Tag Archives: AI

Business

Your Insurance Carrier Is Now Your Auditor

Cyber insurance used to be a safety net. In 2026, it’s also a security exam. And the test is no longer optional.

There was a time when buying cyber insurance was simple.
Fill out a questionnaire. Pay the premium. File the PDF somewhere between your business license and that printer manual nobody has opened since 2019.

Then ransomware happened.
And insurers realized something uncomfortable: companies were buying protection against cyberattacks without necessarily doing much to prevent them. So the questions changed.

Welcome to the new underwriting reality

The 2026 underwriting bar is increasingly specific:

  • Over 90% of applications ask detailed MFA questions, including RDP, VPN, cloud, and privileged accounts. SMS-based MFA is increasingly flagged as insufficient.
  • Over 80% of carriers require EDR or MDR across all endpoints.
  • Immutable backups are becoming a hard requirement, not a nice-to-have.
  • Documented proof is replacing checkbox answers. Some renewals now involve independent audits or evidence reviews.

These figures reflect the underwriting picture in our brief; requirements vary by carrier and policy. The message, however, is clear: saying you have security is no longer enough. You need to prove it.

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AI

One Question, Many Minds: What I Learned Building a Multi-LLM Application

A practical follow-up to “The Power of Many”—from a small Ollama experiment to a workbench for comparing local and cloud LLMs.

A couple of years ago (2024… but it feels like 217 years ago in the AI world), I wrote about an idea that felt slightly unusual at the time: why settle for one large language model when you can ask several?

The argument was straightforward. Different models have different strengths. One might be better at explaining a tricky concept, another at writing code, and a third at spotting the holes in an otherwise convincing answer. Asking more than one model gives you something a single answer cannot: a comparison.

That was the idea behind my little open-source project, Multi-LLM-at-Once.

The original version was modest. It queried local models through Ollama and displayed their answers together. Useful, but still very much an experiment.

Since then, the experiment has become a rather more serious tool.

The question is no longer “Which model is best?”

This is where I think many of us are asking the wrong question.

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AI

Why Code Verification Is the Real Bottleneck Now — and What Developers Should Do About It

For most of software history, writing code was the expensive part.

A developer might spend hours or days implementing a feature, while review was a relatively small step at the end. AI coding tools have quietly flipped that equation. A model can now draft a function in seconds and produce an entire feature in minutes. In other words, producing code become cheap. Way too cheap. But the review (hopefully with human in the loop) is still expensive.

The bottleneck hasn’t disappeared. It has moved.

Today, the scarce resource is increasingly the work that comes after code generation: reading the code, understanding its behavior, testing it, identifying what is wrong, and deciding whether it is safe to ship.

This isn’t simply a matter of perception. Research on AI-assisted development has found that delivery stability can decline as teams adopt more AI, while developer trust in AI-generated code remains far from universal. In one controlled study of experienced open-source developers, AI assistance actually made participants about 19% slower on real-world tasks—even though they expected to be faster and believed afterward that they had been.

The extra time went into prompting, reviewing generated code, debugging it, and fixing things that didn’t quite work.

The lesson isn’t that AI coding tools are bad.
Quite the opposite: they are extremely good at making code cheap.

The problem is that everything downstream of code generation—understanding it, validating it, and trusting it—hasn’t become cheap at the same rate.

That changes where engineering teams need to invest.

Verification Is a Stack of Filters, Not a Single Gate

Code verification isn’t one activity.
It’s a stack of increasingly expensive filters, each designed to catch problems the cheaper layers missed:

  • Type checkers and linters — fast and inexpensive, catching mechanical mistakes and violations of known rules before code runs.
  • Automated tests — validate behavior that static checks cannot. A function can be perfectly typed and still return the wrong answer.
  • Static analysis and security scanning — look for deeper structural, reliability, and security problems that ordinary linters and tests may miss.
  • Human review — evaluates things machines struggle to judge reliably:
    Is this the right design?
    Does it fit the architecture?
    Does it solve the actual problem?
    Will someone be able to maintain it six months from now?
  • Production monitoring — the final safety net, detecting problems that survived everything before it.

These filters fall broadly into two categories.

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Business

Compliance Is Not a Tax. It’s a Sales Accelerator

Most founders treat compliance as a necessary evil — somewhere on the spectrum between filing taxes and getting a root canal. You spend three months scrambling for an audit, pay consultants thousands of dollars, take endless screenshots, answer 200 questions, receive a shiny PDF… and instantly forget about it until next year.

That’s completely backwards.

The best engineering and GTM teams don’t build compliance because an auditor asked for it. They build it because customers buy faster when they trust you.

When you stop treating SOC 2, ISO 27001, HIPAA, or CMMC as part of your IT maintenance budget and start treating them as core go-to-market features, your sales velocity changes overnight.

The Hidden Tax: The 24-Day Black Hole

Every startup optimizes for engineering velocity. Founders obsess over CAC, LTV, net retention, and burn rate. Then, a $250k enterprise deal hits a wall:

“Our InfoSec team just has a few quick questions before procurement can sign.”

Three weeks later, your lead architect is still filling out Question #147 of a security questionnaire that looks like it escaped Microsoft Excel in 2004.

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Business

Understanding the CMMC Pause: Key Changes and Action Steps

On July 13, 2026, the Department of War announced the immediate suspension of CMMC Phase II requirements. The move was memorialized in a memo dated July 10, 2026, signed by DoW Chief Information Officer Kirsten Davies. Those requirements had been scheduled to take effect on November 10, 2026, and would have pushed many contracts handling Controlled Unclassified Information (CUI) into mandatory third-party C3PAO assessments.

The stated goal is straightforward: reduce compliance barriers for small, medium, and non-traditional businesses so the Defense Industrial Base can expand faster under the Department’s current acquisition priorities.
A 60-day CMMC Reform Task Force review is now underway, including a public Request for Information seeking industry input on cost drivers and administrative burden. Phase I self-assessment requirements remain firmly in place.

This is not a free pass.
It’s a pause on one layer of bureaucracy — not a suspension of the underlying security obligations.

What Actually Changed (and What Didn’t)

Suspended

  • The November 2026 transition to Phase II — third-party Level 2 assessments as a condition of award in many cases.
  • Pending and future CMMC implementation milestones (including Phase III and IV) that would have required C3PAO or DIBCAC assessments.
  • During the review period, contracting officers are limited to requiring only Level 1 (Self) or Level 2 (Self) assessments in new procurements.
  • Existing contracts that already contain Phase II language will have that language removed by modification, either before the next option period or at the next scheduled administrative update.

Still fully in force

  • Phase I self-assessments and annual affirmations in SPRS.
  • DFARS 252.204-7012 obligations to protect covered defense information and implement NIST SP 800-171 controls.
  • Contractual cybersecurity requirements that primes flow down to subcontractors.
  • The Department of Justice’s Civil Cyber-Fraud Initiative, which continues to treat inaccurate self-assessments and false claims seriously.

The official release is worth reading in full: Forging the Arsenal of Freedom: Department of War Suspends CMMC Phase II Requirements. The SBA has also publicly backed the move, arguing the prior framework was pushing small firms out of the defense supply chain.

In short: the certification theater got paused. The requirement to actually protect the data did not.

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Business

GRC Platforms vs. Managed Compliance: Understanding the Gaps

TL;DR

A GRC platform tells you where you stand. A managed compliance service (in theory) does the standing-up.
Before you sign either contract, make someone in the room answer this out loud:
when a control fails at 2 a.m., who fixes it, how fast, and how do we know it actually happened?
If nobody can answer that today, that’s the gap you’re actually buying a solution for — not the framework name on the badge.

Btw, If the 2 a.m. question above didn’t have a clean answer, it’s worth a look at what a fully managed model covers versus what’s still sitting on your team’s plate. Check out the Espresso Labs platform

If you’ve bought a GRC (governance, risk management, and compliance) tool in the last five years, you’ve probably had this moment: the dashboard is green, the auditor is happy, and yet you still have an unencrypted laptop sitting in someone’s bag, a service account with a password from 2021, and a patch cadence that only exists on paper. The tool told you the truth. It just didn’t fix anything.

That gap — between visibility and operationalization — is worth thinking about carefully, because it’s where a lot of compliance budget quietly goes to die.

What GRC platforms like Vanta and Drata actually solve

Vanta and Drata deserve real credit. They replaced the compliance shared-spreadsheet — the one where “evidence” meant a screenshot pasted into a folder six weeks before the audit. What they do well:

  • Pull control status from the tools you already run via read-only integrations
  • Map passing/failing checks to a framework (SOC 2, ISO 27001, HIPAA, CMMC, etc.)
  • Automate evidence collection so audit season isn’t a fire drill
  • Alert you when something drifts out of policy

For a company with a mature security function — people who own EDR, MDM, SSO, backup, and vulnerability management day to day — this is exactly the layer you want. It turns “prove you’re compliant” from an annual archaeology project into a live, queryable system.

The quiet assumption baked into that model

Here’s the thing these platforms assume, and it’s almost never stated out loud in the sales process: you already have the underlying security program.

The dashboard reports on controls; it doesn’t implement them, enforce them, or fix them when they break.

When Vanta flags an unencrypted disk, or Drata flags a stale account, that finding lands in a queue. Someone — on your team, or a vendor you’ve separately hired — has to:

  1. Triage it
  2. Actually go fix it (device by device, user by user)
  3. Confirm the fix took
  4. Make sure it doesn’t regress next sprint

For a company with a five-person security team and a mature IT function, that’s Tuesday. For the median SMB or mid-market company — the ones without a dedicated security engineer, running IT through an MSP or a stretched-thin generalist — that queue just grows. You end up with excellent visibility into a program that isn’t actually being run.

This is also why “we’re SOC 2 compliant” and “we’re actually secure” are not the same sentence. A dashboard can be green because your controls are well-enforced, or it can be green because someone knows exactly which checkboxes the auditor samples. Both look identical from the dashboard.

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AI, Business

Outsmarting Cyber Threats: SMBs Need Multi-Layered Security

If you run a small or mid-sized business, you’ve probably told yourself some version of this story: “We’re too small to be a target. Hackers go after the big fish — banks, hospitals, Fortune 500s.”

I get it. I used to think that too. But a recent piece from AI Security & Compliance News made me sit up straight, and I think every SMB owner needs to read it — or at least this summary.

The rules just changed

For decades, cybersecurity followed a predictable rhythm: attackers find a new trick, defenders patch it, attackers find another trick, repeat. Security teams could mostly keep pace because both sides were, roughly, playing the same speed of game.

That rhythm is broken. Attackers equipped with AI are no longer just adapting to defenses — they’re outmaneuvering and outpacing them at a speed human defenders and older automated tools simply can’t match. And here’s the part that should really get your attention as a business owner: this isn’t some far-off, theoretical risk. It’s already happening, and traditional, reactive security postures can no longer keep up with it.

Wait — attacks without malware?

Here’s the stat that stopped me cold.
Roughly 79% of attacks today don’t use malware at all.

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Business

What a Law Firm’s Ransomware Nightmare Can Teach Your Startup

I spend most of my time around developers who think “security” means:
npm audit
and a .env file that’s definitely in .gitignore file.

If you browse our (= Espresso Labs) pitch to law firms, you realized: the threat model we’re describing for a 40-person law firm is identical to the threat model for your bootstrapped SaaS, your dev agency, or your local accounting shop.
Only the data changes.
The attacker’s playbook doesn’t.

Here’s what I learned, and what I think every SMB owner and every engineer who’s ever been “the security person by default” should take from it.

Law firms are basically unencrypted API keys with a bar license

Think about what a law firm actually is, technically: a small team with admin access to an enormous amount of high-value, high-leverage data — M&A deal terms, litigation strategy, medical records, wire transfer instructions — protected by, in a lot of cases, the same IT hygiene as your uncle’s dentist office.
(It’s ugly – I know)

That mismatch between value of data and maturity of defenses is exactly what makes a target attractive, and it’s the same mismatch that makes early-stage startups attractive. You might not have client trust funds, but you’ve got:

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Business

The Cheapest Way Into Your Business Isn’t Malware. It’s a Phone Call.

It’s 4:45 on a Friday.
Someone on your finance team gets a call.
The voice is calm, knows the CFO’s name, references a real invoice number, and just needs “one quick correction” on a wire transfer.
Ninety seconds later, the money is gone.

Nobody wrote a single line of malicious code to make that happen.

That’s not a scare story. It’s the new baseline. CrowdStrike found that 79% of detections in 2025 involved no malware at all — no virus, no exploit kit, nothing your antivirus was ever built to catch. The attacker just… logged in. Or called. Or asked nicely.

If you run a small or midsize business, 2026 is the year to stop thinking about cybersecurity as “did we install the right software” and start thinking about it as “can someone talk, click, or log their way into something they shouldn’t.”

Here’s what the data actually says, and what to do about it.

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AI, webdev

5-Agent Framework for Code Audits

I’ve been seeing the same anti-pattern everywhere lately.
Someone opens Cursor, Copilot or Claude and pastes a giant prompt:

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