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 designed to trick AI chatbots into revealing sensitive customer data or execute commands that harm models and data. Model exfiltration targets proprietary AI models, stealing intellectual property and undermining a company’s competitive advantage.
Shadow AI — or the unsanctioned use of AI tools by employees, who inadvertently (or not) feed sensitive data into external AI models like ChatGPT and Copilot — is also contributing to a problem growing wider and at a faster rate.
As Jeetu Patel, EVP and CPO at Cisco told VentureBeat: “Business and technology leaders can’t afford to sacrifice safety for speed when embracing AI. In a dynamic landscape where competition is fierce, speed decides the winners.”
Simply put: Speed without security is a losing game.
Cisco AI Defense: A new approach to AI security
Cisco’s AI Defense is purpose-built, embedding security into network infrastructure so it can scale and protect every aspect of AI development, launch and use.
At its core, the platform delivers:
- AI visibility and shadow AI detection: Security teams gain real-time visibility into sanctioned and unsanctioned AI applications, tracking who is using AI, how it’s being trained and whether it complies with security policies.
- Automated model validation and red teaming: Cisco’s AI algorithmic red teaming, developed from its Robust Intelligence acquisition, runs trillions of attack simulations, identifying vulnerabilities before adversaries do.
- Runtime AI security and adaptive enforcement: AI models undergo continuous validation to detect and block prompt injection, data poisoning and adversarial exploits in real time.
- Access control and data loss prevention (DLP): Enterprises can prevent unauthorized AI usage, enforce security policies and ensure sensitive data never leaks into external AI models.
By embedding AI security into Cisco’s networking fabric, AI Defense ensures that AI security is intrinsic to enterprise operations — and not an afterthought.
AI Defense embeds security into the DNA of AI-driven enterprises
Anxious for results and fearful of falling behind competitors, more organizations are rushing to deploy AI at scale. The growing “deploy now, secure later” rush to results is risky at best and helps fuel the stealth cyberwar against well-funded adversaries intent on attacking target organizations at will.
Cisco’s 2024 AI Readiness Index found that only 29% of enterprises feel equipped to detect and prevent unauthorized AI tampering. This means that 71% of enterprises are vulnerable to AI-driven cyberattacks, compliance violations and catastrophic AI failures.
Gartner warns that enterprises must implement AI runtime defense mechanisms, as traditional endpoint security tools cannot protect AI models from adversarial attacks.
To stay ahead, enterprises must:
- Adopt unified AI security frameworks: Security solutions must be holistic, automated and embedded into infrastructure.
- Implement AI threat intelligence and continuous validation: AI models require constant monitoring as the threat landscape shifts too rapidly for static defenses.
- Ensure AI compliance across multi-cloud environments: Regulatory frameworks are tightening globally. Enterprises must align AI security policies with evolving compliance mandates like the EU AI Act and NIST AI Security Framework.
Cisco AI Defense: Hardening enterprise AI against evolving threats
AI is the future of enterprise innovation, but unsecured AI is a liability. Left unprotected, AI can be manipulated, exploited and weaponized by cybercriminals.
Cisco AI Defense is not just a security tool — it is an enterprise-wide AI security strategy. By integrating real-time AI monitoring, automated model validation and network-embedded enforcement, Cisco is setting the new standard for AI security at scale.
As Patel warned: “The security challenges AI introduces are new and complex, with vulnerabilities spanning models, applications and supply chains. We have to think differently. AI Defense is purpose-built to make sure enterprises can innovate boldly, without tradeoffs.”
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