There’s a dangerous narrative taking hold in cybersecurity right now—that our biggest emerging threat is a flood of “AI slop.” The assumption is that low-effort AI-generated malware, automated phishing, and machine-produced noise are overwhelming our defenses.
Let me be clear: We don’t have an AI slop problem. We have a foundation problem.
Yes, AI will increase speed, volume, and automation. Yes, it will make bad actors more efficient. And yes, the most recent cry that “we have a prioritization problem” rings true—but it has always been true. The deeper issue is that we continue to build, operate, and defend our most critical systems on a foundation of quicksand.
AI is not creating that instability. It is exposing it. AI is revealing that too many of our fortresses were built on unstable foundations in the first place.
A few years ago, CISA launched its Secure by Design initiative, putting forward what should have become a fundamental reset in how software is built and secured. The work led by Bob Lord, Jack Cable, and the broader CISA team has been visionary. Their message is exactly right: the burden of security has to shift away from the end user and back toward the organizations designing, building, and selling technology.
That is the right mission. The problem is execution.
Across too much of the public and private sector, Secure by Design has become something organizations endorse faster than they operationalize. Some vendors sign pledges, publish statements, add tooling, and point to periodic NIST, CMMC, or compliance assessments as evidence that the underlying structure is sound.
But compliance does not equal resilience. A pledge does not remediate technical debt. And a secure development framework does not matter if the organization treats it as a communications exercise rather than an engineering discipline.
The irony is hard to ignore: Some of the companies quickest to align themselves with Secure by Design principles are also among the first vendors to be plagued by recurring, high-profile vulnerabilities over the last decade.
The issue is not that we lack frameworks. The issue is that too many organizations refuse to invest the time, capital, and accountability required to fix the underlying foundation.
This is why the emergence of AI-assisted offensive security is such a paradigm shift.
The alarming part is not that AI can invent some impossible new attack path. It is that AI can move faster through the same old weaknesses humans have been finding for decades: unpatched systems, brittle legacy architectures, misconfigurations, weak identity controls, insecure defaults, exposed services, poor asset visibility, and business logic flaws that never show up cleanly in a checklist.
AI does not need magic. It needs a search space. And the public sector has given it one.
The real shift is speed and scale. AI-enabled offensive tooling can help identify relationships between systems, prune dead ends faster, chain exposures together, and turn overlooked weaknesses into viable attack paths. The vulnerabilities may not be new. However, the pace at which they can be found, connected, and exploited is.
That is what should concern every agency leader, CISO, CIO, and mission owner. AI is not the root cause; it is the accelerant. It is the pressure test. It is the thing revealing that the foundation was weaker than we wanted to admit.
You cannot counter an adaptive offensive capability with static defenses, annual assessments, or compliance-driven scans alone.
The only way to find and fix deeply chained, context-dependent vulnerabilities before an adversary weaponizes them is to apply an adversarial mindset continuously. That means testing systems the way attackers actually approach them: creatively, iteratively, and from the outside in.
This is where the global hacker and security researcher community becomes one of the public sector’s most critical assets.
Hackers do not think in checklists. They think in exploit chains. They understand how small flaws compound. They look for the business logic weakness, the forgotten subdomain, the exposed API, the misconfigured identity flow, the legacy system no one wants to touch, and the unintended path between them.
This means human creativity matters even more in an AI-enabled world.
When skilled researchers are equipped with modern tooling, clear rules of engagement, safe harbor, and properly scoped programs, they become a continuous pressure test against the foundation. They bring the speed of technology, but with judgment, curiosity, and context that automated scanning alone cannot replicate.
That is how defenders flip the script.
When an AI-enabled actor can exploit a deeply buried legacy weakness before your morning coffee, the traditional “wait-and-see” approach to vulnerability management becomes a national security liability.
Public sector organizations cannot afford to treat vulnerability discovery as an occasional event. They cannot rely solely on periodic assessments or, worse, the hope that known issues will remain undiscovered long enough to make it through the next budget cycle.
The model has to change. Security has to become continuous. Testing has to become adversarial. Remediation has to become measurable. And agencies need trusted ways to bring external security talent to bear against real systems, under controlled conditions, before adversaries do.
That urgency is exactly why Bugcrowd invested so heavily in achieving FedRAMP Moderate Authorization.
Federal agencies should not have to choose between speed and trust. They need a path to securely engage vetted researchers against scoped assets, identify the vulnerabilities that matter most, and continuously reduce risk across the systems that support public missions.
The public sector does not need another framework sitting on a shelf. It needs a new theory of security that puts the global hacker community to work at the speed the threat now demands.