Nice — below is a compact, sourced market brief for AI in Cybersecurity with company references that include numeric values where available, and the sections you requested. I pulled the most recent public financials, press releases and market reports so the numeric claims have citations.

This versatile research report is presenting crucial details on market relevant information, harping on ample minute details encompassing a multi-dimensional market that collectively maneuver growth in the global AI In Cybersecurity market.

This holistic report presented by the report is also determined to cater to all the market specific information and a take on business analysis and key growth steering best industry practices that optimize million-dollar opportunities amidst staggering competition in AI In Cybersecurity market.

Read complete report at: https://www.thebrainyinsights.com/report/ai-in-cybersecurity-market-14793

Company references (companies + public values)

  • CrowdStrike (Falcon) — Total revenue $3.06 billion (FY2024); AI/ML is core to its Falcon detection/response platform.

  • Palo Alto Networks (Cortex, Unit 42, Prisma) — Total revenue ~$8.6 billion (2024); Cortex and other AI-assisted products are strategic growth engines.

  • SentinelOne — Total revenue $621.2 million (FY2024); growing rapidly with AI-driven endpoint detection (FY2025 revenue guidance and later results show continued growth).

  • Darktrace — FY2024 revenue guidance / trading updates pointed to ~$689.5M+ and strong ARR growth in 2024. Darktrace markets self-learning AI for network detection.

  • Orca Security — disclosed / reported revenue milestones ~$64.2M (2024); cloud-threat detection using behavioral/ML approaches.

  • Vectra AI — public statements of rapid, AI-led revenue growth (company press: record growth; private funding $425M raised historically; valuation ~$1.2B earlier rounds).

Market size & Recent development (top-line)

  • Market size (example estimates): The global AI in Cybersecurity market was estimated ~USD 25–26B in 2024 (several market reports cluster around USD 25–26B) and projections show rapid expansion to ~USD 60–94B by the late 2020s / early 2030s depending on source/CAGR assumptions.

  • Recent development: Large incumbent security vendors are embedding AI/ML across their stacks (CrowdStrike, Palo Alto, Microsoft) while specialized AI-native startups (Orca, Vectra, Darktrace, SentinelOne) continue scaling and raising capital. Strategic M&A and acquisitions to add AI capabilities are accelerating.

Drivers

  • Escalating threat complexity & volume — automated, polymorphic attacks and supply-chain/AI-driven threats require AI for scale in detection.

  • Signal volume & cloud scale — cloud workloads and telemetry volumes are too large for manual triage; AI enables prioritized, contextual alerts.

  • Vendor innovation & funding — strong VC / corporate funding into AI-native security vendors fuels product development and go-to-market.

Restraints

  • False positives / model drift — immature models or poor telemetry can produce noisy alerts, eroding trust and increasing workloads.

  • Adversarial attacks on models — attackers targeting the AI itself (poisoning, prompt injection, evasion) create new risk vectors.

  • Regulation, explainability & skills gap — regulators and customers demand explainable decisions and human oversight; security teams still lack AI-skilled staff.

Regional segmentation analysis

  • North America: largest market and fastest adopter — heavy enterprise spend, many vendors & investors headquartered here.

  • Europe: strong demand from telco/finance, rising privacy regulation shapes deployment/architecture. European vendors (Darktrace / Vectra presence) are active.

  • Asia-Pacific: fastest growth rates forecast (large cloud adoption, digital transformation in APAC), but with regional fragmentation and varied regulatory regimes.

Emerging Trends

  • Generative AI security tooling — new products to detect prompt injection, data exfil via LLMs, and to secure AI supply chains. (See vendor M&A & product announcements focused on ‘AI security’).

  • Cloud-native, telemetry-first ML models — shift from signature-based to behavior/telemetry models that operate at cloud scale (Orca, Vectra examples).

  • XDR + AI orchestration — Extended Detection & Response platforms using AI to correlate cross-domain signals (endpoint, network, cloud).

Top Use Cases

  1. Endpoint detection & response (EDR/XDR) — autonomous containment, prioritization (CrowdStrike, SentinelOne).

  2. Cloud workload & posture detection — continuous discovery, misconfiguration and lateral-movement detection (Orca Security).

  3. Network / UEBA / anomaly detection — unsupervised AI for lateral movement and insider threat (Darktrace, Vectra).

  4. AI-specific security — protecting LLM prompts, model inputs/outputs and data used by AI (new niche vendors + acquisitions).

Major Challenges

  • Operationalizing AI outputs — translating AI alerts into reliable, repeatable security playbooks without overwhelming SOCs.

  • Economic pressure & procurement — buyers demanding demonstrable ROI while vendors race to differentiate on AI claims.

  • Attacker use of AI — adversaries are also adopting AI to automate attacks and evade detection, creating an arms race.

Attractive Opportunities

  • AI-secure AI — tooling to protect LLMs, prevent prompt injection and govern “shadow AI” usage inside enterprises. Recent acquisitions highlight strategic focus.

  • SMB-targeted, turnkey AI security — simplified, SaaS-based detection for smaller organizations currently underserved.

  • Managed detection + AI ops (MDR + AI) — outsourcers combining AI with human analysts to scale SOC coverage.

Key factors of market expansion

  • Data availability & telemetry quality — better sensors, cloud logs and integrated telemetry make AI more effective.

  • Trust & explainability improvements — tools that make AI decisions interpretable will accelerate enterprise purchase.

  • Regulatory clarity and standards for AI in security — clearer guidance will help CIOs approve AI projects at scale.

If you want, I can now convert this into one of the following (I’ll generate it immediately):

  1. One-page vendor table (Company | 2023–2024 revenue or ARR | AI angle | source links).

  2. 2–3 slide PPT summarizing market size, 5 company snapshots and 3 strategic recommendations.

  3. Competitive matrix (EDR, Cloud-security, Network AI, AI-security specialists) with cited metrics.

Which deliverable would you like?