# Steve Faruque

Steve Faruque writes about cybersecurity, including QRadar threat detection and how teams can defend against AI-driven attacks.

- Profile: https://b2binfluencer.marketing/creators/steve-faruque/
- LinkedIn: https://www.linkedin.com/in/strive
- LinkedIn followers: 3,110
- Topics: artificial-intelligence, cybersecurity, agentic-ai, ai-security
- Based on 12 dated posts, latest 2026-10-07

## Summary

Steve Faruque is most useful for plain-language cybersecurity explainers on AI-native attacks and QRadar security operations.

- Best for: Cybersecurity vendors wanting educational explainers on AI-native attack detection, prompt injection, agentic threats, or QRadar investigations.
- Not for: Sponsored conversion-tracked endorsements aimed at enterprise CISOs with guaranteed audience demographics and published rates.
- Check first: Check content balance and ask for recent LinkedIn audience analytics.

### Takeaways

- AI-native attacks are hard to detect because they break three old assumptions.
- Attackers weaponize intended functionality, not bugs, according to Faruque's posts.
- Defenses should be matched to what a team can actually afford.

## Main ideas

- **AI-native attacks are hard to detect because they break three old assumptions**: Faruque's central technical argument is that AI agents do not just add a new target; they invalidate rules security teams have trusted for years. Traditional detection assumes data is not code, trust boundaries are clear, and systems behave deterministically. In agentic systems, they argue, data becomes executable instruction, external and internal context blend together, and models interpret intent rather than follow literal commands. The result is an attack with no malware, privilege escalation, or suspicious network traffic — only legitimate-looking activity on approved domains. They describe the pattern as "cross-site scripting for the AI era": a malicious prompt planted in ordinary business data that fires later, sometimes weeks after it was placed, when a routine query triggers the leak.
- **Attackers weaponize intended functionality, not bugs**: Faruque argues that the economics of attack have shifted. Instead of finding code defects, attackers can use a system's own designed behavior against it: an AI tool follows instructions, and theirs are simply more convincing. They group the resulting breach patterns into four named categories — prompt injection that leads to full system access, agentic intrusions that issue unauthorized database queries and API calls, AI-generated "vibe coded" code carrying hidden SQL or logic flaws, and AI-accelerated zero-days that shrink reaction windows from weeks to hours. They pair the threats with a cost contrast to make the scale legible.
- **Defenses should be matched to what a team can actually afford**: Rather than recommend one toolset, Faruque splits recommendations by budget and maturity. For teams starting out, they suggest delimiter "spotlighting" to isolate model inputs, a security code review checklist for every AI feature pull request, basic request logging with a small number of anomaly alerts, and open-source guardrails. For teams with more resources, they recommend commercial prompt-shield products, behavioral anomaly detection that flags unusually large data access, output filters that block external URLs and excess personal data, and monthly automated red teaming against the OWASP LLM Top 10. The approach gives even a small team with no budget a credible starting point.
- **Inside the security operations center, AI's job is to reduce analyst overload**: Faruque's product-side argument runs in the opposite direction from the attack posts: there, AI is the threat; here, it is a teammate. They start from the human cost of detection work and argue that another dashboard or feed makes the problem worse, not better. In their framing, analysts need clarity, confidence, and speed — delivered by summarizing complex alerts and offenses in plain language, suggesting immediate and long-term response actions, and answering natural-language questions about tactics and indicators. They return to the same theme in release notes, where user and entity behavior analytics are described as surfacing insider threats without drowning analysts in noise.
- **Teaching through events and naming the people behind the work is a stated priority**: Across the posts, Faruque teaches in public with others and gives credit by name. They co-host and promote live sessions, name co-speakers and partner speakers, and, in release material, thank colleagues and attendees who joined a walkthrough. They treat the people behind a product as part of the story, not background. The material follows the same pattern: short hooks, bulleted warning signs or features, one concrete case, and a next step such as a recording link, registration page, or invitation to connect.
- **The ideas rest on others' findings, not on original research**: These posts are explainers, launch material, and event promotion. The technical cases are attributed to outside researchers, and the posts do not present original measurement, a dataset, or a tool built by Faruque. One tension matters to buyers: the same body of writing describes AI agents as a serious new attack surface and promotes AI features for security operations, without addressing that tension directly.

## Work to read

- [Why AI-native attacks are hard to detect early (Salesforce ForcedLeak)](https://www.linkedin.com/posts/strive_why-are-ai-native-attacks-so-hard-to-activity-7430342048425291776-NZhT)
- [Your AI stack is your biggest attack surface—and the defenses to match](https://www.linkedin.com/posts/strive_aisecurity-llmsecurity-devsecops-activity-7404157879781568513-vCMi)
- [Launching QRadar Investigation Assistant on watsonx.ai](https://www.linkedin.com/posts/strive_cybersecurity-ai-watsonx-activity-7335258928953368576-3cWR)
- [QRadar's 2025 release roundup and the people behind it](https://www.linkedin.com/posts/strive_qradar-threatdetection-cybersecurity-activity-7392674003854766080-lyfu)
- [Promoting the QRadar Unlocked webinar with a partner speaker](https://www.linkedin.com/posts/strive_qradarunlocked-cybersecurity-siem-activity-7333682756100481024-nvaW)

## Brand relationships

- **IBM** (employment)
- **Pioneering Minds AI** (mention)
- **CarbonHelix** (mention)

## Partnerships

Steve Faruque's posts offer first-person commentary and event promotion: explaining how AI-native attacks work, describing IBM QRadar releases, and inviting people to webinars and panels where they are speaking. They do not describe a partnership, sponsorship, or paid-collaboration format, or publish an official inquiry route, rate, deliverable, format, audience guarantee, sponsor policy, media kit, rate card, or partnership contact page. An event invitation or speaker slot does not show that a brand can buy access. Brands interested in working with them should ask directly for the terms.

## Alternatives

- Okan Yildiz: A better fit than Steve Faruque when the vendor wants to train a security team to build and validate detection hands-on, rather than explain one class of attack. The reviewed work is procedural. One post walks readers through a Microsoft Sentinel home lab, from creating an Azure subscription and a Log Analytics workspace to enabling Sentinel, connecting Windows security logs, writing KQL, hunting failed logins by event ID, turning the hunt into an analytics rule, and investigating the resulting incident. It frames the exercise as learning SOC operations by building a lab rather than memorising what a SIEM does. The other presents an 18-page field manual on reading a pentest report, arguing that a useful report shows how compromise actually happens: scope, attack path, evidence, root cause, remediation, retest, plus the debrief question of which tester actions the security controls should have detected. Both sit closer to a lab exercise or course than to a vendor explainer.
- Makarand Karanjalkar: A better fit when the vendor wants a strategic or standards-level argument about where threat detection should go, rather than a breakdown of a specific attack. One post explains the Open Cybersecurity Alliance Indicators of Behaviour subproject under the OASIS Open ecosystem, which uses STIX 2.1 to represent how an attacker behaves, covering initial access, privilege escalation, credential access, lateral movement, persistence, command and control, and data staging and exfiltration. The aim is to make behavioural intelligence shareable in a machine-readable way that complements traditional indicators of compromise. The other post distinguishes Behaviour Analytics, which flags that something is unusual, from Behavioural Threat Intelligence, which places that activity in a wider campaign and says what the attacker is likely to do next. For a piece arguing that detection should move from anomaly flagging to understanding intent, this is a stronger starting point.
- Bharat Sharma, MBA: A better fit when the vendor wants an executive-level narrative about agentic AI changing security operations rather than a technical explainer for practitioners. One post argues that human-only investigation cannot keep pace with modern attacks and lays out what an agent could do: review identity and authentication history, examine endpoint activity, correlate network, cloud, and threat-intelligence data, build a timeline, and recommend containment. It then lists guardrails such as least-privilege access, approved tools, action boundaries, audit trails, and human approval for high-impact decisions. A second post steps back to AI as a technology wave, covering governance, ethics, workforce change, and adoption across healthcare, logistics, customer service, and cybersecurity. For a leadership-audience piece on the shift to autonomous investigation and how to govern it, this framing is closer to the target.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/