Introduction: Clearing Up the Confusion

AI is reshaping every aspect of business, from customer service to software development and, of course, cybersecurity. But as AI adoption grows, so does confusion—especially when it comes to security.

At 7AI, we often hear a misconception about what we do: many assume we’re a security solution that prevents employees from putting sensitive data into AI models like ChatGPT. While securing AI is important, this is not what we do.

Instead, we focus on something fundamentally different: using AI to make security operations smarter, faster, and more autonomous. Our agentic security platform deploys AI agents to handle security investigations autonomously, reducing manual workloads and enabling security teams to focus on strategic risks.

In this post, we’ll break down the distinction between securing AI and using AI for security, why it matters, and how agentic security is transforming the way organizations handle cyber threats.

Securing AI: Preventing Data Exposure in AI Models

As organizations rush to integrate AI into their workflows, security teams face a critical challenge: how to ensure employees don’t put sensitive or proprietary data into AI models they don’t control.

This concern falls under securing AI, which includes:

• Data Governance & Policies: Preventing employees from exposing private data in AI tools like ChatGPT or Copilot.
• Security Controls: Deploying solutions like Data Loss Prevention (DLP) to block unauthorized AI usage.
• Model Security: Protecting proprietary AI models from adversarial attacks, tampering, and data poisoning.

Example: The ChatGPT Data Leakage Problem

A common example of securing AI is when a developer pastes source code or customer data into ChatGPT to get debugging help. If the AI model retains that input, sensitive information could be exposed in future responses or become part of the model’s training data.

To address this, companies implement usage policies, access controls, and AI-specific security tools. While this is an important challenge, this is not what 7AI does.

Using AI for Security: How AI Agents Enhance Security Operations

At 7AI, we focus on a different problem: using AI to improve security operations.

Security teams today are overwhelmed by a flood of alerts—whether from identity security tools, cloud security platforms, EDR systems, or SIEM solutions. Most of these alerts require time-consuming manual investigation, leading to burnout and missed threats.

This is where agentic security comes in. Instead of merely surfacing alerts, our AI agents take action by:

• Ingesting and analyzing security alerts from multiple sources
• Performing full autonomous investigations (e.g., gathering evidence, correlating data, and enriching context)
• Reaching conclusions and taking action (e.g., resolving false positives, escalating genuine threats, or - potentially -  automating remediation steps)

By offloading non-human work like repetitive security investigations, AI agents reduce alert fatigue, radically reduce investigation time, and enable security teams to focus on high-impact threats.

Example 1: Investigating Suspicious Identity Alerts

A traditional identity security investigation—like detecting a suspicious login or access attempt—requires a security analyst to:

1. Review the alert and check if the login attempt is from an unusual location, device, or time.
2. Cross-check logs to see if the user has successfully authenticated before from that location or if this is a first-time attempt.
3. Check whether the login is associated with other suspicious activity (e.g., privilege escalation or data access).
4. Determine whether the activity is benign (e.g., an employee traveling) or malicious (e.g., compromised credentials).
5. Decide whether to revoke access, reset credentials, or escalate for further review.

With agentic security, this entire process can be handled autonomously:

• A mission agent receives an alert and identifies it as identity-related and dispatches relevant agents.
• AI agents analyze login patterns and correlate them with past user behavior, threat intelligence, and company access policies.
• If a login attempt appears risky, the AI agent checks for additional suspicious activity (e.g., rapid file downloads, privilege escalation attempts).
• If a legitimate threat is identified, the AI agent automatically suggests access revocation, requires additional authentication, and alerts security teams with full investigative context.
• If it’s a false positive (e.g., the employee is on a business trip), the AI agent clears the alert without human intervention, closing a ticket with full documentation and context.

This approach eliminates alert fatigue, reduces manual investigation time, and ensures real threats are handled instantly.

Example 2: Automating Cloud Security Investigations

Cloud security alerts—such as unusual API activity, misconfigurations, or unauthorized resource access—are notoriously difficult to investigate due to the sheer volume of logs and interconnected systems.

A typical cloud security investigation might require an analyst to:

1. Review the alert (e.g., “Unusual access to an S3 bucket from an unknown IP”).
2. Gather context by checking cloud logs, correlating with IAM activity, and determining if the access aligns with normal usage patterns.
3. Assess whether it’s a misconfiguration, a legitimate business need, or an actual security breach.
4. Decide on remediation (e.g., restricting access, enforcing MFA, or rolling back permissions).

With an agentic security platform, this process becomes autonomous and real-time:

• AI agents get alerts about anomalous API calls or resource access and instantly check if similar activity has been seen before.
• If the action is new but not clearly malicious, the agent can query internal knowledge (e.g., has this user ever accessed this resource before? Is this tied to a recent policy change?).
• If the action is clearly malicious (e.g., detected access from a known threat actor IP), the AI agent can either suggest actions to be completed like revoking permissions, isolating affected resources or - when appropriate - automatically performing these actions.
• If it’s a misconfiguration, the AI agent flags it for correction and ensures policies are updated to prevent future issues.

This level of autonomous response ensures cloud security threats are addressed before they escalate—without requiring a security analyst to manually investigate every anomaly.

Why This Distinction Matters

Understanding the difference between securing AI and using AI for security is crucial for businesses looking to invest in AI security solutions.

At 7AI, we are not an AI governance company. Instead, we are pioneering agentic security—where AI doesn’t just assist security teams, it actively works as part of the team to investigate threats autonomously.

Conclusion: The Future of AI in Security

The rapid evolution of AI presents both risks and opportunities. While organizations must ensure that their AI models are used securely, they should also embrace AI’s potential to strengthen their security operations.

At 7AI, we’re leading this shift by building an AI-native security platform that goes beyond alert fatigue and manual investigations. If you’re ready to see how agentic security can transform your security operations, let’s talk.