An adversarial attack is when someone intentionally tricks or manipulates an AI system to make mistakes or behave in unintended ways. Think of it like trying to fool a human employee by giving them misleading information, but specifically designed to exploit how AI systems process data.

These attacks can range from subtle to obvious. For example, slightly modifying a few pixels in an invoice image could trick an AI invoice processor into reading $1,000 as $10,000.

Or someone might craft a vendor email with carefully chosen words that bypass your AI fraud detection system. The attacker doesn't need access to your system's code. They just need to understand how to present information in a way that confuses the AI.

For businesses using AI to automate processes like accounts payable, procurement, or customer verification, adversarial attacks represent a new type of operational risk. Unlike traditional fraud or cyberattacks that target your systems directly, adversarial attacks exploit the AI's decision-making process itself. The AI might be working perfectly as designed, but it's been given input specifically crafted to produce wrong outputs.

The good news is that adversarial attacks require significant effort and knowledge to execute successfully. Most business AI systems face far more risk from simple human error, poor data quality, or misconfigured rules than from sophisticated adversarial manipulation.

Frequently Asked Questions:

What's an example of an adversarial attack that would affect my business?

Imagine your company uses AI to automatically approve vendor invoices under certain conditions. An attacker could study your approval patterns and craft invoices that look legitimate to the AI but contain fraudulent charges.

They might add imperceptible changes to PDF formatting, strategically use certain keywords, or structure the data in a way that exploits the AI's pattern recognition. The invoice appears normal to human eyes, but the AI misclassifies it as low-risk and auto-approves it.

This is different from traditional invoice fraud because the attacker is specifically exploiting the AI's weaknesses rather than just creating a fake invoice.

How is this different from regular fraud or hacking?

Regular fraud tries to fool humans with fake documents or false information. Traditional hacking targets security vulnerabilities in your software or network infrastructure. Adversarial attacks specifically target the AI's decision-making logic.

The attacker isn't breaking into your system or creating obviously fake documents. They're exploiting how the AI interprets data to produce incorrect outputs.

Think of it as the difference between picking a lock (hacking), using a fake ID (fraud), and knowing exactly what to say to get past a security guard without raising suspicion (adversarial attack).

Can AI defend against adversarial attacks?

Yes, but it requires multiple layers of defense. Researchers have developed techniques like adversarial training where AI systems are exposed to attack examples during their development, making them more resilient.

However, no single solution provides complete protection. The most effective approach combines technical defenses with operational safeguards.

For instance, using multiple different AI models to cross-check decisions, implementing approval thresholds that require human review for high-value transactions, and monitoring for unusual patterns that might indicate manipulation attempts.

What makes AI vulnerable to these attacks in the first place?

AI systems make decisions based on patterns they learned from training data, but they don't truly "understand" content the way humans do. They rely on statistical correlations and mathematical patterns. This means that small, carefully designed changes to input data, changes that humans wouldn't even notice, can push the AI's decision in a completely different direction.

For example, an AI that learned to identify cats from thousands of photos might confidently misidentify a cat if specific pixels are changed in ways invisible to human eyes. The AI hasn't learned the concept of "cat" the way you understand it. It has learned mathematical patterns that usually correlate with cats.

Should this change how I implement AI in my business processes?

Yes, but not drastically. The existence of adversarial risks reinforces best practices you should follow anyway.

• Always maintain human oversight for high-stakes decisions, implement approval workflows with clear escalation paths,
• Monitor AI outputs for anomalies.
• Never give AI complete autonomous control over critical business functions.

Design your processes assuming that the AI will occasionally make mistakes, whether from adversarial attacks, edge cases, or simple errors. This means having clear audit trails, exception handling, and mechanisms for humans to review and override AI decisions when something seems off.

How much does protection against adversarial attacks cost?

Protection costs depend on your risk profile and the criticality of the process. For most business AI applications, basic protections like proper approval workflows, monitoring, and human oversight checkpoints add minimal cost, they're simply good process design.

These operational safeguards catch most problems regardless of whether they stem from adversarial attacks or other issues. More sophisticated technical defenses, like adversarial training or ensemble models, require additional investment but are typically only necessary for high-risk applications like fraud detection or security systems.

Start with strong operational controls, which protect against many types of errors, then evaluate whether your specific use case justifies additional technical safeguards.