When deepfakes first exploded into the headlines, they felt like the ultimate AI nightmare. Fake videos of presidents saying things they never said. Fraudsters are cloning voices to trick banks. Celebrities “appearing” in videos they never recorded. For a while, deepfakes were the scariest AI threat out there.
But here’s the uncomfortable truth: deepfakes are scary. Agentic AI Threats are scarier.
And most people don’t realize it yet.
What is agentic AI? Benefits, challenges, and how to implement it
Deepfakes Are Content. Agentic AI Is Action.
Deepfakes manipulate media – video, audio, and images. They spread misinformation, spark confusion, and erode trust. But at the end of the day, they’re just files sitting on the internet. A fake video can’t move money, leak company-sensitive information, or hack into your systems. It still needs a human being to upload it, share it, or weaponize it.
Agentic AI flips that on its head.
These systems aren’t just content generators. They’re autonomous decision-makers. They don’t just write or recommend – they act. They can plan, execute, and coordinate across multiple platforms without a human typing commands.
That shift from output to autonomy is what makes agentic AI far more dangerous than deepfakes.
AI TRiSM (AI Trust, Risk, and Security Management): Enhancing AI Reliability and Security
Why Agentic AI Threats Are Riskier Than Deepfakes
Let’s break it down in plain terms:
1. Autonomous Cybercrime
Think of a phishing email. If ChatGPT writes one, it might trick someone. Annoying, yes. But now imagine an AI agent that:
- Find your CFO’s calendar,
- Hacks into Slack to impersonate a colleague,
- And automatically transfers funds through connected APIs.
That’s not just misinformation – it’s cybercrime at scale without a human hacker behind the keyboard.
2. Attacks That Scale Like Software
Deepfakes are one-offs. Each video has to be made and distributed. AI agents, on the other hand, can replicate themselves, run in parallel, and adapt in real time. A single malicious agent could launch thousands of personalized scams at once.
3. Decision-Making Power
A deepfake convinces you. An agent convinces itself.
It can analyze, test, learn, and escalate without waiting for a human. That’s a game-changer in both productivity and risk.
The Agentic AI Identity Problem
Here’s something few people outside of cybersecurity circles are talking about: identity.
Every AI agent needs an identity to access systems – whether through API tokens, cryptographic certificates, or other credentials. Unlike human employees, agents don’t have birthdays, biometrics, or MFA logins.
That means:
- Their lifespans are dynamic (an agent may exist for hours, days, or indefinitely).
- They often require sensitive permissions to function.
- They’re harder to track, audit, and de-provision.
In other words, if an agent goes rogue – or gets hijacked – most companies don’t have the tools to quickly cut off its access.
According to an Okta survey of executives, fewer than 1 in 10 organizations have a solid strategy for managing these “non-human” identities. Yet by the end of this year, the number of agentic identities is expected to exceed 45 billion – 12 times the human workforce.
Real-World Examples: This Isn’t Theory
- AI Doubles on Zoom: New tools now let an AI “clone” attend meetings for you. Convenient? Sure. However, it is also a dream for fraudsters – agents can impersonate employees in real-time on video calls.
- AI-Generated Social Engineering: Attackers are already using generative AI to launch highly personalized phishing campaigns at scale. Agentic AI enables this autonomy and adaptability.
- Autonomous Malware: Security researchers have shown that agents can write code, exploit systems, and even cover their tracks – without humans directing them at every step.
This isn’t the future. It’s happening right now.
Generative AI Security Risks & How to Mitigate Them
What Businesses Must Do – Now
If you’re deploying AI agents – or even experimenting – you can’t afford to treat security as an afterthought. Here are the top steps experts recommend:
- Identity-First Security
Treat every agent as its own “employee.” Give it only the permissions it needs. Provision and de-provision fast. Log every action. - Interoperability Without Blind Trust
Agents are powerful when connected—but integrations must follow standards like Model Context Protocol (MCP) to keep connections secure. - Visibility and Monitoring
You can’t secure what you can’t see. Businesses need real-time monitoring and anomaly detection to spot when agents are doing something unusual. - Governance and Policy
Establish clear rules before deployment. Who owns the agent’s actions? Who audits them? What happens if something goes wrong? - Regulation and Detection
Governments and enterprises must invest in deepfake detection and agentic oversight frameworks. Trust in digital identity depends on it.
A Comprehensive Guide - Defending Against Identity-Based Attacks
The Bottom Line
Deepfakes shook our faith in what’s real. But agentic AI threatens the systems we rely on to function.
Deepfakes spread lies. Agentic AI can move money, leak secrets, impersonate people, and scale attacks across millions of systems simultaneously.
We’re at an inflection point. Businesses are racing to deploy agents, often without proper security. Regulators are still playing catch-up. And attackers are already one step ahead.
The question isn’t whether agentic AI will impact your organization. It’s how soon—and whether you’ll be ready.