The cybersecurity landscape in 2025 is defined by an
"AI-first"
paradigm: generative AI and large language models (LLMs) are driving both new attack methods and
novel
defenses. Criminals weaponize LLMs for more convincing social engineering and automated malware,
while
defenders deploy LLM-powered tools for detection, investigation and response. The following
report surveys the
current threat landscape, emerging defenses, technical enablers, regulatory changes, funding
trends, and
future outlook – all focusing on how AI (especially generative AI/LLMs) is reshaping
cybersecurity.
Current Threat Landscape
AI-enabled Phishing
& Social Engineering: Attackers use LLMs to craft highly realistic
spear-phishing messages.
A 2024 case study of an Indian bank shows AI-generated emails mimicking a CEO's writing
style and internal
formattingcyberpeace.org.
Industry surveys warn of "AI-powered phishing" campaigns that automatically translate and
personalize
messages for scalemailgun.commicrosoft.com.
These LLM-crafted lures exploit context gleaned from social media, blogs and contact
history, tricking
recipients into disclosing credentialscyberpeace.orgmailgun.com.
AI agents can even simulate conversation or reply in real time, hugely expanding "social
engineering as a
service" for criminalsmailgun.comadaptivesecurity.com.
Deepfake Attacks
& Personas: Generative AI makes realistic audio/video deepfakes cheap and
accessible.
Threat actors now deploy AI-generated "deepfake personas" – cloned voices or videos of
executives – to
extract data or funds. Security leaders note that attackers "can generate realistic AI
personas – deepfake
versions of your coworkers, your CEO, even you – in seconds," using open-source LLMs and
image models to
fine-tune convincing forgeriesadaptivesecurity.comweforum.org.
Such "industrialized deception" has already led to multi-million dollar frauds. Industry
reports warn that
"deepfakes…represent a toolset for cyber criminals"weforum.orgmailgun.com,
amplifying threats like business email compromise and misinformation.
LLM-Generated
Malware and Exploits: Adversaries turn LLMs loose on code generation. By training
on malware
repositories or carefully prompting ChatGPT-style models, attackers can automatically
produce new malware
variants and exploit scripts. For example, researchers note that LLMs can "create many
different attacks
with different characteristics but similar functionality," effectively automating
polymorphic malwareblog.barracuda.com.
New "dark LLMs" – clandestine AI tools built for crime – have emerged. FraudGPT and DarkBart, for
instance, are
LLMs explicitly designed to write phishing templates, cracking tools and crypto-theft
scriptsblog.barracuda.com.
Even "restricted" LLMs can be coerced into aiding attacks through prompt engineering:
miscreants coax
ChatGPT to design phishing payloads or ransomware by bypassing filtersblog.barracuda.comblog.barracuda.com.
In short, generative AI is lowering the bar for skilled malware development and enabling
attackers to
scale.
Overall, security reports emphasize that "emerging threat actor techniques include AI-enabled spear
phishing… [and]
deepfakes"microsoft.comadaptivesecurity.com.
In practice, we see more highly customized phishing with eerily natural language, and hackers
openly
discussing using LLMs for offensive code. The threat volume and sophistication are surging: one
email security
vendor observes that automated, polymorphic phishing (often AI-assisted) accounts for a spike in
malicious
email every 42 seconds in 2024 (up from prior years). Cybercriminals regard AI as "a weapon of
mass
manipulation," broadening every attack chain from recon to exploitationadaptivesecurity.comblog.barracuda.com.
Defensive Innovation
Defenders are fighting back with their own AI-driven
tools. Major
vendors and startups alike now offer LLM-powered
security
platforms and automation to detect, triage, and respond to threats faster:
Microsoft Security
Copilot: Microsoft's Copilot for
Security (GA April
2024) embeds GPT-like intelligence into SOC workflows. In live trials, analysts using
Copilot were 22%
faster and 7% more accurate in common security tasksmicrosoft.com.
Copilot lets analysts query security data in natural language, get summarized incident
reports, and
generate remediation scripts automatically. Its knowledge-augmented LLM sifts through alerts
and logs from
Microsoft Defender and other Microsoft tools to pinpoint threats, breaking down data silos
and reducing
manual effortmicrosoft.com.
CrowdStrike
Charlotte AI: CrowdStrike's Charlotte
AI injects
"agentic" AI into the Falcon platform. Charlotte automates alert triage and accelerates
investigations
across endpoints and cloud workloadsprophetsecurity.ai.
It performs bounded autonomous response actions (e.g. isolating hosts, capturing forensics)
under
analyst-defined guardrailsprophetsecurity.ai.
By blending LLM-driven reasoning with CrowdStrike's massive Threat Graph, Charlotte helps
analysts hunt
threats rapidly and at scale.
SentinelOne Purple
AI: SentinelOne's Purple AI (with its
new "Athena"
engine) evolved from a basic LLM chatbot into a full-fledged autonomous analyst. Purple can
ingest data
from multiple security tools (not just SentinelOne), automatically prioritize alerts, and
even execute
common remediation playbooksprophetsecurity.ai.
It performs real-time detection, triage and response actions via its Singularity platform.
SentinelOne
notes that Purple AI "automates investigations and
prioritizes
threats using OCSF-normalized data"prophetsecurity.ai.
In user trials, Purple can answer multi-step English queries (e.g. "Which servers contacted
this malicious
IP last 24h?") and take protected actions under supervision.
Other
AI-Driven SOC
Tools: Darktrace's Cyber AI Analyst is
a pioneer in
autonomous investigation. Using graph-based and custom transformer models (codenamed
DIGEST/DEMIST-2),
Darktrace's AI analyst autonomously investigates alerts and crafts incident reportsprophetsecurity.ai.
In 2024 it ran 90 million such investigations – equivalent to 42 million analyst-hoursprophetsecurity.ai.
New entrants like Prophet Security and others are also touting "AI SOC analysts" that use
multimodal AI to
cross-correlate logs, emails and user behavior. Many SOAR (security orchestration) and XDR
platforms now
embed LLM query interfaces, letting human analysts interact in natural language or even
delegate complex
tasks to AI agents.
Figure: Modern security platforms are
incorporating AI/LLM
layers to detect threats and automate response. New "AI SOC analyst" tools can query data in
plain English,
automatically triage alerts, and suggest actions in real time.
LLMs are also being used inside detection engines.
For example,
Palo Alto Networks and others now use AI models to scan logs and network flows for suspicious
anomalies that
traditional tools miss. Transformer models (similar to GPT) can be fine-tuned for anomaly
detection or for
understanding code payloads. One strategy is RAG
(Retrieval-Augmented Generation): when an alert occurs, the system retrieves relevant
threat
intelligence (e.g. YARA rules, CVE text) and feeds that context into an LLM to decide if it's
malicious.
Another trend is autonomous threat-hunting
agents: AI
agents continuously roam the network or cloud, form hypotheses, and spawn investigation flows
without human
prompting. These agents may use internal knowledge bases (company policies, architecture
diagrams) and
historical attack data to hunt proactively. In sum, defensive innovation in 2025 means layered
AI: from LLM
chat interfaces for analysts to embedded AI in every detection stage.
Technical Components
Underpinning these innovations are new AI/ML
techniques and
architectures:
Retrieval-Augmented
Detection (RAG): LLMs augmented with threat databases are used to match patterns.
For example,
a query about an email might pull related past incidents or known phishing templates from a
knowledge
store, then prompt an LLM to assess new variants. This RAG approach improves recall of
emerging threats by
providing context the model didn't see in pre-training.
Autonomous AI
Agents: Agentic AI (self-driving AI) is central. These are LLM-based agents that
can execute
multi-step tasks – e.g. "triage this alert chain". They can autonomously gather logs,
cross-reference
identity and asset info, and even enact responses (with human oversight). Tools like
Charlotte AI and
Purple AI exemplify this: they use "bounded autonomy" to act like junior analysts. Research
in 2024
highlights how graph neural networks combined with LLMs can enable AI to "think like an
attacker and
defender" simultaneouslyaibusiness.com.
Transformer-based
Anomaly Detection: Transformer architectures (BERT/GPT-style) are now applied to
network
traffic and log data. Instead of rule-based signatures, these models learn "normal" behavior
patterns in
high-dimensional space. For instance, a transformer model can be trained on time-series of
DNS queries or
process telemetry, then flag subtle deviations. 5G and IoT networks, in particular, see
novel
transformer-based IDS models that use contrastive learning to spot anomalies without
explicit
labellingieeexplore.ieee.org.
These AI models can detect encrypted or zero-day exploits by recognizing abnormal execution
flows.
Technically, many platforms integrate multi-modal AI (text, code, and imagery). For
example, AI root-cause analysis
might combine log text (NLP), exploit code analysis (code LLMs), and even image OCR from
screenshots of
alerts. The end goal is a context-rich AI reasoning engine that spans all data planes, closing
gaps between
email, endpoint, network, and user behavior.
Regulatory &
Compliance Updates
Governments and standards bodies are rapidly
catching up to
AI-driven cyber risks:
EU
Cyber
Resilience Act (CRA): Enacted Dec 10, 2024, this new EU regulation mandates that
any hardware
or software with a digital component meet strict security-by-design requirements. From Dec
2027 onward,
vendors must ensure ongoing security maintenance (patches, updates) throughout product
lifecyclesdigital-strategy.ec.europa.eu.
Products will carry a CE mark indicating CRA compliance. This affects IoT devices,
industrial control
systems, and any AI-capable gear sold in Europe. Manufacturers now face greater liability if
an AI-powered
product is breached or used as an entry point.
US
SEC Cyber
Rules: In 2023–2024 the US Securities and Exchange Commission finalized rules
requiring public
companies to disclose material cyber incidents quickly and to report annually on their cyber
risk
management. Crucially, companies must tag disclosures using Inline XBRL. The SEC has even
released a
Cybersecurity Disclosure Taxonomy, so
companies
"report and disclose cybersecurity information" (incident details, governance, policies) in
a structured
waysec.gov.
These rules mean that any AI-related breach (e.g. a ChatGPT-induced phishing loss) must be
transparently
reported, increasing board-level focus on AI risk.
ISO/IEC 42001 AI
Management Standard: In December 2023, ISO published ISO/IEC 42001, the world's first Artificial Intelligence
Management System
standardkpmg.com.
Like ISO 27001 for information security, ISO 42001 provides a framework for governing AI
development and
deployment. It requires organizations to assess AI risks (bias, safety, security) and
establish controls
across AI lifecycleskpmg.com.
Early adopters are aligning their AI governance with 42001. It is already cited as a
cornerstone for
forthcoming laws (e.g. the EU AI Act)kpmg.com,
so that complying with ISO 42001 also helps companies meet regulatory expectations on
trustworthy AI.
In summary, 2024–2025 saw major regulatory moves
around
software/AI security. The CRA raises the bar for product security, the SEC makes cyber-incident
reporting
granular and data-drivensec.gov,
and ISO's new AI standard gives organizations guidance on safe AI. Compliance teams now must
account for AI
aspects in cybersecurity controls and disclosures.
Investment Landscape
The venture and corporate investment scene is hot
for AI+cyber:
Deal Flow &
Funding: After a dip, Q1 2025 funding for cyber startups surged: VC investment
hit $2.7 billion (up 29% from Q4
2024)news.crunchbase.com.
Notable is the $32 billion acquisition of cloud-security unicorn Wiz by Google/Alphabet –
the largest ever
in the spacenews.crunchbase.com.
That megadeal is attracting more investor interest in next-gen cyber tools. Cyber VC experts
note that "AI – and more specifically, agentic
AI" is a key factor
driving this renewed fundingnews.crunchbase.com.
In other words, investors see promise in startups that can automate and accelerate security
operations.
Major
Rounds: Several flagship AI-cyber startups raised big rounds in 2024–25. Israel's
Dream
(co-founded by ex-leaders of Austria and NSO Group) closed a $100 M Series B at a $1.1 B valuationaibusiness.com.
Dream's pitch: AI models that "think like both a defender and an attacker," using predictive
posturing to
"eliminate threats before they surface"aibusiness.com.
In April 2025 Adaptive Security announced a $43 M
Series A led by Andreessen Horowitz and OpenAI's Startup Fundadaptivesecurity.com
– notably OpenAI's first investment in cybersecurity. Adaptive focuses on AI-driven
anti-phishing and
cyber-awareness. In May 2025, Indian firm CloudSEK (cyber-risk intelligence) raised $19 M for its AI-based threat prediction
platformm.economictimes.com.
These funding rounds highlight key areas: AI-powered phishing defense, cloud and national
security, and
proactive threat intelligence.
Funding
Trends: Crunchbase reports show AI/security startups attracting more interest in
2025. The
largest rounds are generally U.S. and Israeli, often with participation from major tech VCs
and strategic
investors (e.g. cloud providers). Many investors emphasize automation and scalability: in
one analysis, a
managing director noted that "agentic AI…has the potential to make cybersecurity
professionals more
effective, streamline operations and reduce time to resolution"news.crunchbase.com.
In practice, funding tends to favor startups incorporating LLMs or advanced AI into their
products. At the
same time, overall deal count is down (Q1 2025 saw fewer deals than Q1 2024), indicating
possible
consolidation – big exits like Wiz's acquisition may squeeze mid-market valuations.
Nonetheless, the
AI-cyber intersection remains a hot segment, capturing a growing share of fintech and
enterprise tech VC
dollars.
Future Outlook
Looking ahead, several emerging trends point to an
even more
autonomous, AI-centric security posture:
Agentic AI
Security Tools: Analysts foresee fully autonomous "AI SOC analysts" and digital
cops. These
tools will not just suggest actions but carry them out (within policy) – for example,
deploying patches or
reconfiguring firewalls in real time. We will see more automated red-teaming by AI as well:
AI agents that
continuously probe an organization's defenses and harden gaps. In time, security platforms
may largely
self-operate under human supervision. Early signs are visible: Darktrace, Microsoft and
others talk about
reducing "analyst fatigue" via AIgovinfosecurity.com.
The goal is an AI-driven security posture that learns and adapts on its own, only escalating
truly novel
incidents to humans.
AI-Enabled Zero
Trust: The Zero Trust paradigm ("never trust, always verify") is being enhanced
with AI.
Experts believe the "end goal is an AI-enabled zero
trust
environment that can prevent breaches and elevate security posture automatically, without
any human
intervention.”govinfosecurity.com
In practice, AI can optimize micro-segmentation and dynamic policy enforcement: for example,
an AI engine
might automatically adjust least-privilege access or quarantine anomalous devices without
waiting for
manual rule changes. Early use cases include using AI to map asset relationships, detect
unusual access
patterns, and automate identity lifecycle. Ultimately, we expect "Zero Trust+AI" systems
that continuously
monitor every transaction and adapt controls in real time.
Edge-Based
Threat Prevention: As more processing shifts to the network edge (IoT, 5G, remote
work),
on-device AI will be key to security. Edge AI can analyze sensor data or local traffic for
attacks without
round-trip to the cloud. The Edge AI
cybersecurity
market is booming – one report values the US market at ~$8.93 B in 2024 (CAGR
~33.5%)market.us.
Companies are already deploying lightweight ML models on routers, phones and IoT hubs to
detect anomalies
at the source. By 2025–2030, expect much more embedded AI: chips designed to run small LLMs
for security,
and frameworks that let edge devices share threat intelligence peer-to-peer. This reduces
latency and
keeps sensitive data local, improving privacy and resilience against global AI-driven
attacksmarket.usmarket.us.
Continuous AI
Regulation & Standards: On the governance side, frameworks like ISO 42001
will evolve into
industry-specific standards. Regulators worldwide are likely to extend CRA-style rules to
other regions.
Cyber insurance and compliance audits will start asking specifically about AI risk controls.
We may see
new privacy/security guidelines on LLM usage (e.g. restrictions on running company LLMs vs.
public ones).
Overall, businesses will have to integrate AI risk management into their cyber and
compliance programs as
a permanent fixture.
In
conclusion, by
2025 generative AI and LLMs have become both indispensable tools and formidable threats in
cybersecurity. The offense-defense dynamic is accelerating: attackers use AI to
launch more
sophisticated, automated attacks, and defenders respond with AI at every layer of the stack.
Organizations
must prepare for this dual reality with AI-savvy strategies, investing in both advanced AI
defenses (copilots,
agents, automated IR) and the training/governance to use them responsibly. Only a proactive,
AI-first approach
can keep pace with AI-first adversaries.
