This article is part of VentureBeat’s special issue, “The cyber resilience playbook: Navigating the new era of threats.” Read more from this special issue here.
As AI adoption accelerates across enterprises, its lightning-fast adaptability creates a security paradox — how do teams protect a system that constantly evolves while scaling it enterprise-wide?
Adversarial AI is now dominating the threatscape, fueling a stealth cyber war. Adversaries are quick to weaponize every aspect of AI, including large language models (LLMs). AI’s rapid adoption is opening up new attack surfaces that security teams can’t keep up with using current security technologies.
The bottom line is that the gap between adversarial AI and defensive AI is growing fast, with enterprises’ security and financial stability hanging in the balance. From data poisoning to prompt injection attacks, adversaries are already exploiting AI’s vulnerabilities, turning the technology into a vector for misinformation, security breaches and business disruption.
How Cisco is helping close the gaps
Cisco’s AI defense strategy aims to close these widening gaps between adversarial AI tradecraft and its potential to harm enterprises. With the majority of gen AI deployments expected to lack adequate security by 2028, Cisco’s timing is prescient.
Gartner also reported in its Emerging Tech Impact Radar: Cloud Security that 40% of gen AI implementations by 2028 will be deployed on infrastructures lacking adequate security coverage, exposing enterprises to AI-driven cyber threats at an unprecedented scale.
No business can afford to procrastinate about protecting AI models — they need help addressing the paradox of managing such a highly adaptable asset that could be easily weaponized without their knowledge.
Launched in January, Cisco’s AI Defense addresses this conundrum, integrating real-time monitoring, model validation and policy enforcement at scale.
The unseen war: AI as the attack surface
AI’s biggest strength, and where it is delivering the most value to enterprises, is its ability to self-learn and adapt. But that’s also its greatest weakness. AI models are non-deterministic, meaning their behavior shifts over time. This unpredictability creates security blind spots that attackers exploit.
Evidence of just how severe the stealth cyberwar is surfacing as the paradox grows wider. Data poisoning attacks are corrupting training datasets, causing AI to produce biased, flawed or dangerous outputs. Prompt injection attacks are de …