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As AI adoption increases across enterprises, its lightning fast adaptability creates an interesting security paradox. How can teams protect a system which is constantly evolving while scaling it to the enterprise?
Adversarial artificial intelligence is now dominating the threatscape and fueling a stealthy cyber war. Adversaries are quick weaponize AI, including large-language models (LLMs). The rapid adoption of AI is creating new attack surfaces, which security teams cannot keep up with by using current security technology.
The bottom-line is that the gap between defensive AI and adversarial AI is growing rapidly, and enterprises’ financial and security stability are on the line. Data poisoning and prompt injection attacks are just two of the ways adversaries have exploited AI’s vulnerabilities. They have turned it into a tool for misinformation, security breaches, and business disruption.
How Cisco is helping to close the gaps
Cisco’s AI Defense Strategy is designed to close the gap between adversarial AI tactics and their potential to harm enterprise. Cisco’s timing was prescient, as most gen AI deployments are expected to lack adequate protection by 2028.
Gartner also reported in its Emerging Tech Impact: Cloud Security reveals that 40% of gen AI deployments by 2028 are likely to be on infrastructures without adequate security coverage. This exposes enterprises to AI-driven threats at unprecedented scale.
Businesses cannot afford to delay protecting AI models. They need help with the paradox of managing a highly adaptable asset which could be weaponized without their knowledge. Cisco’s AI Defense, launched in January, addresses this conundrum by integrating real-time monitor, model validation, and policy enforcement on a large scale.
The unseen battle: AI as an attack surface
AI is best known for its ability to adapt and self-learn. This is where it delivers the most value to businesses. This is also its biggest weakness. AI models are nondeterministic, which means that their behavior changes over time. This unpredictability leads to security blind spots which attackers can exploit.
As the paradox widens, evidence of the severity of the stealth cyberwar emerges. Data poisoning attacks corrupt training datasets and cause AI to produce biased or dangerous outputs. Prompt injection attacks are used to trick AI chatbots and get them to reveal sensitive customer data, or execute commands which harm models and data. Model exfiltration is a method of stealing proprietary AI models and destroying a company’s advantage.
Shadow AI, or the unauthorised use of AI tools by staff who inadvertently feed sensitive data to external AI models such as ChatGPT and Copilot (or not), is also contributing towards a problem that is growing faster and wider.
Jeetu patel, EVP and Chief Product Officer at Cisco, told VentureBeat that “business and technology leaders cannot afford to sacrifice security for speed when adopting AI.” In a dynamic environment where competition is fierce and speed is the key to success,
Speed is what determines the winners.
Cisco AI Defense – A new approach to AI Security
Cisco AI Defense is purpose built, embedding the security into network infrastructure in order to protect and scale every aspect of AI development and launch. At its core, it delivers:
AI visibility and Shadow AI detection: Security teams can gain real-time insight into sanctioned and unsanctioned AI apps, tracking who’s using AI, how they are being trained, and whether or not it complies to security policies.
By integrating AI security into Cisco’s networking fabric, AI Defense ensures AI security is integral to enterprise operations and not an afterthought.
AI Defense embeds cybersecurity into the DNA of AI driven enterprises
In a rush to achieve results and avoid falling behind their competitors, many organizations are deploying AI at scale. The “deploy today, secure later” trend is at best risky and fuels the stealth cyberwar between well-funded adversaries who are willing to attack target organizations at will.
Cisco’s AI Readiness Index 2024 revealed that only 29% feel prepared to detect and prevent unauthorized AI tampering. This means that 71% are vulnerable to AI-driven attacks, compliance violations, and catastrophic AI failures. Gartner warns enterprises that traditional endpoint security tools are not able to protect AI models against adversarial attacks.
Enterprises must adopt unified AI security frameworks to stay ahead.
- Security solutions must be holistic and automated, and integrated into infrastructure. Implement AI threat intelligenceand continuous validation. AI models must be constantly monitored as the threat environment is changing too quickly for static defenses.
- Ensure AI Compliance across Multi-Cloud Environments: Regulatory Frameworks are tightening around the world. Enterprises must align AI policies with evolving compliance mandates such as the EU AI Act or NIST AI Security Framework.
Cisco AI Defense: Hardening Enterprise AI against Evolving Threats
AI represents the future of enterprise innovation. However, AI that is not protected is a liability. Cybercriminals can manipulate, exploit and weaponize AI if it is not protected. Cisco AI Defense
is more than a security tool. It is an enterprise-wide AI strategy. Cisco sets the new standard in AI security by integrating real-time AI monitor, automated model validation, and network-embedded enforcement.
Patel warned that the security challenges AI presents are new and complex. Vulnerabilities span models, applications, and supply chains. We need to think differently. AI Defense is designed to ensure that enterprises can innovate without compromising on quality.
