Your Guide to the Behavioral Policy Creation Loop

Security leaders often carry the reputation of being "the department of no." Labeled as policy pushers, they're frequently seen as bureaucrats drafting restrictive standards, issuing unreadable documents, and rolling out annual training sessions that tick compliance boxes but rarely drive meaningful change.

But cybersecurity policy isn't the enemy. It's a powerful tool that communicates how the workforce should safely and securely engage with technology. It also translates a security vision into tools, rules, and behavior standards across the organization.

Where policy breaks down is in the way it's created and delivered. It loses its impact when it's overly restrictive, lacks input from the workforce, and closes the communication loop to the very people it affects. In this world, policy is destined to remain a rigid artifact rather than a responsive guide, enabling better security decisions. Let's talk about a better way.

Why Security Policy Gets a Bad Name

Policies get a bad rap because, too often, they're built for audits and written in legalese. Instead of shaping behavior, they create distance between the security vision and the people it's meant to support. At best, they simulate control. At worst, they create resentment. Employees feel boxed in, and security teams feel unheard and powerless to guide adherence. That's when policies become a source of friction instead of clarity.

Let's look at an example.

A security leader creates a new AI Usage policy that grants usage of ChatGPT while prohibiting other AI tools without considering their strengths or differences. Everyone is clamoring for AI, and the leader has to push out something to manage the fervor. They draft the policy as a v1, upload it to their corporate Google Site, and assign a mandatory course in Workday for everyone to read and attest to the new guidelines. This top-down approach is well-intended but misaligned with actual needs and workflows.

Conversely, the bottom-up approach is one many security teams experience firsthand. A few developers signed up for Claude because they've used it before, and Sonnet's performance with code generation has outpaced ChatGPT. They're trying to move fast and get results. But without approved guidance or visibility, this usage happens in the shadows. The team benefits from the tool, but the security team is left without context, oversight, or a straightforward way to respond to the risk.

It's clear that both top-down policy enforcement and bottom-up tool adoption come from a place of good intent but miss the bigger opportunity. Top-down efforts often lack insight into how tools are actually used and why. Bottom-up decisions may prioritize productivity but overlook broader risks. Without understanding real-world usage and its intent, both sides are left guessing and misaligned, creating a security culture gap.

A trust-based culture aligns security teams and the broader workforce. It treats security as a guide and starts by building visibility into how and why tools are being used rather than just what's being accessed. When security leaders understand the real needs behind behavior, they can tailor policies that feel relevant and fair. When employees understand the risks and rationale behind guidance, they're more likely to follow it.

It's time to shift policy from a document to a dialogue, from a rulebook to a shared operating system. This is where the cognitive security approach to cybersecurity policy begins: an iterative process that enables this shared understanding at every stage and focuses on intention and behavioral guidance as the foundation.

Redesigning the Policy Loop for Behavior Orientation

Remember, cybersecurity is a UX problem to be solved, and your policy process is a great place to start. Security teams can move beyond the pejorative "policy pusher" label by embracing a model that prioritizes usage patterns paired with intent to unlock actionable visibility into human risk. It's a process meant to shift policy from a static artifact to a dynamic tool for behavior change, especially when supercharged with a policy behavioral engine like Maro. Let's break it down.

1. Observe

The best security leaders don't start with a policy, they start with the people. They begin by understanding real-world usage: What are employees using? When? Why? And how are those tools helping teams meet their goals? Equally important, what risks do these behaviors introduce? The goal is to surface both the business value and the security tradeoffs. You want to see the whole picture before making decisions that affect everyone.

Observation is insight-gathering. It means talking to employees, watching real behavior, and understanding intent. Think of it like user research. If you can't directly observe usage, then firsthand interviews with frontline employees are a great substitute for understanding user intent. This ensures specific controls are in place throughout the use of any technology.

Real-world example: When Walmart's Jerry Geisler faced the challenge of external generative AI usage, he monitored requests submitted to external platforms instead of outright blocking them. By observing employee activity, he could understand their goals, enabling him to provide better resources and guidance.

2. Author

Now it's time to actually author your cybersecurity policy. In addition to understanding real use cases and the business value of tech usage, your policies should also reflect your company's values, risk posture, and regulatory obligations. Of course, common cybersecurity control frameworks like NIST CSF, CIS Controls, SOC 2, and ISO 27001 provide valuable scaffolding for baseline security practices, but they also tend to focus heavily on checkbox-oriented controls.

What's missing is a framework that targets measurable behavior change and aligns to risk and threat context, not just control presence. Consider these 4 dimensions:

• Your company's value creation model. This identifies critical workflows, assets, and interactions that drive business outcomes. Cybersecurity policy should be a strategic enabler, protecting what powers the business while guiding secure operating behaviors across teams.
• Your regulatory requirements. A well-crafted security policy integrates regulatory controls into system design, access governance, and behavior guidance, ensuring compliance is maintained without disrupting productivity.
• Your organization's risk appetite. Risk appetite sets thresholds for acceptable exposure, but policy defines how those thresholds are enforced. Cybersecurity policy ensures that day-to-day operations stay within defined safety margins by mapping risk tolerance to specific controls and behavioral expectations.
• Your organization's real-world threat model. A threat model identifies likely adversaries, attack vectors, and system weak points. Cybersecurity policy should translate that model into prioritized controls and secure behavior requirements, targeting genuine human risk attack chains like credential theft, insider misuse, or data exposures with actionable safeguards.

Ultimately, a cybersecurity policy should be a guidebook for secure behavior. The more it connects abstract risk concepts to day-to-day decisions, the more effective it becomes at shaping how people work. 

BONUS: Embedding behavior into policy with a Good, Better, Best orientation:

• Good: Provide clear dos and don'ts or basic behavioral guardrails, such as "use your corporate email account for log-in" for a SaaS service or "turn off data sharing with AI models." These define the minimum acceptable behavior standards.
• Better: Define approved and denied use cases by department or role, such as "Sales may use sanctioned cloud sharing apps" while "Engineering must not use AI code assistants without review." This creates context-aware guidance that aligns with how teams operate.
• Best: Dynamically tailor policy to surface adaptive behavior expectations based on real-time cognitive risk factors. For example, you could show elevated warnings or require additional verification when a user shows signs of phishing susceptibility or interacts with sensitive systems.

3. Train

With a usage policy in place, the challenge shifts to education: helping people understand it, internalize it, and apply it in the flow of their work. Too often policies exist only as legal documents, rarely translating into a human-friendly guide that intersects with user behaviors or intent. And security teams typically roll out standardized training throughout the year, but sporadic sessions rarely provide lasting awareness.

To be effective, policy education must evolve from compliance communication to behavioral enablement. Most people don't wake up wondering what the policy says, they care about getting their work done. Policy rollout should focus on making secure behaviors easier, more transparent, and more intuitive. This means:

• Continuous communication instead of annual trainings and ornamental quizzes
• Contextual prompts that surface guidance when decisions are made (not weeks later)
• Interactive walkthroughs that mirror real tasks, not abstract hypotheticals

4. Constrain

When risky patterns emerge, like policy violations, security's role is to guide behavior back into safe territory. That guidance often takes the form of constraints: alerting the user of their risky behavior, providing a better pathway, introducing friction as a "pause and think" mechanism, or requiring additional approval, to name a few.

But those interventions are nearly impossible to deploy without granular, behavior-level visibility. Security teams fall back on blanket technical controls because they lack the insight to do anything else. This leads to frustration, workarounds, and misalignment between policy and productivity. The goal is course correction over control. The most effective constraints are proportionate to the risk, specific to the behavior or use case, and transparent with a path to regain trust or access. Consider a cognitive security solution to help you guide real-time interventions.

Take the UK law firm Hill Dickinson: After detecting tens of thousands of ChatGPT queries in a week, they didn't kill access completely, they just gated it and introduced a request-based reactivation process. This ensured the tool remained controlled without restricting innovation.

5. Report

The final piece of effective policy implementation is reporting. This step enables security teams to assess policy adherence, identify high-risk patterns, and continuously improve behavioral controls. Historically, this level of insight has been out of reach. Most organizations track tool usage or system-level telemetry but not human decision-making in context.

This highlights a persistent challenge: you can't shape what you can't see. While many security tools generate detailed logs of application usage, few offer real-time visibility into how individuals engage in risky decision-making, bypassing guidance, or interacting with interventions.

Behavioral reporting should answer questions like:

• Who is consistently operating within secure norms, and who is trending toward risk?
• Which teams are struggling to apply policy in practice?
• Are behavior-shaping efforts (like guidelines, interventions, or constraints) actually working?

With the right reporting, security becomes a coaching function: it surfaces signals, offers timely feedback, and closes the loop between policy, practice, and performance.

From Policy Pushers to Behavior Partners

The days of static, audit-driven security policies are coming to an end. In a world of rapid technological change, policy must respond to the speed of people and human behavior. That's the core of behavioral governance: treating policy as a living, adaptive layer of the employee experience that flexes with new tools, emerging risks, and real-world workflows. Achieving this shift means repositioning security leaders from policy enforcers to behavior partners—not writing rules in a vacuum but building systems people trust and rely on.

This mindset is foundational to cognitive security. It begins with policies that reflect how people work, adjust to context, and build trust through clarity, collaboration, and real-world relevance. In our next blog post, we'll take this exact approach and show you how to craft a behavior-forward AI usage policy. 

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