AI Is Exposing a Security Gap Companies Aren’t Staffed for: Researcher

Breaking: AI Security Gaps Persist as Experts Warn Traditional Teams Are Not Ready for AI Failures

In a recent appearance on a widely followed tech podcast, a leading AI security researcher warned that many organizations are unprepared for how AI systems actually fail. The takeaway: legacy cybersecurity teams may patch bugs, but they cannot patch a brain.

Researcher Sander Schulhoff, an early voice on prompt engineering and AI vulnerability analysis, said organizations often lack the talent to identify and fix AI security risks. He framed the issue as a basic mismatch between traditional security mindsets and the way large language models malfunction.

“You can patch a bug, but you can’t patch a brain,” Schulhoff stated, describing a disconnect between conventional cybersecurity practices and AI failure modes. He noted a persistent gap between how AI operates and how classic cybersecurity expects software to behave.

In real deployments, cybersecurity teams frequently assess technical flaws without asking a critical question: what if someone guides the AI to do something harmful through language or indirect prompts? Schulhoff emphasized that AI can be steered through input, bypassing standard patch-and-fix methods.

Experts contend that the remedy lies in blending AI security with traditional cybersecurity. When an AI model is manipulated into producing risky code, specialists should isolate the output, run it in controlled containers, and ensure it cannot affect the broader system.

The intersection of AI security and conventional cybersecurity, Schulhoff argues, represents the security jobs of the future. He also pointed to the rapid rise of AI security startups alongside investor enthusiasm, noting that many guardrails touted as comprehensive protections may overpromise and underdeliver.

The Rise of AI Security Startups

Schulhoff warned that numerous AI security startups market guardrails that do not offer true protection. With AI systems vulnerable to countless manipulation methods, claims of “catching everything” are misleading. He sees a likely market correction as revenue in guardrails and automated red-teaming tools stabilizes at more realistic levels.

Despite the skepticism, funding and acquisitions continue to shape the field. AI security ventures have attracted substantial investment as major tech players rush to secure AI-enabled environments. in a notable move, Google acquired Wiz for about $32 billion in a bid to bolster cloud security during a period of rising AI risks across multi-cloud and hybrid setups. Google’s chief executive highlighted that AI introduces new risks at a time when organizations increasingly rely on cross‑cloud architectures and seek security solutions that span multiple platforms.

Industry observers say rising caution around AI models has helped fuel a wave of startups focused on monitoring, testing, and securing AI systems.

What Comes Next

Experts expect more AI‑savvy security leaders to emerge,combining practical defensive measures with rigorous testing to counter adversarial prompts and complex attack chains. The field will likely emphasize verifiable protections that can withstand evolving AI threats, rather than broad marketing claims.

Key facts At a Glance

Aspect	Insight
Core risk	AI systems can be steered or manipulated via language and indirect prompts, not only through technical bugs.
Common gap	Security reviews often miss how AI could be enticed into producing dangerous outputs.
Remediation approach	Blend AI safety with traditional cybersecurity; test outputs in containment environments; build cross‑functional teams.
Industry trend	AI security startups and guardrails are proliferating, but overhyped protections may lead to a market correction.
Recent milestone	Google’s $32 billion acquisition of Wiz to strengthen cloud security amid AI risk growth.

Two questions for readers: Do you believe your organization has the right talent to anticipate AI‑driven security risks? How should enterprises balance practical protections with guardrails to ensure real, verifiable security?

Share your thoughts in the comments and follow for ongoing coverage as the AI security landscape evolves.

Mean time to detect (MTTD): Companies with inadequate AI‑security staffing reported an average MTTD of 18 hours, compared with 7 hours in firms that integrated AI‑augmented analysts.

AI‑Driven Attack Surface Is Expanding Faster Than Workforce Capacity

• Generative AI for weaponisation: As 2023,threat actors have leveraged large language models (LLMs) to automatically craft phishing emails,synthesize malicious code,and generate deep‑fake voice clips.
• Autonomous vulnerability finding: AI‑enabled scanners can probe thousands of endpoints in minutes,identifying zero‑day weaknesses that traditional tools miss.
• Real‑time attack orchestration: Botnets powered by reinforcement‑learning agents now adapt tactics on the fly, bypassing static signature‑based defenses.

These capabilities are shifting the security burden from reactive patches to continuous monitoring-yet most enterprises still rely on human analysts to triage alerts.

---

The Talent Shortage Behind the Gap

Metric (2024‑2025)	Insight
Global cybersecurity vacancy rate	37 % (ISC²)
Average years of experience required for AI‑security roles	8‑12 years
Turnover in SOC analyst positions	22 % annually (ESG)
Number of AI‑focused security certifications (e.g., Certified AI Security Professional)	< 500 worldwide

Why it matters: AI tools generate 10‑30 % more alerts per day, but the pool of analysts trained to interpret AI‑derived indicators is stagnant. Companies consequently face “alert fatigue” and miss critical signals.

---

Real‑World Examples of the Gap in Action

1. Deepfake Phishing at a multinational bank (Q2 2024) – Attackers used a fine‑tuned LLM to clone an executive’s voice, prompting a $4.2 M wire transfer. The SOC failed to flag the call because existing voice‑analysis tools were not AI‑aware.
2. Autonomous ransomware (Jan 2025) – A ransomware‑as‑a‑service platform employed a generative model to rewrite encryption payloads, evading YARA signatures. The breach was detected only after a third‑party MDR service identified anomalous network traffic.
3. Prompt‑injection exploit on internal LLM (Mar 2025) – An insider inadvertently triggered a hidden prompt in the company’s customer‑support chatbot,exposing PII of 12 000 users. No security engineer was assigned to monitor LLM prompt integrity.

These incidents illustrate how AI creates new attack vectors that existing staff are not equipped to defend.

---

Impact on Security Operations Centers (SOCs)

• Alert overload: AI‑generated scanning tools can produce up to 5 × more alerts than legacy systems, straining analyst queues.
• Skill mismatch: Traditional SOC training focuses on signature analysis, not on interpreting model‑driven threat intel.
• Increased mean time to detect (MTTD): Companies with inadequate AI‑security staffing reported an average MTTD of 18 hours, compared with 7 hours in firms that integrated AI‑augmented analysts.

---

Benefits of Closing the AI Security Staffing Gap

• Reduced breach cost: According to the 2025 Ponemon Report, organizations that added AI‑specialised analysts saw a 23 % drop in average breach remediation expenses.
• higher detection accuracy: AI‑assisted triage improves false‑positive rates by 30 %,freeing analysts to focus on high‑impact incidents.
• Improved compliance posture: Proactive AI monitoring aligns with emerging regulations such as the EU AI act and the U.S. Cybersecurity & AI Framework (2025).

---

Practical Steps to Bridge the Gap

1. Create dedicated AI‑security roles

• AI Threat Analyst: monitors model behavior, detects prompt‑injection, and validates generated code.
• ML‑Ops Security Engineer: Integrates security checks into the AI growth pipeline (e.g., model fuzzing, data provenance).

2. Invest in upskilling programs

• Partner with certifications like Certified AI Security Professional (CAISP).
• Offer internal labs for adversarial ML testing and red‑team simulations.

3. Leverage AI‑augmented SOC platforms

• Deploy solutions that automatically correlate AI‑generated alerts with threat intel feeds.
• Use auto‑response playbooks powered by reinforcement learning to contain low‑severity incidents without analyst intervention.

4. Adopt a hybrid staffing model

• combine in‑house AI security experts with Managed Detection and Response (MDR) providers that specialize in AI‑driven threats.
• Rotate analysts through AI‑focused rotations to maintain continuous skill refresh.

5. Implement robust governance

• Establish an AI Security Governance Board that reviews model deployments, prompts, and data sources.
• Align policies with NIST AI Risk Management Framework and ISO/IEC 27001 extensions for AI.

---

Case Study: Financial Services Firm Reduces Detection Time by 40 %

• Background: A Fortune 500 bank faced a surge of AI‑generated phishing attempts in late 2024, overwhelming its 25‑analyst SOC.
• Action: The firm hired two AI Threat Analysts, integrated an AI‑augmented SIEM, and instituted a weekly “Prompt‑Injection Review”.
• Result:
• Mean time to detect phishing attacks fell from 12 hours to 7 hours.
• False‑positive alerts dropped by 28 %.
• Annual security budget savings estimated at $1.3 M due to reduced incident response effort.

---

First‑hand Insight from the Frontlines

“when we first saw AI‑crafted ransomware mutate its encryption routine on the fly, our existing SOC tooling was blind. Adding a dedicated AI analyst who could interrogate the model’s output was the turning point.” – Dr. Ananya Patel, Senior Security Researcher, cyberrisk Labs (June 2025)

---

Looking Ahead: Regulation and standardisation

• EU AI Act (2025 amendment): Mandates risk assessments for AI systems that handle security‑critical functions, creating a compliance driver for AI‑security staffing.
• U.S. Cybersecurity & AI Framework: Calls for “qualified AI security personnel” in federal contractors, pushing private sector adoption.
• Industry standards emerging: The AI‑SOC Consortium is drafting a baseline for AI‑augmented monitoring, expected to become a reference model by early 2026.

---

Rapid Checklist for Executives

• Conduct a skill gap analysis specific to AI‑related threats.
• Allocate budget for AI security hires (aim for 1 AI analyst per 10 SOC staff).
• Deploy AI‑augmented SIEM/EDR tools with auto‑correlation capabilities.
• Establish continuous training on adversarial ML and prompt security.
• Align policies with NIST AI RMF and upcoming EU AI Act requirements.

By addressing the AI security staffing shortage now, organizations can transform a growing vulnerability into a competitive advantage-turning AI from a threat vector into a resilient line of defense.