The external piece, “AI Is Redefining Cybersecurity: Why Awareness Alone No Longer Protects Us” (Oct 29, 2025), spotlights a simple shift: awareness is table stakes; action and AI-powered resilience are the new baseline. Voices from across the ecosystem (Lineaje, HackerOne, LearnUpon, Protegrity, Securin, Luminys, Foxit, and others) argue that modern risks—deepfakes, voice clones, and agentic automation—demand real-time validation, secure-by-design engineering, and human–AI collaboration.
What’s New
- Human-layer attacks surge: Deepfake cybercrime up 900%; voice-clone scams growing 66% YoY; phishing remains the #1 breach entry point.
- Fight AI with AI: Adversaries chain “toxic combinations” of CVEs/CWEs/misconfigs; defenders need AI-assisted discovery, systematic red teaming, and model labeling/visibility.
- Rise of the “bionic hacker”: 70% of researchers self-describe as AI-native; AI systems included in testing grew 270% year-over-year, accelerating reconnaissance and triage.
- Governance gap shows up in traffic blocks: Enterprises reportedly block 18.5% of AI/ML transactions—up 577% in nine months—while policies catch up.
- Beyond digital: Physical security needs smarter SOPs and AI-powered visibility; data alone isn’t protection without aligned process and action.
- Supply chain exposure: Industry-level attacks and domino-style incidents drive TIP-enabled intelligence sharing and collective defense.
Why It Matters
Awareness training can’t keep pace with synthetic media and agentic tools operating at machine speed. Organizations need controls that span channels and contexts; visibility into how employees actually use AI; and protections that keep sensitive data safe across cloud, SaaS, and on-prem environments.
Highlights from Industry Voices
- Lineaje: Treat AI like a supply chain dependency—demand transparency into models/datasets/pipelines and secure “intelligence” with open-source-level rigor.
- Netarx: Close the human-trust gap with real-time, AI-powered validation across email, messaging, voice, and video.
- Securin: Don’t chase single flaws; find the combinations that fail together. Use red teaming and secure-by-design engineering.
- HackerOne: The “bionic hacker” is here—AI amplifies human creativity and speed rather than replacing it.
- Luminys: Physical systems need intelligent analysis and updated SOPs, not just more recorded data.
- Sentra: Shadow and duplicate data are business risks; governance begins with continuous data visibility.
- Foxit & LearnUpon: Everyday documents and training matter; align controls with how people actually work and learn.
Protegrity POV
Enterprise AI is scaling faster than its security architecture. Embedding field-level encryption, tokenization, and privacy-preserving design across the model lifecycle lets teams build useful, compliant systems without exposing regulated data—progressing 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/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: Prototype protections locally and scale the same patterns to production—no rewrites.
Key Takeaways
- Do, don’t just know: 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 an article published by a third-party outlet for convenience. For the complete context, please refer to the original source below.