# Bharat Sharma

Bharat Sharma is a technology leader who writes about practical uses of AI in cybersecurity and business operations. Sharma also says they are building A1 Logix, exploring AI for logistics, including route planning and delivery visibility.

- Profile: https://b2binfluencer.marketing/creators/bharat-sharma/
- LinkedIn: https://www.linkedin.com/in/bharat-b-sharma
- LinkedIn followers: 722
- Topics: artificial-intelligence, supply-chain, cybersecurity, ai-governance, agentic-ai
- Based on 11 dated posts, latest 2026-10-07

## Summary

Bharat Sharma is most useful for grounded commentary on AI in cybersecurity and operations, not product results.

- Best for: A cybersecurity vendor planning thought leadership on agentic AI in security operations.
- Not for: A video led logistics demand campaign needing named customer results and pipeline metrics.
- Check first: Confirm availability, terms, audience evidence, and whether A1 Logix can be named publicly.

### Takeaways

- Rule-based automation breaks down against unpredictable attackers, so an agent layer should run the investigation.
- Autonomy has to be earned: least-privilege access, defined action boundaries and human approval for high-impact calls.
- AI is a force multiplier for stretched security teams and a new attack surface.

## Main ideas

- **Rule-based automation breaks down against unpredictable attackers, so an agent layer should run the investigation**: Sharma's central security argument is that traditional automation follows the pattern "if X happens, perform Y," which works for predictable events but not for attackers who avoid predictable paths. An agent, in this account, gathers context, selects the right tools, performs investigative steps, evaluates evidence, and recommends or executes approved responses. Sharma lists the work this covers: continuous threat hunting across disconnected data sources, alert enrichment and prioritisation, phishing investigation and response, identity and access reviews, vulnerability prioritisation based on business context, and incident reporting and evidence collection.
- **Autonomy has to be earned: least-privilege access, defined action boundaries and human approval for high-impact calls**: Sharma argues that agents able to isolate devices, revoke credentials or block traffic introduce operational risk. Production agents therefore need least-privilege access, approved tools, clear action boundaries, audit trails, validation checks and human approval for high-impact decisions. The goal is a "digital teammate" that handles time-consuming investigative steps at machine speed while people make consequential decisions. The same governance logic runs through Sharma's broader AI writing: AI requires governance, transparency, security and accountability, while judgment, empathy and values remain with people. The post makes a strong case for autonomous investigation while firmly limiting what an agent may decide alone.
- **AI is a force multiplier for stretched security teams—and a new attack surface**: Sharma rejects the idea that AI mainly replaces security analysts; the bigger opportunity, they argue, is helping teams move faster and more precisely. The pressures include millions of security events a day, attack surfaces spanning cloud, endpoints, OT, IoT and SaaS platforms, and attackers using automation and AI. The promised gains are prioritising critical threats, reducing alert fatigue, accelerating investigations, spotting unusual patterns across large environments, improving response times and giving better visibility into enterprise risk. Sharma puts the shift plainly: the challenge is no longer collecting security data but turning it into actionable decisions. The same technology also brings risks: AI-powered phishing and social engineering, deepfakes and identity manipulation, prompt injection, model security and governance, and data privacy and intellectual property exposure. Sharma rejects an "AI versus humans" outcome in favour of security professionals empowered by AI and supported by governance.
- **AI is reshaping the workforce; responsible adaptation is the open question**: Sharma compares AI to the arrival of computers, the internet and mobile: repetitive work disappears, decisions improve and teams move to higher-value problems. They acknowledge that some tasks will disappear, some roles will evolve and a few jobs will be lost, while new work emerges in AI governance, cybersecurity, data quality, process redesign, customer experience, cloud engineering, ethics and human-AI collaboration. Adoption is settled; the open question is whether organisations adapt responsibly. AI can calculate and automate but lacks human wisdom, empathy and values, so responsibility stays with people. Sharma's refrain: humans will not be replaced by AI, but those who learn to work with it will outperform those who ignore it.
- **Technology creates value when it solves a real operational problem, not simply because it is novel**: In one post outside security, Sharma describes building A1 Logix and applying AI to logistics: smarter route planning, fewer delays and less operational waste, real-time visibility into deliveries and operations, and more time to focus on customers and growth. The principle: technology creates value when it solves real operational problems. Sharma says they are building A1 Logix but gives no product detail, customers, scale, metrics, funding or company link. The broader AI post uses the same operational framing, listing logistics among the areas where AI is already having practical impact.

## Work to read

- [From Reactive Defense to Autonomous Investigation: The Rise of Agentic AI in Cybersecurity](https://www.linkedin.com/posts/bharat-b-sharma_from-reactive-defense-to-autonomous-investigation-activity-7486821490512830464-7_oC)
- [AI is rapidly changing cybersecurity — but not in the way many people think](https://www.linkedin.com/posts/bharat-b-sharma_ai-is-rapidly-changing-cybersecuritybut-activity-7467273349853982720-42py)
- [AI is not just another technology wave](https://www.linkedin.com/posts/bharat-b-sharma_ai-is-not-just-another-technology-wave-it-activity-7474287054839816192-zF-h)
- [AI is becoming part of every conversation — building A1 Logix](https://www.linkedin.com/posts/bharat-b-sharma_ai-is-becoming-part-of-every-conversation-activity-7465438617893617664-OziN)

## Brand relationships

- **A1 Logix** (owned_business)

## Partnerships

No sponsorship program, media kit, rate card, or published partnership terms appear in the posts. The four posts offer educational commentary on AI in security and business operations, not promotional placements: none discloses a brand partnership or describes a commercial format a buyer could book. Sharma’s LinkedIn profile is the only identified contact surface, and it offers no partnership inquiry route. The posts cover agentic AI in security operations, alert triage and investigation, identity and access review, and governance limits on autonomous security tools. A vendor proposing a thought-leadership webinar would be asking them to extend an existing format. Before planning a campaign, ask Sharma to confirm availability, scope, compensation, and disclosure requirements.

## Alternatives

- Makarand Karanjalkar: Choose Karanjalkar when the session's centre of gravity is the data model behind behavioural detection rather than the investigation workflow. One post works through the Open Cybersecurity Alliance Indicators of Behaviour subproject under OASIS: a vendor-neutral way to represent how an attacker behaves, built on STIX 2.1 and covering initial access, privilege escalation, credential access, lateral movement, persistence, command and control, and data staging and exfiltration, so behavioural observations can be shared across security products. The argument is that attackers quickly change malware, addresses, domains and infrastructure, but changing behaviour is harder because they still have to reach the same objectives. The second post draws a distinction that matters to buyers: behaviour analytics answers whether something is unusual, while behavioural threat intelligence asks what adversarial objective the activity represents, which attack stage it sits in, how it relates to activity in other domains, and what the attacker is likely to do next. It names Microsoft Defender, CrowdStrike Falcon, Splunk, Exabeam, Darktrace and Google Chronicle as examples of platforms that stop at the anomaly. For a vendor selling detection, correlation or threat-intelligence capability, or an audience of detection and threat-intelligence engineers, this is a sharper and more contestable argument than Sharma's agentic-investigation walkthrough.
- Michelle Woodward Hodges: Choose Woodward Hodges when the buyer is a channel business rather than an end-user security team, or when the session needs to speak directly to partners. Both posts are written from inside a vendor's partner motion. One describes what partners ask about AI—governance, AI-enabled security practice, and AI fluency as distinct from buying tools—and names Barracuda IQ, the AI engine and intelligence layer behind the BarracudaONE platform, alongside natural-language capability, threat intelligence and privacy protections. It explicitly argues that AI should give security professionals more time for work that needs human judgement. The other argues that most breaches still start with something unsophisticated: a weak credential, an exposed service, or an account that should have been disabled months earlier. Attackers often do not break in; they simply log in. It sets out an ongoing managed-service approach to identity and access: reviewing privileged, inactive and third-party accounts, MFA and SSO, least privilege and zero trust through IAM and PAM, continuously monitoring login activity, and finding exposed services before attackers do. If the need is MSP enablement, managed services, or identity security sold as a recurring service line, this is closer to the buyer's own business than Sharma's agentic-operations frame.
- Okan YILDIZ: Choose Yildiz when the session needs to teach a task rather than argue a position. One post lays out a Microsoft Sentinel home lab that runs from an empty Azure environment to a working SOC: subscription and resource group, Log Analytics workspace, enabling Sentinel, auditing, health monitoring and retention, connecting Windows security logs, understanding tables, fields and event IDs, basic KQL, hunting failed logins on Event ID 4625, turning that query into an analytics rule, triggering and validating the first alert, investigating the incident, reviewing entities, logs, evidence and timelines, deciding true or false positive, and building automation rules and playbooks. It closes with the claim that the real skill is not creating an alert but explaining why it matters and what should happen next. The other post is an 18-page field manual on reading pentest reports, organised around attack paths: where the attacker started, the final impact, which weaknesses enabled the path, how each finding is reproduced, what evidence supports it, which remediation breaks the chain, what could not be tested, and whether the retest proved the path was actually closed. It also offers a debrief question that turns offensive testing into detection engineering: which of the tester's actions should our controls have detected. For a live demonstration, hands-on exercise or detection-engineering session, this gives the vendor more to do than a speaking slot.

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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/