Cybrary

Your employees are on the front lines of AI risk, often without realizing it. From prompt injection and data leakage to deepfakes and malicious AI tools, today’s threats target people using AI every day. And as AI becomes embedded in workflows, the attack surface grows just as fast.

Understanding the most common AI threats employees face is the first step to protecting your organization, your data, and your customers. Cybrary's here to help: https://hubs.ly/Q046WqTm0

This is the new reality of AI cybersecurity for employees. Attackers don’t always need to break into systems when they can pressure people into bypassing processes. Deepfake voice and video make that pressure feel more convincing, especially in distributed teams where you can’t just pop your head into someone’s office to confirm.

Check out our recent guide that trains your employees, step by step, to defend against the most common AI scams. https://hubs.ly/Q046W8Rt0

The Top 4 AI Threats today.

The pattern across them all? AI systems blur the line between input, data, logic, and ex*****on. If your threat model still looks like traditional app security, your team is completely missing the attack surface.

Upskill your team today: https://hubs.ly/Q046W6l20

It's finally time to move your AI system from development to the real world! But how does your team ensure the right safeguards are in place?

They'll need access controls, monitoring, governance, and protections against AI-specific threats like prompt injection, model abuse, and sensitive data exposure.

In Cybrary’s course, AI Security Lifecycle: Release, your team will learn how to securely deploy AI systems, establish the right controls, and prepare for safe operation in production environments.

Enroll them today: https://hubs.ly/Q046W1Q80

AI systems requirer robust security-focused evaluation, that includes testing for prompt injection, data leakage, model manipulation, and other AI-specific threats that traditional testing often misses. This is the whole goal of phase 4: Test & Evaluate.

In our corresponding course, your team will learn how to rigorously assess AI systems for security, reliability, and resilience, and ensure your models perform safely in the real world.

Enroll your team today: https://hubs.ly/Q046V_GB0

During development and experimentation, your team is building prompts, training pipelines, and integrations. And without the right security practices, this stage can introduce vulnerabilities like data leakage, insecure model access, or hidden prompt injection paths.

In our new course, AI Security Lifecycle: Dev & Experiment, your team will learn how to secure AI systems while they’re being built to ensure innovation doesn’t outpace protection.

Get started today: https://hubs.ly/Q046VRln0

In Augment & Fine Tune Data, part of our AI Security Lifecycle Collection, your team will learn how to strengthen your datasets while minimizing risks like data poisoning, bias, and unintended data exposure. https://hubs.ly/Q0460C8d0

Because when you secure the data, you strengthen the model. 🤖

Securing AI isn’t something you bolt on later; it needs to start at the very beginning with a rock-solid foundation.

In the Plan & Scope course from our AI Security Lifecycle Collection, your team will learn how to define objectives, identify risks, and establish governance before AI development begins.

If you’re building, managing, or securing AI systems, this course is for you: https://hubs.ly/Q0460l8d0

Now that you know the current landscape of AI Security Frameworks, let's take it a step further. Effective AI security programs integrate technical controls with human training. What might that look like?

Technical Layer: Implement technical controls for things like prompt injection detection (OWASP), model access controls (ISO 42001), jailbreaking monitoring (Cisco framework), and third-party AI assessments (NIST AI RMF).

Human Layer: Train employees on AI social engineering recognition, establish verification procedures for AI-generated requests, create safe reporting channels, and build cultural norms around questioning AI outputs.

Learn how to do build robust AI Security by combining frameworks with human training here: https://hubs.ly/Q0460d0H0

From the NIST AI Risk Management Framework guiding organizations on how to identify and manage AI risk, to NIST AI 100-2 advancing trustworthy AI practices, to MITRE ATLAS mapping real-world adversarial threats against machine learning systems, today’s AI security landscape is rapidly taking shape.

Understanding how these frameworks fit together is essential for cybersecurity teams responsible for protecting AI systems. Learn the ins and outs (and the shortcomings of each): https://hubs.ly/Q045Ksrf0

AI changes how risks are introduced and how quickly those risks scale. For GRC leaders, that means the traditional cycle of policy → audit → remediation is no longer enough.

AI systems evolve after deployment. Prompts change. Data sources shift. Models update. And employees experiment, often faster than governance can keep up.

The opportunity? GRC can become a strategic enabler. By building AI-aware risk frameworks now, organizations can adopt AI confidently, support innovation responsibly, and stay ahead of regulatory and reputational fallout.

Prepare your team today: https://hubs.ly/Q045yWTb0