TL;DR:

Prevention-only security breaks under modern complexity and change. Your job isn’t to stop change, it’s to make it survivable. Be the department of how: embed with product from day one, threat-model real workflows, instrument meaningful logging across identities, data flows, and integrations, and operate on assume-breach. When the miss happens, detect fast, contain the blast radius, rotate credentials, quarantine affected components, roll back safely, and keep the business moving. Ground your guardrails in widely accepted frameworks — NIST CSF’s Govern/Identify/Protect/Detect/Respond/Recover, plus incident-response playbooks — so adoption happens in daylight instead of via shadow tools. Resilience beats theater. Progress with a seatbelt is still progress.

Introduction

Imagine a mid-sized company in 2025. The R&D team proposes an internal AI assistant to automate tedious tasks, boost productivity, analyze reports, even prototype features faster. The board is cautiously enthusiastic. The engineers see this as the next edge.

Then security shows up with a 47-page risk assessment and says “We need to understand every possible attack vector before we can approve this.” The project gets shelved. Three weeks later, you discover half your developers are using ChatGPT for the same tasks anyway—just without any controls, logging, or oversight. When the inevitable data leak happens, guess who gets blamed?

This isn’t a hypothetical. It’s happening right now in companies across every industry.

This isn’t fiction. It’s what happens when legacy posture meets modern velocity. With AI already in use - inside your org or at your competitors - security has to be the department of how, not no.

Here’s the argument: Perfect prevention isn’t real, especially with AI; security’s job is to show the how early and keep the org moving with visibility, detection, containment, and fast recovery.

If security doesn’t evolve, it won’t just miss opportunities - it becomes the speed bump everyone swerves around.

The “Department of No”

Security exists to enable safe(ish) risk, yet all too often it becomes the gatekeeper out of fear. The default posture: “If we don’t understand (the tech and\or the purpose), we block.” That seems safe - until it freezes progress, breeds resentment, and drives adoption underground (shadow usage).

You know the pattern: promising AI pilots stall behind “unknown risk” memos, policies refuse even tiny experiments, and security parachutes in after the architecture is locked. The net effect is predictable - progress freezes, trust erodes, and shadow usage explodes.

As “no” becomes default. Engineers see security as the obstacle, not the partner. When new technologies with vague boundaries arrive (hello, AI), that posture is a liability.

We (security people) need to shift the mindset. We don’t have to know every risk before engaging. But we must make security part of the journey - not the roadblock.

We’ve seen how blanket bans backfire. In 2023, Samsung temporarily barred staff from using generative AI after sensitive code was pasted into ChatGPT; the company then had to craft controlled ways to let employees use AI productively rather than drive usage underground. Reuters, Bloomberg.

Why Prevention-First Thinking Is Broken (Especially for AI)

Prevention has long been a center of gravity for security: lock down, patch, firewall, restrict. In many real-world breaches, however, prevention was circumvented via paths nobody saw, zero days, or trust manipulation. I’ve spent over a decade proving “100% secure” wrong. Prevention helps, but it isn’t a strategy.

Prevention-only collapses under modern reality: the threat surface mutates faster than controls, many AI risks aren’t fully modeled yet, tools drift and misconfigure, and harsh rules just push people to work around them. Keep prevention for known, commodity stuff, but move the center of gravity to detection, response, and resilience.

Strong security voices (and myself) argue that detection and response are now more essential than chasing unattainable prevention guarantees. Prevention must remain for known vectors, but the center must shift toward detection, response, and resilience. If you want a sane baseline for “secure by design” in AI systems, start with the joint NCSC/CISA guidance and keep MITRE ATLAS close when you threat-model AI workflows (UK NCSC guidelines, PDF, MITRE ATLAS.

AI as a Catalyst and Mirror

AI does not create new weaknesses as much as it exposes old ones. Many of the challenges security faces with AI are just magnified reflections of long-standing issues: unclear ownership, slow decision cycles, and lack of partnership with the business. When AI arrives, those cracks widen. Real incidents underline this. Microsoft AI researchers accidentally exposed ~38 TB of internal data via an overly-permissive SAS token posted in a public repo - a classic governance and process failure made louder by AI’s pace (Wiz Research, Microsoft MSRC, TechCrunch). Even platform vendors trip: in March 2023 a library bug led to ChatGPT exposing other users’ chat titles - small scope, big lesson about unexpected failure modes in AI-era stacks (OpenAI postmortem, Reuters).

AI adoption is different in speed and visibility. Teams often start experimenting without waiting for policy, and the technology itself evolves weekly. AI started with the online chatbots, and less than 2 years we already seeing AI-based browsers, AI-based IDEs, MCP servers, A2A protocol, and more. This pace makes traditional approval processes meaningless. The old model of risk review and sign-off cannot keep up. If the only security tool you have is the word “no,” you are guaranteed to lose control of the situation.

Security teams must become co-designers. Joining early, they can shape data-handling decisions, model governance, and access controls. Arriving late, they will simply inherit the risks.

The Strategic Gap

Many security programs fail not because they lack talent, but because they lack strategy. They focus on operations and compliance, not on outcomes. When faced with AI, this lack of direction becomes a full stop.

No alignment with business goals. Tie AI risk to money. Map each AI feature to a revenue line, a cost line, or a contractual promise. “If prompt injection skews pricing outputs, we breach SLA X and trigger Y penalties.” Action: add a one-page “AI risk to business outcome” appendix to each product PRD. Technology over strategy. Don’t buy a tool to replace a missing process. First write the Target Operating Model for AI: who owns prompts, datasets, connectors, approvals, rollback, and incident response. Then buy tools that serve that diagram. Weak communication. Replace acronyms with stories. “This feature retrieves customer data then writes into a model. Here’s how an attacker poisons retrieval, here’s the smallest guardrail we can ship this sprint.” Action: force 3-sentence security briefs per feature. Siloed thinking. Put security in the AI dev loop weekly. Co-own a backlog with DS/ML and product. Run short ATLAS-based drills on new features and record three mitigations you’ll actually ship. MITRE ATLAS.

Getting Buy-In When You Have No Political Capital

Let’s address the part nobody wants to say out loud: most security teams reading this don’t have the organizational weight to just decide they’re transforming into strategic partners. You’re underwater, you’re understaffed, and half the company sees you as the people who slow things down. So how do you actually make this shift when you’re starting from a position of weakness?

You don’t ask for permission. You ask for a pilot. Pick one AI project with one product manager who doesn’t actually hate you. Tell them you want to sit in on their sprint planning for six weeks, not to block anything, just to understand their workflow and flag risks early. Frame it as making their life easier. Most PMs are terrified of shipping something that’ll blow up in production and create a three-day war room. Position yourself as the person who helps them avoid that nightmare.

The other move is finding an executive sponsor who isn’t your boss (or your boss, if they have the weight and resources to assist). Look for the person who has the most to lose if AI adoption goes sideways - usually the CTO or Chief Product Officer. Go to them with a one-page proposal: “I can help you ship AI features faster and safer, here’s the three-month plan, here’s what I need from you.” What you need is air cover, not budget. You need them to tell product teams that security has a seat in planning meetings. In return, you make their roadmap move faster by preventing the disasters that cause rollbacks. Start small and use wins as leverage. You don’t need to transform the entire security function on day one. You need one success story. “We embedded with the Q4 AI project, caught a data leakage risk in week two, shipped a fix that added 11 milliseconds of latency, and the feature launched on time with no incidents.” Now you’re not asking for a philosophical shift. You’re asking to repeat something that already worked.

What This Actually Looks Like

That all sounds reasonable until you try to do it. So here’s what it actually looks like on a Monday morning. Security sends one person to product sprint planning. Their job is to ask three questions about each AI feature: what data goes in, where does the output go, and what’s the rollback plan. That’s it. You’re not doing a full threat model in the room. You’re flagging the stuff that needs a deeper look. This costs maybe three hours a week and prevents the multi-week rewrites that happen when security finds problems in staging. If you can’t scale yourself, build a security champion program. Find two or three engineers per team who give a damn about not shipping disasters. Give them a 30-minute workshop on AI threat modeling using MITRE ATLAS. Then give them a Slack channel directly to you for fast questions. They become your early warning system. You’re not deputizing them to be security experts. You’re giving them just enough context to recognize when to call for help.

Another move is proposing a shared OKR with the product team. Something like “ship AI feature X with less than 50 milliseconds of latency overhead from security guardrails.” Now you’re measured on enablement, not just “incidents prevented.” This changes the dynamic. You’re co-owners of the outcome. When product is struggling to hit latency targets, you help optimize. When security controls slow things down, product helps you instrument differently.

Logging is your peace offering. Build the observability infrastructure that product and engineering also want. Correlation IDs, debug telemetry, request/response logging that helps them reproduce bugs. Make it useful for them first, and your security instrumentation rides along. You get model inputs, outputs, and access patterns. They get debuggability. Everybody wins.

From Prevention to Resilience

The future of security is not about stopping every breach, but limiting damage when one happens. AI systems make that even clearer. Their scale and unpredictability mean that defense must focus on preparedness and adaptability.

Resilience starts by embedding security in AI dev from day zero: sit with your people, agree on safe defaults for inputs, prompts, retrieval, and outputs, and write down who approves what. Run scenario planning like a fire drill: what happens if prompts are injected, data is poisoned, or connectors exfiltrate records? Build basic detection and anomaly signals around model access, data egress, and unusual tool invocations so you can see trouble early. When something does slip, containment wins the day - design blast-radius limits and quick isolation switches so you can keep the business running. Governance should enable, not freeze: publish clear acceptable-use rules, approval flows, and audit trails that make “the right way” the easiest way. Finally, treat every miss as training data. Close the loop with short post-incident reviews focused on learning, not blame.

Resilience is operational, not theoretical. Ship kill switches and feature flags for AI components. Log model inputs/outputs and tool calls with correlation IDs. Scope tokens and SAS links tightly and set auto-expiry. Pre-write rollback for model versions and RAG indices. Tabletops shouldn’t be PowerPoint; run 60-minute drills: prompt injection, retrieval corruption, runaway cost spike. Use NIST AI RMF “Map/Measure/Manage” as the cadence for those drills — then turn fixes into code, not slides. NIST AI RMF/Playbook.

A Practical Example

Company A banned all generative AI on day one. Productivity didn’t stop; it just went dark. Engineers copied snippets into personal accounts, analysts used free browser extensions, and managers pasted sensitive notes into random web tools to “save time.” Three months in, a contractor’s personal account was subpoenaed in an unrelated case and contained fragments of internal data pasted during “temporary testing.” Legal panic. Security had no logs, no controls, and no narrative beyond “we said no.” Company B took the opposite path. Week one, they shipped a basic, approved AI workspace: SSO, logging, redaction on copy/paste, and a simple “do/don’t” page. They trained teams on prompt hygiene and created a 2-page exception process for edge cases. Incidents still happened, but they were visible and recoverable. The lesson is boring and effective: control isn’t a wall, it’s a workflow.

Leadership and Ownership

Security’s evolution into the department of “how” will only work if leadership across the organization buys in. This shift is cultural before it is technical.

Executives must redefine success: not “no breaches ever,” but no breach that cripples us. Fund small experiments, tolerate controlled failure, and stitch security into data and product leadership so decisions happen with security in the room, not after the fact.

For CISOs, it means shifting from control to collaboration. Instead of dictating rules, they must set direction. That direction should be based on shared responsibility: security guides, but every team implements. Security can’t own AI risk alone, but it can lead the way in understanding and mitigating it.

Strong leadership creates psychological safety around innovation. When people know they won’t be punished for using AI responsibly, they ask for help instead of hiding.

Anticipating Objections

Every change meets resistance, especially in security. The most common objections sound reasonable at first glance:

“If something breaks, I’ll get fired.” Yeah, you might. But you’re more likely to get fired when shadow AI causes a breach you didn’t even know was happening. The thing that saves you is documentation. Risk registers, decision logs, email threads showing you recommended X and the business chose Y. When leadership has selective amnesia about what they approved, that paper trail is the difference between “security failed us” and “we made an informed choice.”

“I don’t have the headcount for this.” Stop asking for headcount and start with time. Allocate 20% of one person’s time to one AI project. Track whether that prevents rework or reduces incident response costs. You’re already spending time on AI reactively after things break. This redirects that effort earlier where it’s cheaper.

“I don’t know how to talk to product teams.” Learn their metrics. If it’s time-to-launch, frame security as “we help you ship without rollback drama.” If it’s uptime, you’re the blast-radius people. Stop talking about CVEs and start talking about the things they lose sleep over. This takes practice, but it’s learnable.

“Leadership won’t fund this shift.” Don’t ask them to fund it yet. Ask for access. “Let me sit in on sprint planning for three AI features. No budget, no headcount ask.” Prove value first. Once you have one project where you made things better, you have a case study. Funding follows proof, not proposals.

“My team doesn’t have the skills to be strategic partners.” Probably true. The security professional who can threat model AND translate risk into business language AND build relationships is the one who’ll have a career in five years. If that makes you uncomfortable, good. Now decide whether you’re going to do something about it.

The Real Measure of Maturity

Security maturity used to be defined by controls in place. The next era will measure it by resilience, adaptability, and learning. A team that can detect and recover quickly from failure is more secure than one that claims it never fails.

Ask simple questions:

  • Can we detect AI misuse or data exposure within hours, not weeks?
  • Can we isolate affected systems or AI features with a feature flag, without halting business?
  • Are model inputs, outputs, and tool calls logged with correlation IDs so we can reconstruct an incident in 24 hours?
  • Do we have an accurate inventory of AI systems, models, prompts, connectors, and datasets in use?
  • Are prompts and retrieved data classified and redacted by policy before leaving our boundary?
  • Can we revoke and rotate credentials, SAS tokens, API keys, and embeddings indexes quickly when something goes wrong?
  • Do third-party AI contracts cover logging, data retention, breach notice, and model-training restrictions on our data?
  • Do we run adversarial testing or red-team drills for AI features using a common language like MITRE ATLAS?
  • Do we exercise AI-specific tabletops: prompt injection, data poisoning, model rollback, retrieval index corruption?
  • Are we monitoring abnormal spend, latency, or call-volume spikes that can signal abuse or runaway agents?
  • Is there a documented rollback plan for model versions and RAG indices, tested in staging at least quarterly?
  • Are service accounts for AI connectors least-privileged with tight scoping on datasets and tools?
  • Do engineers and leadership understand the same risk story in plain language, not acronyms?
  • Are our guardrails aligned to public guidance (NIST AI RMF, OWASP LLM Top 10, NCSC/CISA), but implemented as code, not slides?
  • Do post-incident reviews produce concrete changes to logging, controls, or process within a sprint?
  • Are users one click away from reporting AI abuse or weird outputs inside the product UI?

Maturity is not static. It’s the ability to change course fast, grounded in strategy rather than panic.

The Skills Gap Nobody Talks About - Call to Action

This transformation requires security people to develop capabilities that don’t come naturally. You need to become part product manager, part business translator, part relationship builder. Most of us picked security because we liked technical puzzles, not stakeholder management. But technical excellence alone doesn’t cut it anymore. You can be the best threat modeler in the building, and if you can’t explain why it matters in business terms, you’ll lose every prioritization fight.

So what does that look like? Learning how to sit in a product meeting without being the person everyone dreads. Learning to deliver bad news without making people defensive. Reading your company’s P&L so you can connect security risks to actual business outcomes. Being able to say “yes, and here’s how” way more often than “no, because.” None of that is intuitive if your background is pentesting. You’ll be bad at it at first. Your first sprint planning meeting will be awkward. Do it anyway.

For CISOs, budget for this transformation. Send your people to product management workshops, not just DEF CON. Pair junior security engineers with product managers for a quarter. Bring in someone to teach presentations that don’t make executives fall asleep. This is as important as your SIEM. For individual contributors, you don’t need permission. Read your company’s strategy docs. Learn what engineering is measured on. Volunteer to help with something that’s both a security problem and their problem. Practice explaining security to non-technical people until you find metaphors that land.

The cold comfort is that almost nobody has these skills naturally. Everyone is figuring this out. The difference is between people who acknowledge the gap and start closing it versus people who complain that the job isn’t what it used to be. The security person who can only speak security is going to be obsolete. The one who can translate between security and the rest of the business gets to shape what comes next.

AI adoption is already happening. The question is whether security will lead it or chase it.

The security teams that thrive will be those that say: “Yes, let’s do this, but safely.” They’ll build the frameworks that let innovation move at speed. They’ll replace rigid prevention with adaptive resilience.

Shift the goal from stopping the breach to surviving it. Shift your mindset from resistance to readiness.

Because in the end, the only unacceptable risk is standing still.

“You can’t stop the waves, but you can learn to surf.” - Jon Kabat-Zinn