Agentic AI promises autonomous threat detection and process automation at machine speed — but introduces new security risks, unseen attack surfaces, and governance challenges that CISOs must address.

AI is becoming integral in cybersecurity, and companies and VCs are logically investing AI-based defenses against cyberattacks

Recent findings of AI ecosystem insecurities and attacks show how important MLSecOps is to securing AI strategies from complex and often highly concealed risks.

For all its complexity, encryption and obfuscation, the weakest link in AI security is not AI itself but the humans behind it.

Understand the major threats that large language models pose and apply effective defensive measures to deploy AI securely.

The critical question is no longer whether AI will be weaponized, but how soon organizations can adapt to counter it.

Agentic AI redefines how to secure digital ecosystems, where data pattern recognition and decision-making need to happen in real time.

As AI systems become increasingly complex, companies must continually refresh security components to ensure infrastructure security and employee training remain top priorities.

A new report from the Paladin Global Institute, The AI Tech Stack: A Primer for Tech and Cyber Policy, breaks down how AI systems are built and where the biggest security risks live.

This is a practical look at how agentic AI is entering the enterprise and what every CISO should know to stay secure.

AI is already transforming the cybersecurity landscape. The question is whether we can secure that transformation in time. The intelligence layer is here. It’s powerful. And it’s vulnerable.
Now is the moment to reimagine what security looks like when intelligence is everywhere.

Supercharging your data analysis strategy with machine learning, data science, and custom-trained LLMs can unlock a higher level of threat detection and a deeper understanding of organizational risks.

Here are five ways the industry can more effectively secure AI.

In a recent case tracked by Flashpoint, a finance worker at a global firm joined a video call that seemed normal. By the end of it, $25 million was gone. Everyone on the call except the employee was a deepfake. Criminals had used AI-powered cybercrime tactics to impersonate executives convincingly enough to get the payment approved.

AI is accelerating cyber threats faster than enterprises can react, and most lack the cyber defenses needed to secure their AI-powered future.

Fraudsters are optimizing illicit models with underground datasets, while criminal vendors roll out increasingly sophisticated deepfake generation and prompt engineering services.

Identity is the essential control plane for agentic AI security, redefining enterprise defenses amid rising credential-based breaches.

Security leaders must become AI cheerleaders, risk experts, data stewards, teachers, and researchers. Here’s how to lead your organization toward more secure and effective AI use.

LLM agents with too much agency may act on unclear or adversarial inputs, risking unauthorized actions and harming security integrity.

As companies move from narrow to generative to agentic and multi-agentic AI, the complexity of the risk landscape ramps up sharply. Existing AI risk programs—including ethical and cyber risks—need to evolve for organizations to move fast without breaking their brand and the people they impact. 

Without new frameworks, agentic AI threatens to overwhelm identity systems with complexity and risk.

As AI becomes the weapon of choice for cybercriminals, defenders must rethink their own approach to artificial intelligence. Automation alone is no longer enough. The future lies in agentic AI—intelligent systems capable of planning, learning, adapting, and acting on behalf of human operators.

AI acts like Pac-Man—devouring sensitive data across clouds, apps, and copilots. Varonis analyzed 1,000 orgs and found 99% have exposed data AI can access, exposing them to data risks.

Blocking access to public AI applications is not enough to stop employees from putting data at risk

AI automation is a top priority for CISOs, though data quality, privacy, and a lack of in-house expertise are common hurdles