How We're Building AI That Security Teams Actually Want to Use

We all know this: AI is transforming cyber threats. Attackers now use it to find vulnerabilities faster, automate complex attack chains, and evade detection with unprecedented sophistication.

AI presents many opportunities for defenders as well, however we see the security industry wrapping AI around traditional tools and calling it innovation.

At Nemesis, we took a different approach: we built an AI operator that actually runs security validation, not just reports on it.

The Current State of AI in Security

The cybersecurity industry is experiencing an AI gold rush. Everyone's racing to add "AI-powered" features, but many are missing the mark:

The Reporting Focus: Many vendors use AI primarily to generate more eloquent reports about the same vulnerabilities. It's impressive technology applied to the wrong problem - like using a supercomputer to organize your email.

The Integration Challenge: Some solutions simply connect LLMs to existing security tools. While this can add value, it often just repackages old capabilities with conversational interfaces.

The Transparency Gap: "Our AI found critical issues!" sounds great until you need to understand how, why, and whether you can reproduce the findings. Many AI implementations operate as black boxes, making verification difficult.

The Production Limitation: Most AI-enhanced security tools require isolated test environments because they can't safely operate in production. This limits their usefulness for continuous validation.

A Different Approach: Building an AI Operator

We took a step back and asked: What if AI could actually operate security validation, not just extend the list of problems you already have?

To prove the positive instead of the negative is hard but the right approach in security! This led us to build an AI that runs an actual BAS platform, makes intelligent decisions, and takes meaningful actions - while remaining transparent and auditable by a human operator. If it's not repeatable reliably, it's worthless! Unfortunately that counts for the majority of automated pentest agents today and in the near future.

The Security Expertise Gap

Every security team faces the same challenge: there's too much to defend and not enough expertise to go around. Elite security professionals who can design meaningful tests, interpret complex threats, and optimize defensive strategies are rare and expensive.

This expertise gap is what we're solving with AI. Not by replacing these professionals, but by making their knowledge and methods accessible to every organization. The Nemesis team members are experienced experts in the field of offensive security, encoding their knowledge and approaches into the platform.

Three Ways We're Using AI Differently

1. From Threat Intelligence to Actionable Assessments

The Common Approach: Use AI to summarize and categorize threat reports - helpful for awareness but stops short of action.

Our Implementation: Our AI translates threat descriptions, detection rules, and any kind of TTP collection directly into executable security assessments. And even for new threats we can synthesize novel attack techniques, so when a new ransomware variant emerges using Windows Print Spooler for privilege escalation, our AI creates the actual proof-of-concept to validate if you're vulnerable, complete with safe execution and cleanup procedures.

The Difference: It's one thing to know about a threat or use a library of known attacks; it's another to have an automated test running within hours of its discovery.

2. Custom Validation at Scale

The Common Approach: Run standard vulnerability scans and use AI to tailor the reports to different audiences.

Our Implementation: AI that creates client-specific atomic tests. MSSPs describe their needs - "validate that our client's payment processing application logs authentication failures to their SIEM" - and our AI generates the complete, production-safe test.

The Difference: Instead of finding the same vulnerabilities everyone has, we're validating the specific controls that matter to each organization.

3. From Assistant to Operator

The Common Approach: AI chatbots that answer questions and search documentation - useful but limited to advisory roles.

Our Implementation: An AI operator that actively manages the security validation lifecycle. It monitors threat feeds, schedules appropriate tests, interprets results in context, and generates role-specific communications. When a critical vulnerability is announced, it's already validating your defenses.

The Difference: You don't just get advice on what to do - the AI operator handles the entire workflow while maintaining full transparency.

The Key Principle: Transparency and Trust

One area where we fundamentally diverge from common AI implementations is transparency. Security teams need to understand and verify what's happening - not just trust an algorithm.

Our Approach:

• Every AI decision is logged and explainable

• All generated tests are human-readable and deterministically reproducible with a few clicks

• Results include clear reasoning and evidence

• Full audit trails satisfy both security and compliance needs

This isn't just about building trust - it's about building better security. When teams understand what's being tested and why, they can improve their defenses more effectively.

Real Impact: From Hours to Minutes

These aren't theoretical capabilities. The AI operator is transforming how teams work:

Before AI Operator:

• Security engineer spends 2 hours weekly reviewing threat intel

• 1 hour selecting relevant tests

• 30 minutes scheduling assessments

• 2 hours analyzing results

• 1 hour creating reports = 6.5 hours per week on BAS operation

With AI Operator:

• AI continuously monitors threat feeds

• Automatically selects and schedules relevant tests

• Provides pre-analyzed, contextualized results

• Generates role-specific reports instantly = 30 minutes per week reviewing AI decisions

One CISO told us: "It's not just about time savings. The AI operator catches things we would have missed and tests things we wouldn't have thought to validate."

What's Next: Fully Autonomous Security Validation

We're pushing the boundaries of what an AI operator can do. The next evolution will handle complete security validation workflows autonomously:

Dynamic Attack Chain Generation: Tell the AI operator "validate all paths to customer database access" and it will:

• Map your environment and identify assets

• Design multi-stage attack chains

• Execute them safely with full rollback capabilities

• Document every step for compliance

• Automatically re-test after remediation

Intelligent Campaign Orchestration: The AI operator will run themed validation campaigns based on your risk profile:

• "Ransomware readiness week" with escalating sophistication

• "Supply chain validation" targeting your specific vendors

• "Insider threat simulation" based on your access controls

Predictive Validation: By analyzing patterns across all deployments, the AI will predict and test for attacks before they're hitting the environment by utilizing threat intel and other signals to identify what matters for the specific organization.

Crucially, this isn't a black box. Every decision is logged, every test is auditable, and human oversight remains paramount. It's augmented intelligence that shows its work.

Why This Approach Works

We've found success by focusing on three core principles:

1. Operational Focus Rather than using AI to enhance reporting, we use it to improve actual security operations. The measure of success isn't how good the output looks, but how effectively defenses are validated.

2. Production-Safe by Design Every AI-generated test is designed for safe execution in production environments. This enables continuous validation rather than periodic assessments.

3. Augmenting Human Expertise Our AI multiplies the effectiveness of security teams rather than trying to replace them. It captures and scales expert knowledge while keeping humans in control of critical decisions.

Moving Beyond AI Hype to Real Value

The security industry is at an inflection point with AI. We can continue down the path of adding conversational interfaces to existing tools, or we can reimagine how AI can fundamentally improve security operations.

At Nemesis, we've chosen to focus on the latter:

• Beyond report generation to active security validation

• Beyond black boxes to transparent, auditable operations

• Beyond test environments to production-safe continuous testing

• Beyond AI assistants to AI operators that handle complete workflows

The result? Security teams that spend less time on repetitive tasks and more time improving their actual security posture. Organizations that can validate defenses continuously rather than hoping their last pentest is still relevant.

This is what happens when AI is applied thoughtfully to security challenges - not as a marketing checkbox, but as a tool for genuine operational improvement.

Interested in seeing how an AI operator can transform your security validation program? Schedule a demo - we'll show you AI that enhances security operations, not just security reports.