Home About Black Hat Contact Essays Fireside Chats My Take News Alerts Q&A Reels RSAC Videocasts ☰
 

Essays

 

Guest Essay: Before AI agents takes hold, define who can challenge them and who owns the outcome

By Michael J Padilla-Pagan Payano

Early in my military career, I learned a simple lesson: when you give people a task, you also have to give them room to think.

Related: AI agents take initiative, unchecked

As a young “door kicker,” I made a point of telling my teams to ask why. Not because every decision needed a debate, but because the person closest to the work often sees something the person giving the order does not.

During a deployment in Afghanistan, after operating in the same area for three weeks, we received intelligence about a location that did not match what the team had seen on the ground. One of my team members asked, “Why are we accepting this intelligence as current? Where did it come from, how old was it, and who had vetted it?”

His question was valid. We had been moving so quickly from one operation to another that we had not stopped to verify the intelligence, revalidate the source, or revise the plan before moving. It reinforced a lesson I carried into business: rank carries responsibility, but it does not give a leader a monopoly on judgment.

GUEST ESSAY: AI coding assistants are putting open source in your code without declaring it

By Mike Pittenger

For years, software teams have managed open source risk in two familiar forms: licensing obligations and known vulnerabilities. AI coding assistants introduce a third risk that is harder to see.

Related: Where SBOMs fall short

They can place open source snippets directly inside source files without declaring a package, updating a build file or adding anything to a software bill of materials.

These hidden dependencies expose a weakening assumption behind established software controls: that third-party code arrives through a declared component. When it does not, the Software Composition Analysis tools and SBOM processes organizations rely on may never see it.

Where trails break

When a developer uses an open source package, the package normally appears in a manifest or dependency file. That creates a trail. SCA tools can identify the component, check its license and known vulnerabilities, and include it in the resulting SBOM.

GUEST ESSAY: AI can speed up communication, but it can also weaken human connection

By Esther Choy

The first warning sign came on stage.

Related: Carol Sturka declares her agency 

I had turned to ChatGPT to help organize research notes for an upcoming keynote. I was pressed for time and wanted help spotting patterns I might have missed. That seemed harmless enough. Then the tool offered to help write the speech.

I was tempted. I let it try.

The result was cohesive, polished and snappy. It was based on my own research. It sounded good. Maybe too good.

Then I stood in front of an audience and did something I had not done in more than 30 years of public speaking: I read from the page. The words were mine in one sense, but they had not passed through me in the usual way. For a moment, I stopped trusting myself.

GUEST ESSAY: How augmented reality (AR) can turn building images into ad space with no control

By Neil Mandt

Every major building in America has three things: a physical address, a legal owner, and an unmonitored attack surface.

Related: Sam Altman’s quest to usurp the browswer

That surface extends from the ground up through every floor, every facade, and into the airspace above — invisible, commercially exploited, and almost entirely ungoverned. It is the augmented reality layer: the digital space that maps directly onto the physical world.

Technology platforms currently occupy this space, placing content, advertising, and interactive experiences onto private property without permission, compensation, or any security accountability for what happens there.

In practice, this means someone can point a phone at your building and see digital content placed there — ads, experiences, or data collection — that you neither approved nor control.

GUEST ESSAY: Google’s 2029 deadline exposes readiness gap as move to quantum-safe crypto lags

By Rebecca Krauthamer

For years, quantum risk was easy for most institutions to treat as premature: real in theory, urgent someday, but not yet an operational problem. That is no longer tenable.

Related: AI spawns semantic attacks

Two developments this month brought the shift into focus. Google Quantum AI published research suggesting the computing resources needed to break widely used encryption may be significantly lower than earlier estimates assumed. Around the same time, researchers from Oratomic, Caltech and UC Berkeley described a plausible path for running Shor’s algorithm at a scale relevant to real-world security.

Most executives do not need to understand the technical details. They need to understand the takeaway: the threshold for quantum systems to threaten today’s cryptography is moving closer, faster and more credibly than many institutions planned for.

GUEST ESSAY: Real cyber risks arise when small flaws combine and alerts are viewed in isolation

By Jeremy Colvin

Security teams are drowning in signals. Alerts fire. Logs accumulate. Dashboards light up. Yet breaches still unfold quietly, often through a series of low-level actions that never trigger a single catastrophic alarm.

Related: How ‘observability’ drives security

Attackers do not rely on one silver bullet. They move incrementally. They probe. They chain together small weaknesses that, in isolation, look harmless. The problem is not a lack of data. It is a lack of context.

For years, security programs have optimized for visibility. Distributed systems and microservices expanded east-west traffic long before AI models amplified data movement further. Today, teams are asked to detect data exfiltration inside oceans of legitimate activity, while also worrying about proprietary information leaking into external AI systems. The volume has grown. The signal has not.

Signal vs. noise

A single vulnerability, a single anomalous login, or a single configuration drift rarely explains real risk. Even mature vulnerability frameworks, with precise CVE classification and continuous scanning, only describe theoretical exposure. They do not reveal which weaknesses are actually exploitable in a given environment.

GUEST ESSAY: The hidden risks lurking beneath legal AI — permission sprawl, governance drift

By Ariel Perez

In many law firms today, leadership believes their data is secure. Policies are documented, annual reviews are completed, and vendor questionnaires are answered with confidence. On paper, the safeguards look strong.

Related: The cost of law firm breaches

Yet in practice, few firms have a clear, current view of how their systems actually behave. That gap between written policy and operational reality has existed for years. The rapid adoption of AI tools is now bringing it into sharp focus.

Research assistants, document review platforms, and productivity copilots are arriving quickly in legal environments. They promise efficiency, and in many cases they deliver it. But these tools are often introduced into data environments where permissions have accumulated without review, access rules are poorly understood, and no one can clearly explain who has access to what, or why. Over time, that drift creates real exposure in how work is performed.