks, and adjust their tactics in real time to look exactly like routine employee activity.

  • Bypassing CAPTCHA and MFA. Attackers combine visual AI models with automated workflows to instantly solve anti-bot CAPTCHA prompts. They also use automated, AI-driven fake login pages to trick users, capture active session tokens, and bypass weaker multi-factor authentication (MFA) methods.

Top 5 AI security best practices: a CISO’s checklist

Organizations that use AI security identify and contain breaches 108 days faster than those that don’t. What’s more, these companies save an average of $1.7 million per incident—a nearly 40% reduction in breach-related costs. 

Securing an AI-driven environment requires anchoring your strategy around identity control, strict data governance, and continuous endpoint visibility. Use this 5-step checklist to protect your enterprise workflows from exploitation.

1. Establish clear rules for how data interacts with AI systems

  • Define the approved tools. Keep an explicit inventory of approved enterprise AI models. Create approval channels for AI tools, making sure your employees don’t use the unsanctioned public ones. 

  • Secure the data pipeline. Put strict limits on the data your tools can ingest. Ensure training data, internal knowledge bases, and documentation are protected against unauthorized manipulation. 

  • Integrate AI with your existing infrastructure. Tie any new automated tools seamlessly into your centralized user directory and identity management frameworks. 

2. Protect corporate accounts with strong authentication

  • Deploy phishing-resistant MFA. As AI-driven phishing and voice impersonation (vishing) grow more convincing, relying on basic SMS verification creates a vulnerability. Protect corporate entry points with multi-factor authentication (MFA) or passkeys.

  • Use single sign-on (SSO). Keep login verification entirely separate from your underlying vault data or decryption keys to block automated account takeovers. 

3. Enforce strict credential and password hygiene policies

  • Create strong password policies. Use a credential manager for generating strong, unique passwords that are not predictable. This way, you also prevent employees from reusing those passwords. 

  • Audit compliance. Organization-wide policies can scan for weak, reused, or compromised credentials to eliminate simple paths to compromise.

4. Limit the AI agency with the principle of least privilege

  • Restrict system permissions. Apply strict least-privilege access rules to every automated tool. An AI chatbot or copilot should only have access to the exact databases and files it needs to perform its job.

  • Prevent administrative control. Never grant autonomous agents full administrative privileges or the ability to modify account settings, delete critical directories, or alter user permissions without manual human oversight.

5. Deploy continuous monitoring and treat detection 

  • Log every interaction. Continuously monitor and log access records, unusual prompt patterns, unexpected API activity, and abnormal model outputs to catch exploitation attempts early.

  • Track exposed credentials. Use automated scanning to check if corporate emails or credentials linked to external services have appeared on the dark web or in external data breaches, allowing you to force password resets before attackers can use the leaked data.

How NordPass can help with AI security posture management

Strong data protection relies on knowing who has access to your systems and ensuring that credentials are properly secured. As AI makes phishing attacks more convincing and easier to carry out on a large scale, organizations need authentication practices that help prevent unauthorized access.

Here is how NordPass can protect your business against automated AI threats: 

  • Neutralizes AI-powered password guessing. Taking password creation out of human hands is the single most effective way to block automated brute-force tools. NordPass generates complex credentials that break predictable patterns. 

  • Eliminates password reuse. Because passwords are securely stored in an encrypted vault, employees don’t need to memorize them. Also, centralized oversight prevents users from reusing weak credentials across critical work applications.

  • Allows to securely share credentials. This ensures only authorized users have the necessary access keys.

  • Monitors for compromised credentials. Data Breach Scanner tracks your verified business domains in the background. The moment any employee emails or company credentials leak onto the dark web, administrators receive an alert and can reset the credentials before automated AI bots can exploit the data.

Ready to secure your access and eliminate the risks of credential theft? Contact our sales team today and secure your business.