Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

For years, conversations about AI security risks were framed as forward-looking. Organizations were told to prepare for a future where autonomous agents would act on their behalf, access sensitive systems, and make consequential decisions without human intervention at every step. That future, it turns out, is now.

Technical debt costs US enterprises an estimated $2.41 trillion per year, according to Accenture research cited by AWS. For most organisations, roughly 30% of engineering time gets absorbed by maintaining legacy systems – work that’s necessary, but produces no new business value. That’s the problem AWS has been trying to solve with AWS Transform, its agentic AI service for enterprise application modernization.

→ Audit your AI systems against EU AI Act requirements now — validate Annex IV technical documentation, logging, and data governance. The initial August 2025 compliance date has passed, and full penalties begin in August 2026. → Build a continuous compliance evidence chain — document risk management across the full lifecycle (design, development, deployment, and post-market monitoring).

Anthropic’s Project Glasswing and the Claude Mythos model represents a fundamental change in the physics of cyber defense. With the gap between patch releases and weaponized exploits shrinking to hours, traditional manual security triage is now obsolete. Organizations must adopt AI-driven automated remediation.

The question circling boardrooms and compliance departments in 2026 is no longer hypothetical: Can AI replace a QSA? After nearly two decades guiding organizations through PCI DSS audits, gap assessments, and remediation programs, the answer is clear — No, AI cannot replace a Qualified Security Assessor in 2026. But it is fundamentally reshaping what being a QSA means, and professionals who ignore that shift do so at their own peril.

RAG systems connect AI models to your internal data, making them powerful but also creating serious security gaps in access control, data retrieval, and compliance. Knowing how to ensure data security in RAG systems means securing every layer of the pipeline from ingestion to retrieval to output.

It was a bold move, but our finance team was fully on board. They both approved and championed the approach. They wanted to see exactly how much value we could unlock for our customers. They didn't look at the resulting bill and ask us to slow down.