Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
By Ravi Ithal, GVP and CTO of
Proofpoint DSPM
If
I had a dollar for every prediction that starts with "AI is going to be big,"
I'd have enough to fund my own AI startup. Let's face it, the buzzwords are
endless, and most predictions stop short of saying anything truly surprising.
But here's the deal: predictions that matter should take you out on a limb,
making you think about risks and opportunities you haven't considered yet. With
that in mind, let's dive into what might be coming in 2025-and why getting
human-centric data defenses right will make or break the next wave of
innovation.
The Chaos of
Multi-Cloud Environments
The shift to multi-cloud strategies is
fundamentally reshaping IT infrastructure. On the surface, cloud adoption
offers unparalleled flexibility and resilience. However, it also introduces
unprecedented complexity in managing and securing data. Imagine sensitive files
replicated across multiple clouds, each governed by inconsistent security
policies and configurations. This fragmented approach creates fertile ground
for shadow data-untracked or unmanaged data-and accidental exposure.
The challenge intensifies with the
introduction of AI. These systems consume vast amounts of data to operate
effectively but lack the ability to distinguish between sensitive and
non-sensitive information. In a multi-cloud environment, even a single poorly
managed data input can inadvertently lead to the leakage of critical
information across interconnected systems.
To address these challenges,
organizations must move beyond basic security protocols. They need
comprehensive, real-time visibility into their data: understanding where it
resides, how it is classified, and who has access to it. This enhanced approach
to data security acts as the anchor that keeps organizations steady amid the
complexity of multi-cloud and AI-powered operations.
AI as the
Developer's Apprentice-and Its Unintended Risks
By
2025, AI tools will have evolved into the ultimate developer's apprentice,
automating routine tasks, catching bugs, and even suggesting code
optimizations. The promise of faster, more efficient software development is
real. However, there's a hidden risk: what happens when these tools have access
to sensitive data?
Consider
a scenario where an AI-powered tool is tasked with reviewing a codebase and
unknowingly flags data containing sensitive information. If that data is stored
or processed improperly, it could lead to compliance violations or worse, data
breaches. While AI can speed development cycles, it can also amplify security
blind spots if not monitored carefully.
Developers
and security teams must work hand in hand, treating AI as both a tool and a
potential threat. Building security into the development pipeline, from the
first line of code to deployment, will be critical in avoiding the unintended
consequences of AI integration.
DevOps
Transformed: The Predictive Production Line
AI's
impact won't stop at development-it'll revolutionize DevOps, turning it into
what I call a "predictive production line." By analyzing data in real time, AI
can predict deployment bottlenecks, spot potential vulnerabilities, and
recommend fixes before issues arise. This proactive approach will reduce
downtime and accelerate software delivery.
But
here's the catch: these predictive systems require access to massive
datasets-many of which are sensitive or proprietary. If that data isn't
properly managed, AI's insights could become a liability instead of an asset.
Worse, attackers could exploit these systems to gain access to highly valuable
information.
To
stay ahead, DevOps teams will need robust data security frameworks that ensure
sensitive data remains protected at every stage. Real-time monitoring and
anomaly detection will become essential components of the DevOps toolkit.
The Security
Challenges of Serverless Computing
Serverless
computing is gaining traction as a way to simplify application deployment. By
abstracting infrastructure management, developers can focus entirely on writing
code. But this convenience comes at a cost: security responsibilities shift
from organizations to cloud providers, leaving gaps that are easy to overlook.
For
example, in serverless architectures, businesses often lose visibility into how
data is processed and stored. Without proper oversight, they risk
misconfigurations that expose sensitive information. As serverless computing
becomes mainstream, organizations will need tools that provide visibility and
control over these environments, ensuring that security isn't sacrificed for
scalability.
The Road Ahead:
Preparing for 2025
As
AI reshapes everything from software development to infrastructure management,
one thing remains constant: the critical importance of data security. In 2025,
organizations that succeed will be those that embrace a data-first approach to
cybersecurity.
What
does that look like in practice? First, it means understanding where your
sensitive data resides and ensuring it's properly classified and monitored.
Second, it requires integrating security into every layer of your
operations-from development pipelines to cloud environments. Finally, it means
recognizing that AI isn't just a tool for innovation; it's also a potential
threat vector that must be managed carefully.
And let's not forget the external
factors. With the new political regime in the U.S., we can expect curveballs
like changing tariffs, regulatory shifts, and AI-related policy chaos. These
political moves may not seem immediately relevant to cybersecurity, but they
create ripple effects that directly impact it. Here's how:
- Supply Chain
Disruptions: Tariffs can increase costs or
disrupt the availability of key components in IT infrastructure. This can
force organizations to make rapid changes in vendors or suppliers,
introducing unknown variables into their systems. Unvetted or hastily
integrated components can open vulnerabilities, creating new attack
surfaces for cyber threats.
- Regulatory Overlap and Compliance Challenges: As new policies emerge-particularly in sectors like
AI-organizations must navigate complex compliance requirements that vary
by jurisdiction. This heightened complexity often leaves gaps in security
policies, making it harder to maintain a consistent and secure posture
across multi-cloud or global operations.
- Increased Threat
Activity: Political and economic
changes, including tariffs, often heighten geopolitical tensions. Cyber
actors-whether state-sponsored or opportunistic-can exploit this
uncertainty, targeting industries or regions perceived as vulnerable.
To stay resilient in this evolving
landscape, organizations must adopt proactive cybersecurity measures, ensuring
real-time visibility and adaptive protection that can weather these external
shifts. By tying political and economic unpredictability to the need for agile
cybersecurity strategies, we underscore the interconnectedness of these
dynamics.
The opportunities in 2025 are immense,
but so are the risks. By prioritizing data security now, organizations can
confidently leverage AI's transformative potential without falling victim to
its unintended consequences. It's time to stop seeing data security as a checkbox and
start treating it as the foundation for everything else. The future demands it.
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ABOUT THE AUTHOR
With a career steeped in enterprise and cloud security, Ravi Ithal is the
group vice president and chief technology officer for Proofpoint's Data
Security Posture Management (DSPM) team. Mr. Ithal is responsible for advancing
innovative technologies that help organizations discover, classify, and protect
their data at scale-transforming how businesses safeguard what matters most.
Before joining Proofpoint through the acquisition of Normalyze, a leading
DSPM company he co-founded, Mr. Ithal built a reputation as a trailblazer in
the security industry. As the co-founder and chief architect of Netskope, he
helped develop cloud-native solutions to defend businesses against modern
threats while protecting critical data. Mr. Ithal was one of the founding
engineers at Palo Alto Networks, shaping the architecture behind one its
security platform.. His earlier career saw him holding impactful engineering
roles at Juniper Networks and Cisco, where he honed his skills in building
secure, scalable systems.