The Psychological Perimeter: Human Risk, AI, and Cyber Resilience

Perimeters used to be simple.

First it was the network perimeter: keep the bad guys outside the firewall.
Then it became the identity perimeter: defend accounts, sessions, and devices.

Now, in an AI-driven, hyper-connected world, the real perimeter is neither your network nor your identity stack.

It’s psychological.

It lives in how people think, decide, feel pressured, cut corners, and interact with AI tools every single day. It’s shaped by culture, incentives, cognitive overload, and the stories your organization tells itself about risk, productivity, and innovation.

At Cybermaniacs, we call this the Psychological Perimeter:

the shifting boundary where human cognition, emotion, and behavior meet cyber risk, AI, and organizational culture.

And that perimeter is now the new frontline of cybersecurity. If you’re accountable for cyber, AI, or information risk, the most important perimeter you manage now doesn’t live in a firewall – it lives in your workforce’s heads.

In this guide, we’ll explore:

• How the Psychological Perimeter emerged from the evolution of security perimeters

• Why AI has exploded the cognitive attack surface

• What AI risk culture and cyber risk culture really mean in practice

• How malicious insiders, accidental insiders, and high-risk roles are transformed by AI

• What modern Human Risk Management Programs must look like in an AI world

• Practical steps to start securing your Psychological Perimeter today

Use this as your anchor: a mental model, a practical roadmap, and a hub to deeper content on human risk, AI workforce risk, AI governance, cybersecurity culture, and insider threat.

From Network Perimeter to Psychological Perimeter

Perimeters Have Moved - Your People Didn’t Get the Memo

Security has been shifting inwards for years:

• Network perimeter: “Keep them out of the castle.”

• Identity perimeter: “Verify who you are and what you’re allowed to do.”

• Zero trust: “Assume nothing, verify everything, everywhere.”

But even with all of that, breaches keep happening.
Why? Because attackers go where defenses are weakest:

They target minds, not machines.

Phishing, social engineering, fraud, influence operations, insider threat, and now AI-generated deception all aim at one thing: human cognition. Your people’s attention, emotions, biases, habits, trust, and assumptions are the new battleground.

Leading research backs this up. Verizon’s Data Breach Investigations Report has repeatedly found that around two-thirds of breaches involve a human element - 68% in the 2024 dataset and 74% in the prior year, with social engineering, error, and misuse accounting for a dominant share of incidents. IBM’s Cost of a Data Breach research shows the average breach now costs millions of dollars, with phishing and stolen credentials among the most expensive root causes. Analysts from Gartner and Forrester likewise project that human failure and talent gaps will drive over half of significant cyber incidents in the coming years, underscoring that this ‘human layer’ is not a side issue, it will become the main arena.

That’s what we mean by the Psychological Perimeter: the “edge” where your people’s thoughts, beliefs, and behaviors interact with systems, data, and AI tools.

Overlay that with the cognitive attack surface:

• Every place attackers can exploit how people think

• Every moment of distraction, urgency, fear, curiosity, or misplaced trust

• Every interaction with an AI system that can be misled, misused, or over-trusted

And suddenly, your security problem is no longer just about infrastructure. It’s about human-factored risk and human resilience.

This isn’t a side quest, it’s the main story. In both cybersecurity and information security, and in how you enable AI across the business, your cognitive layer will be everything. We’re only just beginning to see what that really means, but if you’re not thinking this way yet, this guide will help you reframe, reset, and start building for the Psychological Perimeter now - not five breaches from now.

Humans as Endpoints in an AI World

In the AI era, humans don’t sit outside the system.
They are part of the system.

They’re endpoints.
They’re decision engines.
They’re amplifiers - of both risk and resilience.

Here’s the twist: even Human Risk Management as a category still doesn’t go far enough for where this is all heading. Just like “security awareness” framed the problem too narrowly around content and compliance, “risk management” can trap us in a deficit-only mindset counting failures rather than building strengths. The real game is human resilience and workforce security enablement: designing systems, culture, and AI-era workflows so that humans can safely create value, not just avoid mistakes. We don’t think the market’s true category name for this exists yet - but whatever it becomes, it will have to sit above traditional risk management, not inside it.

Analyst firms are starting to put language around the current stage of this journey. Forrester and Gartner both position human risk management as a behavior- and outcome-focused discipline:

Forrester defines human risk management solutions as those that manage and reduce cybersecurity risks posed by and to humans by detecting and measuring human security behaviors, quantifying human risk, and initiating policy and training interventions based on that risk. Gartner emphasizes human risk management as building a security-conscious organization by driving measurable secure employee behaviors rather than relying on compliance-only training.

In practice, that means treating human risk as a first-class category, not a side effect of awareness training; building Human Risk Management Programs, not just rolling out courses and phishing simulations; and designing your security strategy around the Psychological Perimeter, not just the tech stack.

This is the mindset shift modern security and information leaders need:
from “blame the user” to “design for the human.”

How AI Supercharges the Cognitive Attack Surface

AI didn’t invent human risk. It just turned the volume up to 11. (Ok make that 100). 

Social Engineering at Machine Scale

Traditional social engineering already worked depressingly well: before generative AI went mainstream, phishing and other social engineering tactics were already implicated in the majority of breaches worldwide, with global phishing volumes climbing to nearly five million attacks in 2023 and most organizations reporting at least one successful social engineering incident in a given year. Over the past two years, groups like Scattered Spider (also known as Octo Tempest or UNC3944) have shown what that looks like at scale: social-engineering-led intrusions that took brands like MGM Resorts and Caesars offline, contributing to losses estimated at around $100 million on the Las Vegas strip alone. In 2025, the same playbook hammered major retailers such as Marks & Spencer - where the April 2025 attack is expected to wipe up to £300 million (roughly $400 million) from annual profits - and airlines such as Hawaiian Airlines, which has disclosed a Scattered Spider-linked incident and warned of potential material impact in regulatory filings. In other words, even before AI turned it up to 100, human endpoints were already under siege.

Now add AI:

• AI-generated phishing emails that sound exactly like your CEO

• Deepfake audio used to bypass voice verification

• Synthetic identities and realistic fake profiles to build trust chains

• Highly personalized scams built from scraped data and OSINT

The result? Social engineering at machine scale.

Attackers can generate, test, and refine lures faster than your team can run a single awareness campaign. And they can tailor those lures to the cognitive vulnerabilities of specific roles and individuals. This doesn’t just raise the bar for cybersecurity awareness.

It raises the bar for Human Risk Management Programs to also be focused on:

• Decision-making under pressure

• Emotional manipulation techniques

• Pattern recognition of AI-boosted attacks

• Building human resilience instead of fear

The Perfect Storm: Speed, Overload, and Blind Trust in AI

Most people don’t wake up intending to create risk; they’re just trying to get work done. For years this has been the adage in security: from errors to slips, mistakes to misunderstandings, much of the human‑factored risk taxonomy in the cyber and digital workspace stems from action‑based behavior (people doing something under pressure, distraction, or confusion) while inaction‑based risk (not reporting, not questioning, not escalating) quietly compounds the problem.

AI-era failures like misconfigured models, over-permissive cloud setups for AI workloads, data and model poisoning, or “rubber‑stamping” AI decisions without real human oversight aren’t some alien new species of risk - they’re the next generation of the same pattern. NIST’s emerging AI risk and threat taxonomies, MIT’s AI Risk Atlas, and industry bodies like the Cloud Security Alliance all group these under familiar headings: configuration and access risk, data and input risk, and governance and oversight risk. Early signals are already stark: recent studies show that more than 80% of enterprises have suffered security incidents from misconfigurations, that a tiny fraction of poisoned training samples can meaningfully subvert large models, and that “human‑in‑the‑loop” oversight, if poorly designed, can actually create a dangerous false sense of safety.

In real life that looks like:

• “I don’t have time - let me paste this into a public AI tool to summarize.”

• “The model said this is okay, so… it must be fine.”

• “I don’t fully understand this code, but the AI generated it, so I’ll deploy it.”

This is the new reality of AI workforce risk:

• Speed vs. scrutiny: AI makes everything faster - good decisions and bad ones.

• Overload vs. clarity: AI becomes a crutch in a world of too much information.

• Blind trust vs. healthy skepticism: People often assume that if AI said it, it must be right.

At the same time, you’re under pressure to do AI workforce enablement—help people use AI tools to be more productive, innovative, and effective.

The tension is real:

Enable AI use too slowly → shadow AI, workarounds, frustration.
Enable AI use too quickly, without guardrails → data leakage, compliance failures, and quiet, accumulating risk.

“Perfect Pins” of AI: Precision-Targeted Psychological Manipulation

Of everything in this landscape, the fastest‑moving and most viscerally unsettling frontier is synthetic media and deepfakes. Our visual system and social brain have spent millennia learning that what we see and hear in front of us is a trustworthy shortcut to reality: faces, voices, expressions, and micro‑cues are processed in dedicated neural circuits that are optimized to say “this is real, this is a person, this is safe or unsafe” in a fraction of a second. Deepfakes hijack exactly that machinery.

One of the most dangerous aspects of AI is its ability to create “perfect pins”:

• Highly personalized messages tuned to an individual’s language, preferences, and vulnerabilities

• Deepfake content that mimics specific leaders, colleagues, or brands

• Adaptive attacks that evolve based on how people respond

Where traditional phishing relied on volume and probability, AI allows attackers to home in on psychological precision.

This is where the cognitive attack surface + Psychological Perimeter fully merge:

• The target isn’t your email gateway.

• It’s your people’s pattern recognition and emotional triggers.

Security teams cannot out-filter this purely with technology. Your people are now being attacked at the level of perception itself, not just information. CISO's and Human Risk Management Teams must build psychological literacy and human resilience into the way their organization thinks, works, and responds.

AI Risk Culture: The Missing Link Between Tools and Behavior

From a C‑suite vantage point, you can invest heavily in AI platforms, models, and automation, but if your underlying culture is misaligned, those same capabilities will be deployed in ways that quietly accumulate risk faster than they create durable value.

What Is AI Risk Culture?

We define AI risk culture as:

the shared beliefs, norms, stories, and decision habits around how AI is used, questioned, governed, and challenged inside your organization.

It sits alongside and overlaps with:

• Cybersecurity culture – how people think and behave around digital risk generally

• Cyber risk culture – how the organization balances innovation vs protection

• Compliance culture – whether rules are lived or just acknowledged and ignored

In a healthy AI risk culture, people feel safe raising concerns about AI tools and outputs, leadership consistently models responsible AI use, teams are clear on when to trust, when to double-check, and when to challenge AI-generated decisions, and security, risk, and governance functions are experienced as strategic partners in value creation rather than as compliance-driven blockers.

In an unhealthy one, AI becomes just another chaotic productivity hack.

Shadow AI, Hero Hacks, and Quiet Workarounds

If you’re not providing clear, usable, and safe AI paths, people will make their own—and the data now shows this is the norm, not the exception. Multiple global surveys paint a consistent picture: Software AG’s Shadow AI study suggests roughly half of employees are already using unapproved AI tools at work, often unwilling to stop even if banned, while Menlo Security reports shadow GenAI usage surging, with close to seven in ten employees relying on free-tier tools and more than half pasting sensitive data into them. Independent research from Harmonic Security and others indicates that a significant share of sensitive AI interactions, over 45% in some environments, are flowing through personal accounts completely outside corporate controls.

At the same time, KPMG’s global AI trust study and Deloitte’s State of Generative AI series show a governance gap at the top: most boards and executive teams expect AI to be transformational, yet only a minority feel highly prepared on governance and risk, and fewer than half say their people are sufficiently educated on safe, compliant AI use. In other words, employee behavior is racing ahead while formal AI governance, understanding, and acceptance of new rules are lagging behind.

Common patterns:

• Shadow AI: unapproved tools, unsanctioned plugins, side accounts

• Hero hacks: “I built this AI workflow over the weekend; it’s not approved, but look at the results!”

• Quiet workarounds: “We’re not technically allowed to, but it’s how we get things done.”

This is where AI workforce enablement and AI workforce risk collide.

Without Human Risk Management Programs that explicitly address AI, you get:

• Fragmented practices

• Hidden data risk

• Inconsistent standards

• Misalignment between policy, productivity, and real work

AI Adoption Without Culture: The Silent Risk Curve

Because enterprise AI is still relatively new, it can be genuinely hard for boards and executives to tell whether their AI risk culture is healthy or quietly drifting into danger. You won’t always see it first in dashboards or policies; you see it in how people work around controls, how often AI-enabled processes are misconfigured or poorly owned, and how comfortable teams feel challenging automated decisions. In practice, some of the most reliable leading indicators show up in day‑to‑day behavior:

These aren’t just “adoption” issues. They are human risk issues at the heart of your Psychological Perimeter: the living perimeter formed by your workforce’s cognitive layer and your culture. It is the people who use your technology and create value who now also sit at the junction of access and identity, making them both your most important control surface and your most attractive target. That places identity not outside the Psychological Perimeter, but at its core. For decades, cybersecurity has been organized around protecting data, systems, information, and endpoints; in a world where intelligence itself becomes part of the infrastructure, we are likely to see a profound inversion, where securing the cognitive layer and the psychological perimeter becomes the organizing principle, and traditional controls are reinterpreted through that lens.

Inside the Psychological Perimeter: Humans, Roles, and Insider Threat

The Psychological Perimeter is not evenly distributed.
Some people, roles, and teams carry far more human-factored risk than others.

Malicious Insiders in an AI-Enabled World

A malicious insider is not a shadowy figure outside the walls; it is someone you already trust (an employee, contractor, or service‑provider staff member) who intentionally abuses legitimate access for personal gain or to cause harm. In the language of the Psychological Perimeter, they sit right at the intersection of cognition, culture, identity, and access.

Well before AI entered the picture, malicious insider cases were already demonstrating how expensive that combination can be:

• At Coinbase, outsourced customer service agents at a third‑party provider were bribed to pull sensitive customer data from internal tools. That data was then weaponized in highly convincing scams that drained user accounts. Coinbase has told regulators it expects hundreds of millions of dollars in remediation and reimbursement costs linked to the incident, on top of brand and regulatory fallout.

• At Microsoft, a software engineer responsible for testing the company’s online retail systems quietly discovered that “test” purchases could generate real digital gift cards. Over time he siphoned off more than $10 million in value, laundering the proceeds through cryptocurrency and even using colleagues’ accounts as cover. This was not a perimeter breach; it was a trusted identity exploiting a blind spot in process and oversight.

• In the Capital One case, a former cloud engineer at a major provider exploited a misconfiguration in Capital One’s environment to access data on more than 100 million customers. Subsequent class‑action settlements and regulatory penalties alone have been measured in the hundreds of millions of dollars, before counting long‑term trust and remediation costs.

In each case, the perimeter wasn’t breached from the outside; it was redefined from within by people who already had the keys.

These are not exotic “one‑off” tales. They are early signals of what happens when deep system knowledge, privileged access, and human motivation collide inside your perimeter.

Now add AI.

In an AI‑enabled world, malicious insiders don’t just have access, they have amplifiers:

• Tools to generate highly convincing phishing, fraud, or social engineering campaigns that abuse their internal knowledge of brands, processes, and people

• Assistance writing evasive code, scripts, or prompts that help bypass controls or hide activity

• Support in crafting persuasive cover stories, synthetic identities, or fake artifacts (logs, emails, screenshots) that complicate investigations

• Faster ways to discover where sensitive data lives, how it flows through AI and data platforms, and how to quietly extract or poison it

Insider threat in the AI era is not a new category of villain; it is a new category of capability. The same Psychological Perimeter that enables your workforce to create value (identity, access, context, and cognition) can be turned inward by a small number of bad actors with outsized impact.

That means organizations must:

• Blend technical monitoring (identity analytics, data security, AI usage telemetry) with behavioral and cultural insight into pressures, incentives, and grievances

• Treat insider risk as a human and cultural issue, not just a logging or tooling problem

• Integrate Human Risk Management Programs with insider‑threat teams, HR, legal, ethics, and AI governance so that high‑risk access, roles, and AI workflows are understood and actively managed

Accidental Insiders and Everyday AI Misuse

More often, your risk comes from well-intentioned people doing risky things: pasting sensitive data into public AI tools, fully trusting AI-generated content in contracts, code, or communications, relying on “free” AI services that mine or resell data, or misconfiguring AI workflows in ways that unintentionally expose information.

These are accidental insiders: people who become a risk because the Psychological Perimeter is unguarded or security culture is lax. 

This is why cybersecurity awareness on its own isn’t enough, and the evidence has been clear for years. After more than a decade of mandatory training modules and phishing simulations, human factors still show up in around two-thirds of breaches in studies such as Verizon’s DBIR (68% in the 2024 report, 74% the year before), which tells us the traditional awareness model has not materially bent the risk curve. That doesn’t mean employees don’t need training, support, and clear expectations; they absolutely do for compliance, governance, policy, and proper technology use. But the *methods* need to evolve.

If you expect your workforce to be ready, willing, and able to operate safely with AI, then the content, processes, engagement, learning experiences, reinforcement, and remediation all have to be rethought as part of a coherent Human Risk strategy (not treated as a once-a-year checkbox). Brains, habits, mindsets, and culture don’t change in a day; humans adapt through sustained effort, context, and practice. We trained a human to walk on the moon, it's clear people are capable of extraordinary adaptation, but if you don’t allocate the time, attention, and resources to your human risk management team, that adaptation is not going to happen by magic.

You still need:

- Role-specific training focused on AI use cases

- Just-in-time nudging and feedback

- A culture where people can say “I’m not sure this is safe” without fear

High-Risk Roles and Cognitive Load

Certain roles and functions are especially exposed:

• Executives and senior leaders

• Finance and treasury

• R&D, IP-heavy teams, product, and innovation

• Dev, data, and ML teams

• OT/ICS operators in critical environments

These roles combine high decision power, access to sensitive data and systems, intense time pressure and cognitive load, and now, increasingly, AI-enabled workflows.

That’s a recipe for an expanded cognitive attack surface.

Your Human Risk Management Programs should explicitly identify and support these high-risk roles with:

• Deeper education and simulation

• Scenario-based workshops involving AI

• Stronger guardrails around AI tools and data access

• Psychological safety to report concerns early

Building Human Risk Management Programs for the AI Era

If the Psychological Perimeter is the new frontline, then Human Risk Management Programs are your operating system for defending it.

From One-Off Awareness to Human Risk Operations

The speed of AI adoption, the sophistication of modern attacks, and the wholesale changes required in workflow and process design (as highlighted in recent MIT studies on AI and work redesign) mean that getting your cognitive layer “ready, willing, and able” will never be achieved with a 30‑minute e‑learning module. It requires a strategic, programmatic, holistic, structured, best‑practice‑driven, culture‑aligned effort; maturing that capability beyond managing phishing tools and celebrating October’s Security Awareness Month should be at the front of every CISO’s transformation agenda today.

Human Risk and Resilience Programs must go further:

• Programmatic, not episodic

• Based on measurement, behavioral insight, and culture, not just content

• Integrated with governance, AI, compliance, and operational risk

• Designed to reduce human-factored risk and build human resilience over time

Think of it as moving from “we told people once” to “we run human risk as a continuous operation.”

Four Pillars of an AI-Era Human Risk Program

1. Strategy & AI Governance

Your program must tie human risk directly into:

• Your Overall Cybersecurity strategy

• AI Policy & Governance

• Enterprise Risk and Compliance

• Information Security and Data Protection

• Workforce Management and Human Capital Planning

This means treating AI and human risk as an enterprise‑wide concern, not a security side project. The stakeholder set expands: security and risk leaders, HR and talent acquisition, legal and compliance, procurement, technology, and business owners all have to share real accountability for how AI is selected, rolled out, and governed. The workforce itself, and even how you hire, becomes part of the attack surface, as cases of North Korean operatives using deepfakes to secure remote roles, “laptop farms” fronting for sanctioned entities, and nation‑state actors embedding themselves in distributed teams for IP and data theft have already shown. What you are aiming for is a unified, board‑visible view of AI risk culture and cyber risk culture, backed by clear ownership, secure workforce pipelines, and aligned incentives across the organization.

2. Culture & Psychological Perimeter Design

The Psychological Perimeter isn’t just observed; it can be designed. At the foundation of that design is culture: the shared norms, stories, and “this is how we do things here” that define what is accepted as normal and what is not. Culture is the fabric of your cognitive layer and your Psychological Perimeter; it shapes how quickly your workforce notices new risks, how they adopt new technologies, and how deeply cyber safety, digital risk avoidance, and responsiveness are woven into everyday decisions. External guidance is increasingly explicit about this: the UK NCSC’s culture-focused guidance, NIST’s human factors and culture working groups, and CISA’s recommendations for running an effective cyber program all place culture at the center of resilience. Culture is groups of people in motion (what we call the Human OS), and like any critical operating system, it needs to be patched, cared for, monitored, and managed, not left to chance.

• Use stories, rituals, messaging, and leadership behavior to set norms

• Normalize asking questions and reporting near-misses

• Remove shame and blame where possible; focus on learning

• Reward safe, thoughtful use of AI, not just speed and output

Culture is a control surface. Treat it like one.

3. Education & Cybersecurity Awareness with a Mindset Shift

Education must evolve from “what not to do” to “how to think.”

That includes:

• Building literacy around AI-generated content, deepfakes, and deception

• Teaching people to recognize pressure tactics and emotional triggers

• Helping teams understand the cognitive attack surface in their daily work

• Showing how their actions impact cyber risk culture and overall resilience

People aren’t the problem; they’re the smartest control surface you have—if you equip them for a new kind of work. The mindset shift here isn’t only about attitudes to AI; it is about reframing work itself: how people edit, steer, validate, verify, refine, and approve through agents and new workflows. Those are different mental pathways and cognitive processes that don’t come naturally to everyone, which is why we treat Cognitive Operations as a distinct competency area in our Human OS model—one that requires time, practice, creativity, and better use of existing tools, not just another training module.

4. Measurement & Human Risk Metrics

The days of phishing click rates and compliance course completions being the only meaningful measurements in human risk management are over. The monitoring layer—and the insight and analysis it enables into behaviors, mindsets, competencies, compliance, and real‑world concordance with policy—has to become core if you want true visibility into your human endpoints. You can’t secure what you don’t know you have, and you can’t secure humans if you don’t know who they are, what they are doing, and—in our view, perhaps most importantly—why they are doing it.

Modern human risk measurement should include:

• Behavioral metrics (not just training completion and phishing clicks)

• Patterns of AI misuse and near-misses

• Culture indicators: transparency, acknowledgement, responsiveness, respect

• Human resilience measures: how people respond and adapt after events

These data points feed into board-level dashboards and Human Risk Management Program roadmaps.

Practical Safeguards for the Psychological Perimeter

 Let’s bring it down to earth. What can you actually do?

Design Patterns for Safe AI Use

Document and socialize patterns, not just policies:

• Safe AI patterns:

  • “You can use AI to draft, summarize, and brainstorm on non-sensitive content.”

  • “You can use approved AI tools for coding support within these repositories.”

• Unsafe AI patterns:

  • “Don’t paste customer PII, confidential IP, financial forecasts, or legal strategy into public AI tools.”

  • “Don’t use AI-generated content in contracts or public statements without review.”

Make it visual, concrete, and role-specific.
This is where cybersecurity awareness meets real work.

Protecting High-Risk Roles from AI-Driven Social Engineering

For high-risk roles:

• Run targeted simulations that incorporate AI-generated attacks

• Offer small-group workshops that analyze real-world AI scams and deepfakes

• Give them decision playbooks for verifying identity, payments, and critical actions

• Provide direct support channels for “something feels off” moments

You’re not just teaching them to spot “bad emails.”
You’re training them to navigate the Psychological Perimeter with more awareness and confidence.

Embedding Human Risk into Governance and Compliance

Governance and compliance must reflect human reality, not just controls on paper.

That looks like:

• Aligning AI policies with how people actually work and collaborate

• Involving frontline teams in shaping guidelines and guardrails

• Reporting on human risk metrics alongside traditional security metrics

• Treating human risk as a strategic dimension of information security, not an afterthought

When boards and execs see human risk, AI risk culture, and cyber risk culture in their dashboards, they’re more likely to resource and prioritize it.

Roadmap: How to Start Securing Your Psychological Perimeter

You don’t need to fix everything at once. You do need to start on purpose. (Many organizations start by partnering with a specialist Human Risk and AI culture provider to accelerate this work.)

Step 1: Map Your Psychological Perimeter

• Where do humans + systems + AI interact in critical ways?

• Who are your high-risk roles?

• Which workflows rely heavily on AI already (even informally)?

Step 2: Baseline Human Risk & AI Behaviors

• Survey, interview, and listen

• Look at incidents, near-misses, and “quiet” patterns

• Understand your current cyber risk culture and AI risk culture

Step 3: Define Your Target State

• What does “good” look like in 12–24 months?

• How do you want people to think, talk, and act around AI and risk?

• What does a healthy Human Risk Management Program look like for you?

Step 4: Build or Upgrade Your Human Risk Program

Use the four pillars:

1. Strategy & governance

2. Culture & Psychological Perimeter design

3. Education & mindset shift

4. Measurement & metrics

Prioritize high-impact, realistic changes over grand but vague aspirations.

Step 5: Pilot with High-Risk Roles and AI Power Users

• Start where risk and influence are highest

• Co-create solutions with the people who actually do the work

• Use feedback loops to refine training, processes, and governance

Step 6: Measure, Adapt, Communicate

• Track progress in human risk and AI behaviors

• Share stories and wins, not just charts

• Make human risk and the Psychological Perimeter part of regular leadership and board discussions

FAQ: Humans, AI, and the Psychological Perimeter

What is the psychological perimeter in cybersecurity?
The Psychological Perimeter is the boundary where human cognition, emotion, and behavior intersect with systems, data, and AI tools. It’s the real frontline of security today—shaped by culture, incentives, mindset, and how people actually work.

What is the cognitive attack surface?
The cognitive attack surface is the set of ways attackers can exploit how people think: their biases, attention, trust, fears, habits, and decision shortcuts. AI expands this surface by enabling more targeted, convincing, and scalable forms of manipulation.

How does AI risk culture relate to cyber risk culture?
AI risk culture is a specific slice of cyber risk culture. It focuses on how people think about, use, question, and govern AI tools and systems. A strong AI risk culture supports safe innovation; a weak one leads to shadow AI, data leakage, and avoidable incidents.

What are Human Risk Management Programs?
Human Risk Management Programs are structured, continuous efforts to understand, measure, and influence how humans create and reduce risk. They go beyond traditional awareness training to include culture, AI governance, behavior design, measurement, and ongoing operations.

How does AI increase insider threat and malicious insider risk?
AI gives malicious insiders more powerful tools to exfiltrate data, evade detection, and craft convincing scams. It also increases the risk of accidental insiders, where well-meaning people misuse AI in ways that expose sensitive information or create compliance issues.

What is AI governance, and how does it connect to compliance and information security?
AI governance covers the policies, frameworks, and decision structures that guide how AI is selected, deployed, and used. It intersects with compliance (regulatory and policy requirements) and information security (protecting data and systems). Without a human risk lens, AI governance often fails at the point where real people and real work meet.

If you see your organization in this picture, that’s not a failure. It’s a starting point.

The Psychological Perimeter is already there, shaping your risk every day, whether you acknowledge it or not. The question is whether you’ll design it intentionally, or let it evolve by accident. And that’s where Human Risk Management, AI risk culture, and a new kind of cybersecurity leadership come in.