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# Tackling the Risks of AI - With AI

How do you stop automated cyberattacks that breach enterprise networks in minutes? Defending your expanding attack surface requires fighting malicious AI with precision AI.

- **Speakers:** [Kai Grunwitz](https://www.wearedevelopers.com/@kai-grunwitz), [Klaus Bürg](https://www.wearedevelopers.com/@klaus-burg), [Tomislav Tipurić](https://www.wearedevelopers.com/@tomislav-tipuric)
- **Event:** World Congress 2025
- **Published:** August 20, 2025
- **Duration:** 29:28
- **URL:** https://www.wearedevelopers.com/videos/1690-tackling-the-risks-of-ai-with-ai

## Summary

The integration of generative AI is expanding the corporate attack surface and radically accelerating cyber threats. Threat actors are utilizing AI to automate crime-as-a-service, reducing attack preparation time from months to minutes and dropping average IT breach times from weeks to mere hours. As shadow AI flourishes—with internal audits often revealing that half of a company's workforce uses unapproved models—defenders face novel challenges like data leakage, poisoned training sets, and sophisticated phishing campaigns that easily bypass traditional security filters.

To combat the unprecedented volume of daily novel attacks, defenders must fight AI with AI; human intervention alone is no longer fast enough. Leveraging a consolidated cybersecurity platform approach eliminates the friction of managing fragmented point solutions and unifies network telemetry into a centralized data lake. By utilizing "precision AI" and machine learning pattern recognition, security teams can process massive datasets to detect behavioral anomalies with near-perfect accuracy. This proactive stance is required to secure the four critical pillars of organizational AI risk: compute power, language models, training datasets, and API plugins.

The deployment of agentic AI introduces powerful automated remediation capabilities, such as dynamically prioritizing system patches during an active threat. However, granting autonomous agents administrative rights turns them into high-value targets for attackers seeking unauthorized access. Mitigating these systemic risks requires a fundamental cultural and business transformation. Organizations must safely bridge robust legacy mainframes with modern cloud environments while heavily investing in digital literacy, ensuring security teams maintain a human-in-the-loop mindset to critically assess AI outputs rather than blindly trusting automated decisions.

**Keywords:** generative AI attack surface, shadow AI discovery, cybersecurity telemetry consolidation, agentic AI vulnerabilities, precision AI threat detection, automated incident response, machine learning pattern recognition, legacy IT infrastructure modernization, cybersecurity platform approach, AI model poisoning, crime-as-a-service automation, enterprise data leakage prevention, human-in-the-loop security, API plugin security, cloud hyperscaler networking

## Chapters

1. **Evolving threat landscapes and generative ai attack tools** (00:05) — The adoption of new APIs and large language models lowers the barrier for automated, sophisticated cyber attacks.
1. **Managing shadow ai adoption and enterprise data leakage** (04:17) — Unsanctioned employee use of large language models requires organizations to secure AI infrastructure, datasets, and plugins against data leakage.
1. **Modernizing legacy infrastructure for ai driven business transformation** (07:51) — Bridging secure legacy systems with agile hyperscaler platforms enables companies to survive disruption while maintaining critical operational resilience.
1. **Precision ai and high quality security telemetry data** (11:00) — High-fidelity telemetry and pattern recognition are essential for achieving precise threat detection across complex hybrid environments.
1. **Accelerating threat response times with machine learning automation** (13:35) — Since threat actors can now compromise environments in hours, organizations must fight automated attacks with machine learning defensive patches.
1. **Autonomous security responses and delegated ai agent risks** (15:46) — Granting artificial intelligence agents administrative rights enables rapid threat response but turns them into prime targets for attackers.
1. **Building trust and cultural adoption for ai frameworks** (20:28) — Developing human confidence in algorithmic decision-making demands organizational training, transparent human-in-the-loop models, and critical evaluation of outputs.
1. **Consolidating cybersecurity tools into a unified platform approach** (26:28) — Standardizing telemetry data across a single platform eliminates the visibility gaps caused by disjointed point solutions.

## Related Moments

- [Uncovering the hidden risks of generative AI adoption](https://www.wearedevelopers.com/videos/1744-genai-security-navigating-the-unseen-iceberg) (from "GenAI Security: Navigating the Unseen Iceberg")
- [Understanding the landscape of AI capabilities and risks](https://www.wearedevelopers.com/videos/715-a-hundred-ways-to-wreck-your-ai-the-in-security-of-machine-learning-systems) (from "A hundred ways to wreck your AI - the (in)security of machine learning systems")
- [Addressing active AI incident remediation and broad ecosystem support](https://www.wearedevelopers.com/videos/100248-reporting-active-exploits-in-24-hours-are-you-ready-for-the-cra) (from "Reporting Active Exploits in 24 Hours: Are You Ready for the CRA?")
- [Emergence of generative AI as a new attack surface](https://www.wearedevelopers.com/videos/926-wwc24-chris-wysopal-helmut-reisinger-and-johannes-steger-fighting-digital-threats-in-the-age-of-ai) (from "WWC24 - Chris Wysopal, Helmut Reisinger and Johannes Steger - Fighting Digital Threats in the Age of AI")
- [Identifying emerging security vulnerabilities in generative AI agents](https://www.wearedevelopers.com/videos/1383-the-state-of-genai-machine-learning-in-2025) (from "The State of GenAI & Machine Learning in 2025")
- [Emerging risks and attack vectors in AI systems](https://www.wearedevelopers.com/videos/1948-building-trustworthy-ai-in-industry-beyond-traditional-cybersecurity) (from "Building Trustworthy AI in Industry: Beyond Traditional Cybersecurity")

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