Published summary based on a third-party press release • Last updated: Oct 27, 2025
The external piece, “Building a Cyber Strong Industry: From Awareness to Action,” spotlights how this year’s Cybersecurity Awareness Month themes—CISA’s “Building a Cyber Strong America” and NIST’s “Stay Safe Online”—come down to one thing: turning awareness into practice. Contributors across threat intel, AI governance, and data security argue that modern risks (deepfakes, voice clones, agentic automation) demand real-time validation, secure-by-design engineering, and data protections that travel with systems and people.
What’s New
- Human-layer attacks surge: Deepfake-related cybercrime reportedly up 900%; voice-clone scams rising 66% YoY; phishing remains the top breach entry point.
- Fight AI with AI: Adversaries use AI to chain “toxic combinations” of CVEs/CWEs/misconfigs; defenders need AI-assisted discovery, red teaming, and labeling frameworks.
- “Bionic hacker” era: 70% of researchers identify as AI-native; tested AI systems grew 270% year-over-year, accelerating reconnaissance and triage.
- AI governance gaps: Enterprises reportedly block 18.5% of AI/ML transactions (up 577% in nine months), signaling caution while policies mature.
- Supply chain risk escalates: Domino-style incidents and industry-level attacks push for TIP-driven intel sharing and joint defense.
- Beyond digital only: Physical security needs smarter SOPs and AI-powered visibility—data alone isn’t enough without aligned action.
Why It Matters
Awareness training alone won’t close the gap when synthetic media, agentic tools, and unsupervised AI adoption move at machine speed. Organizations need controls that cross email, chat, voice, and video; visibility into AI usage; and protections that keep sensitive data safe across cloud, SaaS, and on-prem environments.
Protegrity POV (from the piece)
Enterprise AI is scaling faster than its security architecture. Field-level encryption, tokenization, and privacy-preserving design embedded across the model lifecycle let teams build useful, compliant systems without exposing regulated data—shifting from reactive blocking to proactive protection.
How Protegrity Helps
- Discovery: Find and classify sensitive data (PII/PHI/PCI) across apps, prompts, logs, and pipelines to reduce shadow and duplicate data risk.
- Find & Protect APIs: Tokenize and encrypt at the field level so data stays useful for analytics and GenAI—without exposing raw values.
- Semantic Guardrails: Inspect inputs, tools, agent plans, and responses to mitigate social engineering, leakage, and unsafe actions in real time.
- Developer Edition: A lightweight path to prototype protections locally and scale to production controls without rewrites.
Key Takeaways
- Awareness isn’t the finish line—pair training with cross-channel verification, intel sharing, and AI-assisted defense.
- Protect the data layer—bring security into the AI/ML lifecycle so innovation and compliance rise together.
Note: This page summarizes a press release published by a third-party outlet for convenience. For the complete announcement, refer to the original source below.