Cybersecurity Awareness Month Is About More Than Phishing: 4 Priorities for Security Leaders In the Age of AI

Every Cybersecurity Awareness Month, organizations return to the fundamentals: use strong passwords, enable multifactor authentication, keep software updated and think before clicking suspicious links.

Those messages still matter. Phishing remains an important threat vector, and employees should continue to be trained to recognize suspicious emails and links. But for security leaders, awareness needs to go further.

AI is changing both sides of the cybersecurity equation. Organizations are looking to AI to make security operations more efficient and deliver insights to decision-makers faster. At the same time, increasingly capable AI models are changing how quickly vulnerabilities can potentially be discovered and exploits developed.

The security mission hasn't fundamentally changed. Organizations still have to understand their exposure, prevent compromise, detect threats and remediate vulnerabilities. What is changing is the clock.

As we near the end of this Cybersecurity Awareness Month, I encourage security leaders to look beyond employee awareness all year ‘round and ask a bigger question:

Are our security operations ready for an environment in which both defenders and adversaries can operate faster?

Here are four priorities I recommend focusing on now.

1. Use AI to find weaknesses before attackers do

If AI can help uncover vulnerabilities, security teams should take advantage of that capability before their adversaries do.

Organizations developing software should consider bringing AI into their own vulnerability scanning and software development processes. Frontier AI models can supplement traditional vulnerability scanning by helping analyze source code for weaknesses earlier, giving development and security teams an opportunity to address them before software reaches production.

The larger lesson is important: AI shouldn't only be viewed as a new source of cyber risk. It can also become part of the defense.

Security leaders can start by asking where AI can help move security activities earlier in the lifecycle. Finding an issue before deployment is preferable to racing to remediate it once that issue is already sitting in a production environment.

2. Find where vulnerability remediation is actually getting stuck

"Patch faster" is easy advice to give. Making it happen is much harder.

Instead of treating remediation time as a single problem, security leaders should examine the entire process and identify where delays actually occur.

Is the security team struggling to triage a large volume of findings and determine which vulnerabilities deserve attention first? Are findings getting stuck because the responsible owner can't be identified? Could work be better distributed so remediation happens in parallel? Or is the real bottleneck later, when teams need to test a patch and make sure it doesn't break something before pushing it into production?

That last step matters. Applying a patch in a test environment isn't the finish line. You need the testing infrastructure and delivery processes to validate it and move it safely into production. Otherwise, you've identified the fix without eliminating the exposure.

I see AI making this operational discipline increasingly important. More capable models can help uncover vulnerabilities that are difficult to find through human review or traditional automated analysis. Their software-development capabilities can also accelerate exploit creation. That combination can leave defenders with less time to act.

In other words, AI changes the clock, not the mission.

The goal remains remediation before exploitation. The difference is how quickly organizations may need to get there.

3. Make sure your security stack is keeping pace

As attacker techniques evolve, security leaders should also take a hard look at the technologies they're relying on to identify them.

My recommendation is to challenge your vendors on how they're adapting to AI-driven threats, including increasingly complex, multi-step attacks. Don't assume the capabilities you bought yesterday will automatically address the techniques emerging today.

That suggests a useful question for every security leader to put to their strategic vendors:

What's different about your technology today because of AI?

The answer shouldn't simply be that AI has been added to a product description. Security teams should understand whether and how the technologies they depend on are evolving alongside the threats they're intended to detect.

Apply that same scrutiny to your own operations. Metrics such as mean time to detection (MTTD) and mean time to remediation (MTTR) remain relevant. I expect AI to raise the bar as organizations use it to shorten those timelines. The question isn't whether to abandon these measures, but whether your expectations are keeping pace.

But leaders should also think about the incidents that never happen. Prevention is notoriously difficult to quantify, yet understanding whether security investments are reducing the need for breach response can provide another perspective on whether security posture is improving.

4. Don't let AI governance become another silo

Generative and agentic AI have created a new governance challenge. Enterprises need to understand how AI is being used, establish policies for that use and manage the resulting risk.

The temptation is to treat that as an entirely new problem and build an AI-specific governance program around it.

There's a better opportunity.

I recommend looking at AI governance within the context of your broader cybersecurity controls, rather than bolting on a separate operation. Use the urgency around AI to examine the policies, systems and controls that may already need modernization. Address the risks AI introduces, but connect that work to the way you manage security across the enterprise.

That's an especially useful mindset for security leaders because AI doesn't arrive in an organizational vacuum. It touches users, applications, data and business processes that are already governed, or should be governed, by existing security and risk practices.

Use the urgency around AI to modernize security governance, not to create another governance silo.

Awareness should lead to action

Cybersecurity awareness will always include helping employees understand their role in protecting the organization. And yes, "don't click the suspicious link" is still good advice.

But awareness for security leaders must mean something more.

The arrival of more capable AI and frontier AI models gives organizations an opportunity to revisit assumptions about how security operates: how quickly vulnerabilities are discovered, how efficiently they're prioritized and assigned, how rapidly remediation moves into production, how security technology keeps pace with emerging techniques, and how governance adapts to a changing technology environment.

Most importantly, organizations should reconsider how much time they assume they'll have to react.

I expect machine-speed attacks to put more pressure on containment strategies as defenders have less time to intervene after an initial compromise. Response and recovery still matter. But we need to put greater emphasis on preventing compromise, detecting threats earlier and remediating weaknesses before attackers can exploit them.

For Cybersecurity Awareness Month, that's a message worth taking beyond the security team:

AI doesn't change the fundamental mission of cybersecurity. It changes how quickly organizations need to accomplish it.

And the time to prepare for that new clock is now.

Turn awareness into action

Preparing for AI-accelerated threats means knowing where controls fall short, removing delays in remediation and making governance part of everyday security operations. At DataBee, we help teams put those priorities into practice: Continuous Controls Monitoring (CCM) measures control effectiveness and surfaces gaps; Frontier AI Defense connects vulnerabilities to business applications and accountable owners so teams can prioritize remediation; and User Access Review (UAR) brings governed access reviews to modern and legacy applications.

Explore how DataBee’s products can help your team spend less time assembling information and more time acting on risk.