The Top Five Ways AI Powers Hacks

Several breach report studies have reported the use of Artificial Intelligence (AI) in breach statistics. As of the 2024 report, 16% of breaches studied were found to have used some AI to make it happen. As AI becomes more prevalent in our daily life and models become more available, this will likely rise.

With the help of ChatGPT, I asked for the top 5 AI-powered hacks in the financial industry. Personally, I’ve heard of items one through four in threat reports and had several institutions report number one. Number five is probably the least likely so far, but is quite deceptive.

1. Deepfake-Driven Fraudulent Transactions

How it works:
Attackers now use AI to generate highly convincing voice clones and video deepfakes of executives. In 2020, criminals cloned a CEO’s voice well enough to convince a bank manager to authorize a $35M fraudulent transfer.
AI makes this scalable; thousands of targeted calls can be generated in minutes.

How to protect:

• • Strong authorization policies: No financial transfers approved solely by voice or chat.
  • Human callback verification: Finance teams must verify unusual requests via a trusted channel.
  • Deepfake detection tooling: Deploy tools that flag anomalies in audio cadence or digital noise patterns.

2. AI-Automated Phishing & Business Email Compromise

How it works:
Generative models craft tailored emails using public employee data, writing in natural, error-free language. The result: phishing emails are nearly indistinguishable from legitimate internal communications.
These models can even dynamically adjust tone—formal with executives, casual with operations teams.

How to protect:

• • Advanced email filtering using behavioral signals (sender identity, communication history).
  • Regular phishing simulations to condition employees to spot subtle red flags.
  • DMARC, SPF, and DKIM enforcement to reduce spoofing.

3. AI-Enhanced Vulnerability Discovery

How it works:
Threat groups use AI to analyze open-source code, leaked configurations, and API documentation to identify weaknesses at scale. Instead of manually scanning for vulnerabilities, AI prioritizes the most exploitable paths, speeding up attack timelines dramatically.

How to protect:

• • Continuous automated code scanning and supply chain management.
  • Threat-informed patch prioritization integrating AI-driven scoring (e.g., EPSS).
  • Regular threat hunting and monitoring to stay ahead of automated recon techniques.

4. LLM-Assisted Social Engineering & Multi-Channel Impersonation

How it works:
AI systems can maintain long, adaptive conversations across email, SMS, LinkedIn, and even chatbots—mirroring writing styles and adjusting to employee responses. This makes traditional “scripted” social engineering detectable, but AI-driven ones feel like real humans.

How to protect:

• • Enterprise identity verification embedded into internal workflows.
  • Monitoring for look-alike domains and social profiles.
  • Employee training focusing on behavioral red flags, not grammar or spelling errors (because AI eliminates those).

5. Data Poisoning & Model Manipulation

How it works:
As banks adopt AI for fraud detection or risk scoring, attackers attempt to poison training data or inject malicious inputs that cause the model to misclassify fraudulent behavior. An example in the industry: researchers demonstrated poisoning of ML-based anti-fraud systems by subtly altering transaction patterns until the system “learned” to accept abnormal behavior.

How to protect:

• • Rigorous data supply-chain controls—monitor for anomalies in data sources and labeling.
  • Model explainability & drift monitoring to catch suspicious pattern shifts.
  • Segregated training environments and strong validation before model deployment.
    
    

Takeaway

AI does not create new types of attacks—it supercharges existing ones by increasing scale, personalization, and speed.

The most effective defenses combine the following:

1. Stronger identity and verification controls
2. Behavioral detection
3. Employee resilience training
4. Proactive monitoring of AI systems themselves

If your institution needs help preparing for AI-driven threats, our team at Bedel Security is here for you. Contact us to get the conversation started.