Every October, Cybersecurity Awareness Month reminds us how quickly the digital threat landscape evolves. This year, we’re focusing on one of the biggest shifts yet, how Artificial Intelligence (AI) is transforming not only the way we work, but also the way we protect it.
As AI adoption accelerates, one question rises to the top: How do we keep AI itself secure? That’s where Security for AI comes in, one of the core focus areas within Destination AI™, TD SYNNEX’s framework for helping partners confidently explore, adopt, and scale AI. It’s about protecting the foundation of AI innovation, the models, data, and pipelines that make it work, from attack, manipulation, and misuse.
AI is driving innovation, accelerating decision-making, and automating complex tasks. Yet this progress introduces a new paradox: the same technology fueling transformation can also expand the attack surface. Traditional security frameworks weren’t designed for the complexity of AI, creating a clear need for a new approach.
Regulators are taking notice, too. By 2025, all AI products will require documentation of model builds and exclusion of sensitive data1, a clear signal that security and governance are no longer optional, they’re essential.
The Expanding Threat Landscape
The numbers tell the story:
- In 2025, AI model attacks overtook malware as the top security concern, with nearly one in four organizations citing it as their biggest risk.2
- 25% of organizations will move Generative AI use cases from pilot to production without comprehensive risk assessments, exposing fragile deployments to unseen threats.1
- 49.2% of global partners see emerging threats, including AI-driven attacks, as a major challenge in cybersecurity. 3
The risks are evolving as fast as technology itself:
- Data Poisoning: Attackers corrupt training data to subtly manipulate model behavior.
- Model Theft & Inversion: Bad actors probe APIs to reverse-engineer models or extract sensitive data.
- Prompt Injection & Jailbreaks: Manipulative inputs cause large language models (LLMs) to produce harmful or unauthorized outputs.
AI is rewriting the rules of engagement, and the threat landscape with it.
Building Resilience: Security for AI in Action
Defending AI requires more than adding another security layer, it means integrating protection into every stage of the AI lifecycle. Here are five key measures to consider:
- Secure the Data Pipeline: Validate and sanitize training data to prevent corruption or bias.
- Harden Model Access: Implement API authentication, rate limiting, and continuous monitoring.
- Embed Model Governance: Maintain audit trails, documentation, and traceability for every model version.
- Test Continuously: Conduct red-teaming and adversarial testing to identify vulnerabilities before attackers do.
- Train and Educate: Build security awareness into every AI and data science team.
Organizations are taking notice and action. According to IDC, companies are 2.5x more likely to increase their security budgets in response to GenAI-related risks than to reduce them1.
Let’s consider Cybersecurity Awareness Month as a rallying point. As AI becomes central to business strategy, securing innovation must become a shared priority.
At TD SYNNEX, we’re helping partners lead the charge through our Destination AI™ framework, enabling them to design, deploy, and secure AI solutions with confidence. Together, we can help customers innovate faster, protect smarter, and build AI systems they can trust.
Ready to take the next step? Explore Destination AI™ and our Cyber Range to see how you can strengthen your AI security strategy. Or head to Channel Academy for a Global Specialized Skills (GSS) course covering three strategic focus areas of Destination AI™, AI Factory, Agentic AI, and Security for AI, with an in-depth look at five core domains of Security for AI.
1 IDC FutureScape: Worldwide Security and Trust 2025 Predictions
2 IDC AI Life-Cycle Trends Survey, 2025: Uncovering Insights and Challenges
3 TD SYNNEX fourth annual Direction of Technology report
