The best work in the background: how machine learning strengthens cyber security

Machine learning: between hope and risk

Big data analytics, informed ML, visual analytics, quantum learning; the world of machine learning is growing rapidly. The good news is that ML can detect cyberattacks faster, report anomalies in data traffic automatically and react proactively. The bad news is that these same technologies can also be used by attackers, often to devastating effect.

Cybercriminals, including state-sponsored groups, use targeted ML models to spy on vulnerabilities, infiltrate systems and better disguise attacks. Companies that do nothing to counter this are falling behind. The race is not linear, but exponential.

Cybersecurity in the age of ML: What really matters

At CANCOM, we observe many companies using AI and ML offerings uncontrolled on a daily basis. Employees use ChatGPT, CoPilot and other tools without security guidelines or governance structures in place. The problem is not ML, but the lack of a secure framework.

What matters:

• Verified data sources: ML models are only as good as the data used to train them. Incorrect or manipulated data makes them worthless or even dangerous.
• Automation with control: Sophisticated ML-based automation in security operations enables faster reactions and better prevention. But only if control is not relinquished.
• Expertise & trust: Not only good tools are needed, but also experienced analysts and reliable technology partners.

The CANCOM Cyber Defense Center - Silent excellence

At the CANCOM Cyber Defense Center (CDC ), specialists with a deep understanding of cyber security processes and state-of-the-art ML models work together in the background, but highly effectively. The CDC is one of the leading facilities in the DACH region and focuses on:

• Early detection through ML-supported threat detection
• Valid, verified data pools
• Synergies with leading global technology partners
• Close integration of IT and OT security

"You can't blindly trust AI," says Erwin Friedl, Principal Cyber Security Analyst at CANCOM. "Every decision must be validated, every data source scrutinized, every model checked."

What companies should pay attention to now

Cyber security does not end with network protection. Especially in industry and critical infrastructure, it is also about production processes and operational systems (OT). It is therefore important to use ML responsibly and strategically.

Our recommendations:

• Sensitize your team.
  Train employees in the use of AI/ML tools. Explain the risks and set clear rules for their use.
• Segment your systems.
  Separate productive ML models and AI systems from the general network, especially in the OT area.
• Implement what you plan.
  Concepts alone do not help. Only practiced and maintained security measures offer real protection.
• Update consistently.
  System maintenance, updates and patch management are the cornerstones of any security strategy.
• Choose strong partners.
  CANCOM offers you a comprehensive ecosystem of tools, expertise and services. For sustainable and future-proof cyber defense.

Trust is good, validated machine learning is better

Cybersecurity thrives on precision, speed and foresight. Machine learning can be an enormously powerful lever, but only if it is controlled, verified and used within a secure framework. The best technologies work in the background. Just like the best people.