Security and Governance in the Age of Agentic AI

Agentic AI is no longer a futuristic concept — it’s already here. These aren’t simple chatbots anymore. These are autonomous agents that can schedule appointments, trade stocks, extract data, or even make purchases without waiting for a human to click a button.

According to Gartner, one third of enterprise applications will include agentic AI by 2028 — and that timeline is closer than it might initially seem. The potential is huge, but so is the risk.

This new level of autonomy brings with it not just innovation, but serious governance and security challenges. Unlike traditional rules-based software, agentic AI systems learn and adapt in real time — they interpret data, make decisions, and act on their interpretations. That flexibility is powerful — but also potentially dangerous.

In this post, I’ll walk through:

• The security threats facing agentic AI
• The governance challenges these threats expose
• A set of recommended safeguards for building trustworthy, secure AI

Understanding the Security Threats

I recently spoke with cybersecurity experts who work with agentic AI every single day — and they made one thing clear: many traditional AI vulnerabilities are magnified when agents are autonomous.

Here are the key threats you need to know about:

1. Agent Hijacking

What happens if an attacker sends commands into your agent and takes control — causing it to operate on behalf of the attacker instead of you?

One major entry point for this is prompt injection — where an attacker inserts malicious instructions that the AI interprets as valid. According to the Open Web Application Security Project, prompt injection is the number one attack type right now.

2. Model Infection

Just like software can be infected with malware, AI models themselves can be infected. Since most organizations don’t build their own models, they must trust and verify externally sourced ones.

3. Data Poisoning

Models are trained and fine-tuned using data. If someone subtly contaminates that data — even in small amounts — it can later distort decision-making in unpredictable ways. Think of it as putting a toxin into the water supply: you don’t notice it at first, but it eventually has wide-reaching effects.

4. Evasion Attacks

Evasion occurs when attackers manipulate the input, not the model itself. By disguising or altering key information, they can confuse the AI; causing it to misinterpret data and produce incorrect (or even dangerous) results.

5. Extraction Attacks

Threats aren’t just about input — they’re also about output.

Model theft can happen when attackers feed inputs piece by piece, observe the outputs, and reconstruct the model logic over time.

Even worse, a compromised agent might be tricked into revealing sensitive internal data, like credentials or customer information; sometimes through what’s known as a zero-click attack (i.e., the user doesn’t have to take any action for data to be exfiltrated).

6. Denial of Service

This classic attack, overwhelming a system with more requests than it can handle, applies to agentic AI as well. Flood your agent with requests, and it might become too busy to serve legitimate users.

Governance Challenges: A Cautionary Story

Security is only one half of the problem. Without proper governance, agentic AI can still behave in unpredictable or unacceptable ways.

To illustrate this, let’s walk through a hypothetical (but realistic) scenario:

A recruiting firm adopts an autonomous AI to:

• Read and evaluate resumes
• Schedule interviews
• Send job offers

Sounds efficient — until the AI sends an offer without human approval.

Questions arise:

• How much autonomy is appropriate?
• When should humans be in the loop?
• Can HR explain why the AI made that decision?

These questions point to core governance issues: autonomy vs visibility, fairness, and explainability.

In this story, the AI also showed bias — favoring candidates from certain schools due to unbalanced training data. As a result, the firm missed qualified applicants and ultimately faced a discrimination lawsuit.

And then the real question emerges: Who is responsible?

• The AI?
• The HR team?
• The vendor that built it?
• The people who deployed it?

There’s no simple answer — and that’s precisely why governance matters.

Recommended Safeguards

Agentic AI is powerful; but it must be both governed and secured. Below are practices that help bring both together.

1. Discover What You Have

You can’t secure or govern what you don’t know exists.

First, you must identify all AI instances across your environment — including unauthorized systems (often called shadow AI) where developers or teams spin up models without oversight.

2. AI Security Posture Management

Once identified, every AI instance should be evaluated for:

• Proper authentication
• Data protection
• Encryption
• Permission boundaries
• Public exposure risk

If an instance is exposing sensitive data or services, it must be hardened immediately.

3. Penetration Testing

Just as we test networks and applications, we must stress-test AI models.

Simulate:

• Prompt injection
• Evasion attempts
• Model-based attacks

Real tests reveal vulnerabilities before attackers do.

4. Runtime Protection (AI Firewalls)

A runtime layer, or AI-specific firewall, analyzes inputs and outputs in real time. For example:

• Reject suspicious or malicious prompts
• Detect unusual data extraction
• Block unwanted actions before they execute

This adds a layer of defense between users and the agent’s decision-making process.

Governance: The Three Key Pillars

Effective governance isn’t an afterthought — it’s foundational.

Here are the pillars every organization needs:

1. Lifecycle Governance

Ensure that AI agents are:

• Approved before deployment
• Documented
• Continuously reviewed from inception to decommissioning

2. Risk & Regulation

Evaluate agents for:

• Compliance with legal and ethical standards
• Bias mitigation
• Fairness testing
• Transparency requirements

3. Monitoring & Evaluation

Ongoing assessment is critical:

• Track agent performance
• Watch decision patterns
• Detect drift over time
• Report results in an accessible dashboard

A centralized dashboard gives executives and auditors visibility into AI operations and compliance.

Security + Governance = Trustworthy AI

Here’s the key takeaway:

• Governance without security is fragile. You can set rules, but if the model can be hacked or poisoned, those rules fall apart.
• Security without governance is blind. You can block attacks, but if the AI itself is biased or unexplainable, you’re protecting something fundamentally broken.

To build trustworthy agentic AI, you must integrate governance and security; not treat them as optional extras.

Without this integration, autonomous agents may not just be unpredictable; they may be uncontrollable.