Mind the Gap: Training Your Team for the AI-SOC Era
How to Train a Cyber Team
That Can Question the Machine
Take the AI-SOC Training Guide With You
Getting Security Analysts Ready for the AI-SOC Era
AI is rapidly transforming cybersecurity, but it brings with it a new set of challenges that demand clear strategy, technical precision and strong human oversight. We have seen firsthand how organizations can stumble by treating AI as a silver bullet, relying too heavily on autonomous systems without understanding their blind spots. It starts by facing three of the most urgent AI-SOC challenges we’re seeing today:
Challenge 1: Over-reliance on independent AI functions
Without the right checks and balances, even the most advanced models can lead to misjudgments, missed threats or a false sense of security. As a managed services partner, our goal is to help clients implement AI responsibly, with guardrails that reinforce—not replace—human intelligence. At the same time, AI adoption is widening the skills gap faster than most institutions or internal teams can keep up.
Challenge 2: Risk from investing too much to offset current AI-SOC shortcomings
AI is advancing quickly, but that doesn’t mean every solution is ready for prime time—or worth a blank check. At TekStream, we’ve seen organizations rush to plug gaps in their cybersecurity stack with heavy AI investments, only to find the tools underdeliver or create new complexities. Overcompensating for AI’s current limitations with oversized budgets or reactive spending can actually increase risk, draining resources from proven strategies and exposing critical blind spots.
Challenge 3: To supervise intelligent systems, we’ll need even more intelligent operators
Today’s SOCs require cybersecurity professionals who understand not only traditional tools, but also how AI-driven systems make decisions. TekStream is bridging that gap through workforce solutions that train over the curve, combining hands-on experience and just-in-time learning through our private-public partnership with top-tier universities.
This guide outlines how to meet the moment with practical strategies, sustainable training pipelines and a forward-thinking approach to cybersecurity resilience in the age of AI.
Over-Reliance on Independent AI Functions
When implementing AI solutions for cybersecurity, there’s a significant risk in training these systems to operate independently, fostering unwarranted over-reliance. It’s critical to maintain a balance between automation and human oversight. Relying too heavily on AI to operate independently can create a false sense of security. These systems are valuable for accelerating detection and response, but they’re not immune to blind spots, especially when facing novel or targeted attacks.
Without ongoing human involvement, we risk missing high-impact threats or making costly misjudgments. To ensure resilience, AI should enhance, not replace, our security team’s capabilities, helping them respond faster and more effectively while maintaining critical human judgment at the core.
TekStream Strategy
TekStream’s AI adoption strategy for MDR is unique in its emphasis on trust-first deployment through a human-in-the-loop augmentation model. While many MDR vendors rush to automate detection and response through AI, TekStream prioritizes analyst validation, phased delegation of responsibility and progressive trust-building without relying on opaque models or offloading to unvetted automation. This ensures that AI-enhanced workflows evolve in tandem with human expertise, reducing the risk of blind reliance or erroneous escalation while maintaining operational accuracy and client confidence
What Executives Should Consider
There is a real risk that critical threats will slip through an AI-powered cybersecurity system undetected, or that the system will misclassify legitimate activity as malicious. Primary considerations for executives include:
- Balance AI automation with human oversight to avoid creating blind spots or a false sense of security. AI should accelerate decisions, not make them in isolation.
- Recognize that AI is not foolproof, especially against novel or targeted threats. Human expertise remains essential for interpreting context and managing complex incidents.
- Invest in AI as a force multiplier, not a replacement. Focus on solutions that enhance your security team’s capabilities and preserve resilience through layered defense.
What Technical Teams Should Consider
AI can significantly streamline threat detection and response, but letting it run without oversight introduces risk. If we train these systems to act too independently, we risk over-relying on models that can’t always recognize new or context-specific threats. AI is only as good as its data and tuning: zero-days, adversarial inputs, or subtle behavior changes can throw it off.
To stay resilient AI should support and not replace the expertise and judgment of security teams. Human analysts are still essential for interpreting context, making nuanced decisions and responding to complex incidents. Your team can get started today:
- Watch the OWASP Top 10 for LLMs
- Establish standards that define acceptable use of GenAI by your cybersecurity team
- Create protocols that identify and inventory unauthorized AI usage
- Align with the NIST AI Risk Management Framework
- Adopt ISO/IEC 23894 or other relevant AI security standards
Risk of AI-SOC Shortcomings
There’s also a real risk in over-investing to compensate for AI’s current limitations. Throwing more resources at immature or unproven AI capabilities can lead to diminishing returns and misaligned priorities. Instead, we should focus on smart, targeted investments that complement existing security processes and allow room for the technology to mature.
TekStream Approach
TekStream’s AI is embedded with a mature ecosystem rooted in Splunk Enterprise Security and SOAR. Taking the retrofit stance that uses AI to patch limitations in an underdeveloped MDR platform is not our style.
The AI supports, rather than replaces, core processes, focusing initially on SA1-level tasks like enrichment, risk scoring and preliminary triage. Automation then expands into controlled SA2/SA3 zones only after runbook validation and accuracy benchmarks are achieved.
TekStream also avoids proprietary AI overlays, instead leveraging open frameworks and customer-governed logic to ensure transparency and auditability of AI-driven decisions. We apply AI selectively on a customer/use case basis and can elect to automate detection, analysis, containment and eradication wherever it makes sense.
What Executives Should Consider
Your firm may want to approach AI investment with a long-term, risk-managed mindset. It’s easy to be swayed by the promise of AI as a quick fix, but over-investing in emerging AI tools that aren’t yet reliable can drain budgets, misalign priorities and leave real gaps in your security posture.
- Avoid hype-driven spending: Allocate budgets based on risk reduction and measurable outcomes, not vendor promises.
- Support hybrid strategies: Invest in AI that augments human analysts, not replaces them. Prioritize tools that integrate into existing workflows.
- Build flexibility into budgets: Given the rapid evolution of AI, maintain agility to pivot when tools don’t perform or when better options emerge.
- Measure ROI carefully: Demand clear performance benchmarks and risk reduction metrics tied to any AI deployment.
What Technical Teams Should Consider
Technical teams should avoid over-engineering solutions or relying too heavily on AI to fill current capability gaps. Overcompensating for AI limitations with complex architectures or excessive tuning can introduce new risks and strain operations.
- Focus on integration, not perfection: Choose tools that work with your existing stack and allow for human validation.
- Avoid black-box reliance: Prioritize AI solutions that provide transparency and explainability.
- Balance automation with control: Implement safeguards, thresholds and fallback procedures to catch false positives/negatives.
- Continuously evaluate: Conduct post-incident reviews to assess whether AI-enhanced workflows are improving outcomes or adding noise.
Need for More Intelligent Operators to Supervise AI-SOC
As AI becomes more embedded in cybersecurity operations, the bar for human oversight is getting higher. These systems aren’t just running basic playbooks anymore, they’re helping make real-time decisions about threat prioritization, risk scoring and incident response. That means the people working alongside them need to be able to spot when the AI gets it wrong, even if the output looks polished or confident. Doing that well takes a deep understanding of both cybersecurity and how AI models operate. In fact, it may take expertise at the level of the top 10% in the field to reliably detect the subtle mistakes these tools can make.
This creates a serious challenge for how we train the next generation of cybersecurity professionals. The more capable AI becomes, the more demanding the human role becomes, not just in using these tools, but in questioning them. It’s not enough to know how to operate a SIEM or write detection rules anymore. Today’s analysts need to think adversarially, understand machine learning mechanics, and know how to vet AI-driven decisions under pressure. That’s a big shift.
So the question is: Are our universities and technical programs ready to deliver that kind of training? And if they aren’t, how quickly can they evolve to close the gap? Because the longer we wait, the bigger the risk that AI outpaces our ability to use it responsibly.
TekStream Approach
Right now, TekStream’s job is to keep our customers safe with the best tools available and to prepare students to effectively use those tools in their SOC environments, before and after they graduate. The advantage we have is that we are relying upon the private-public partnerships we’ve formed with top-tier higher education institutions across the country, incorporating just-in-time (JIT) cybersecurity training with advanced incident response tools, to react quickly to this accelerating degree of change. As AI becomes more deeply integrated into cyber defense, we have a responsibility to take the lead in developing the next generation of cyber talent.
Universities are unprepared for the rate of change this introduces into student preparedness. TekStream, out of necessity, must keep up with the latest innovations in security. Our solution is purpose-built to append academic curricula with a layer of practical skills and innovation that are applicable within security. We have taken the challenge of evolving our workforce development to keep pace with that rate of change.
Looking ahead, our role will evolve from enabling security operations to shaping the future SOC. TekStream training programs will teach students how to audit, guide and challenge intelligent systems. This means expanding our training frameworks to include AI fluency, adversarial thinking and ethical oversight, while continuing to innovate in how we deploy real-time SOC environments.
The launch of our Digital Resilience Center and our AI Lab are poised to become a national model for that role.
What Executives Should Consider
You can’t afford to assume that automation means less human involvement. It actually demands deeper expertise and sharper oversight. If your workforce and education partners aren’t keeping pace, you are potentially exposing the firm to long-term risk.
As you turn risk into resilience, ask your team:
- Are we equipped to interpret and challenge AI-driven decisions right now?
- Do we have a clear plan for upskilling or recruiting talent with advanced AI-cybersecurity expertise?
- What internal training or partnerships can we build to close this skills gap sooner?
- How do we ensure that AI strengthens, rather than replaces, critical thinking in our security operations?
What Technical Teams Should Consider
This reinforces what many technical teams are experiencing on the ground: AI is powerful, but it’s not plug-and-play. The better it gets, the more you’ll need people who can challenge its decisions, understand how the models work and catch what the system might miss. That’s a different skill set than traditional SOC work, and we can’t assume everyone’s ready for it.
As you assess your readiness ask these questions:
- Do you have team members who can identify when an AI tool is making flawed or biased decisions?
- Are you training staff on how AI models function, not just how to use the tools?
- How should you build workflows that keep a human in the loop without slowing things down?
- What processes are in place to validate and audit AI-generated security outcomes?
- Can you partner with universities or vendors to shape training programs that match real-world needs?
The AI SOC is here. The future is bright.
Responsiveness to change is the greatest test to AI adoption, no matter the end result. We are preparing our teams to build resilient, AI-ready cybersecurity teams, bridging the skills gap with hands-on experience, industry collaboration and a commitment to securing what’s next.
We’re here to help you do the same.
Read about our approach to cybersecurity.