Best Practices for Getting Ahead of AI Slop with Exposure Management
AI is reshaping the cyber threat landscape, scaling how vulnerabilities are discovered across the ecosystem. That creates real opportunity, but also more report volume. As discovery accelerates faster than validation, prioritization, and remediation, defenders are feeling the strain.
The question is no longer about whether enterprises receive more volume, but whether they have systems in place to continuously identify which findings actually matter.
This is where Continuous Threat Exposure Management (CTEM) comes in. CTEM is a framework that gives organizations a roadmap for a clear, trusted, and continuously updated view of real-world exposure.
Recent conversations in the community highlight this tension, including curl’s decision to step back from its bug bounty program. Their experience reflects a broader challenge and why program design and workflow matter more than ever in an AI-driven world.
The Operational Reality of Open Source Security
Open source security depends on sustained, proactive effort, and the curl project is a standout example of responsible stewardship in practice. Despite limited resources, curl has made a deliberate investment in security by maintaining a dedicated team focused on vulnerability handling and long-term risk reduction. That commitment reflects a deep responsibility to the millions of users and organizations that rely on open source software as critical infrastructure.
At the same time, operating with finite resources means that excess noise in vulnerability intake is felt immediately. Time spent triaging low-quality or false reports is time not spent fixing real issues, improving resilience, or advancing the project. This is not unique to curl. It highlights a broader operational constraint faced by open source communities at global scale, especially as AI increases the speed and volume of discovery.
Designing Exposure Management Programs That Scale in an AI-Driven Environment
The experience of the curl project, including the challenge of being overwhelmed by a surge of AI-generated noise, looks meaningfully different from what we see across many other security programs.
That difference is not about intent or commitment. Open source programs like curl often operate under a fundamentally different model than commercial enterprises, with lean or volunteer teams and fewer structural buffers between incoming submissions and manual validation. In an AI-driven environment, those constraints surface quickly.
Across the broader ecosystem, programs that scale effectively tend to follow a common set of Continuous Threat Exposure Management best practices. These practices help absorb AI-driven volume, preserve signal, and ensure human effort stays focused on reducing real risk rather than sorting through noise.
Best Practice 1: Establish clear program policy and design to preserve signal
For programs experiencing elevated noise, we recommend taking a first-principles approach and analyzing where researchers are most often confused. This typically means identifying recurring patterns in submissions and updating program guidance or product documentation to address those misunderstandings directly. For example, repeated reports framed as authentication issues may reflect a misunderstanding of how the product behaves. Clarifying that behavior upfront can help researchers avoid common pitfalls and reduce noise.
As AI accelerates vulnerability discovery, programs that remain stable also avoid trying to absorb everything. They continuously refine what is in scope and tune bounty incentives so rewards align with risks and exposure levels they actually want reported.
Best Practice 2: Reduce reliance on manual validation
When 100% of incoming submissions flow directly into a small or volunteer review queue, triage quickly becomes a bottleneck. Most programs on the HackerOne platform use Hai’s growing team of AI agents, combined with managed triage, to filter noise, remove duplicates, surface credibility signals, and prioritize what warrants deeper investigation. This structure preserves scarce human effort for validation, remediation, and long-term risk reduction rather than initial sorting.
Best Practice 3: Pair AI scale with human judgment
AI alone does not solve the problem. Programs that scale effectively combine AI-driven analysis with human oversight, ensuring speed does not come at the expense of trust or accountability. Most customers on the HackerOne Platform leverage managed triage to bring human-in-the-loop governance, enabling faster validation while preserving confidence in what gets escalated and acted on.
Together, these best practices align closely with the principles of Continuous Threat Exposure Management. They shift the focus from managing raw findings to maintaining a clear, continuously updated view of real-world exposure as environments evolve.
What Comes Next: Agentic Validation in Practice
To continuously remediate what truly matters, validation must keep pace with discovery.
We are moving in this direction with advancements in agentic validation that automatically checks scope, eligibility, duplicates, and priority, then uses past outcomes to recommend next steps. This helps teams focus attention quickly and move into remediation with confidence.
The future of exposure management is about continuously exposing what truly matters, despite rising volume, evolving tools, and changing attacker behavior. That is the problem we are focused on solving.
With the right architecture and support, bug bounties remain one of the most powerful tools for uncovering novel, hard-to-find exposures that drive real-world risk.
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