Deepfakes represent a rapidly escalating cybersecurity threat
The deepfake threat is real, fast, and global. In 2023, deepfake incidents increased 3,000%. One specific example says more than any theoretical model: a CFO got a call at 3 a.m. from someone who sounded exactly like the CEO. Voice, accent, breathing, even the familiar throat clear, spot on. He authorized a million-dollar transfer. In the morning, they found the CEO was asleep in London. That voice wasn’t human. It was synthetic. The money? Gone.
This technology doesn’t require expensive tools or months of planning anymore. By 2024, a convincing voice clone needs under three minutes of audio. That’s it. Three minutes. You can find that everywhere, from company earnings calls to random podcasts. And once attackers have the voice, the rest is just intent.
From a leadership perspective, this shifts trust dynamics in the workplace. Internal and external communication channels, once considered secure, are now part of the attack surface. Employees may struggle to distinguish a real executive from AI-generated audio. Customers might be misled in the same way. You can’t rely on what seems familiar. And that’s the fundamental issue, deepfakes don’t break passwords, they break trust.
Companies are already deploying countermeasures. OpenAI’s GPT-4o now includes deepfake detection directly in its security layers. That’s not just a patch; it’s an evolution in how we protect digital identity. But it’s still not standard across industries. And it needs to be, fast.
According to Persona’s 2024 Identity Fraud Report, they blocked 75 million deepfake attempts related to hiring fraud alone. One solution provider. One vertical. Scale that across industries, and what you get is a threat environment that’s outpacing conventional security thinking. That $40 billion loss projected by 2027?
George Kurtz, CEO of CrowdStrike, put it simply in a recent Wall Street Journal briefing: deepfakes today are weaponized narratives. They’re driven by AI and often fueled by hostile nation-state actors. The problem isn’t just the tech, it’s who’s behind it and what they’re doing with it.
Deepfakes destroy trust. Rebuilding that trust requires systems that verify identity and intent at every level, in real time. That means AI detecting AI, not quarterly audits or outdated scripts. Executives who act now can reduce the damage curve. Those who wait might fund someone else’s attack.
AI agents introduce an unregulated and expanding attack surface
AI agents are not just tools. They’re users, highly active ones. And unlike humans, they don’t clock in and out. They don’t take breaks. They don’t forget passwords. Once they’re in your system, they’re operating 24/7, processing data, running checks, generating insights. That’s where the real risk starts.
Each agent typically requires broad access to function, far beyond what’s traditionally given to most human users. This includes access to files, APIs, internal systems, and sometimes even decision-making layers. When they act, it’s with full authority. When they get compromised, the damage is silent and wide-ranging. One mission-critical use case already happened. An AI agent with full access to a company’s internal knowledge base was compromised, but not to steal data. Instead, the attacker fed it misinformation over time. Subtle, hard to detect. And it worked. Employees made decisions based on corrupted output. That’s not a breach, it’s quiet sabotage.
We’re at a point where these AI agents are multiplying fast. If your company uses tools like ChatGPT, Gemini, Claude, or Copilot at scale, you’re spawning new agents constantly, each with credentials, each with access privileges. Governance and oversight haven’t kept up with the generation speed.
Now add the fact that machine identities outnumber human ones by 45 to 1. That’s not just a problem. It’s a crisis of scale. Every AI agent grows that number exponentially, and they’re mostly invisible to legacy IAM systems. You can’t protect something your systems can’t see.
What you need isn’t more policies. You need real-time identity management that knows every access point in your ecosystem and can shut it down or adjust it dynamically. You need systems that can identify if an AI agent is acting out of pattern or manipulating results. Because the moment your AI is making decisions based on compromised inputs, your business is already on the wrong path.
C-suite leaders need to abandon the assumption that governance can be manually scaled. Cristian Rodriguez, Field CTO of CrowdStrike for the Americas, said it best: “These aren’t edge cases anymore. They’re today’s attack surface.”
So act like they are. Build models that assume agents are peers in access. Secure them accordingly. AI governance is a race. Fall behind, and you’ll find that your biggest threat isn’t a hacker, it’s an autonomous system you gave permission to.
Proliferation of machine identities creates blind spots in traditional IAM
Machine identities are growing at a rate legacy systems were never designed to handle. The numbers are extraordinary, today, organizations manage 45 times more machine identities than human ones. That includes containers, service accounts, APIs, digital certificates, most of which operate independently, expire quickly, and often go completely unseen.
This is where organizations start losing visibility. For example, containers may terminate within minutes, but in that short window, they still generate credentials, authenticate, access internal systems, and too often, vanish before traditional IAM platforms even register their existence. That leaves security leaders stuck with a delayed view of a constantly moving problem. And that delay creates actual exposure.
Even worse, most of these identities are poorly managed. CyberArk reports 92% of service accounts are orphaned, 67% of API keys are never rotated, and 40% of certificates are self-signed. These are significant gaps across environments where identity is quickly becoming the dominant attack surface. These credentials are the most attractive attack vectors available today, because they’re poorly controlled and rarely monitored.
Most current IAM tools don’t scale to this complexity and speed. So security teams rely on static access controls, periodic reviews, and human-led operations, which simply can’t respond fast enough. The result is an environment where access grows exponentially, but governance lags behind.
Automation is the only definitive answer here. Tools like Venafi TLS Protect are cutting detection and mapping time from weeks to hours while eliminating 89% of certificate-related outages. SPIFFE/SPIRE frameworks auto-rotate and terminate credentials associated with container workloads. These systems don’t rely on human memory or monthly cycles, they respond in real time.
Gartner is already putting numbers on this. Organizations without automated machine identity management are four times more likely to experience a breach. This isn’t just theory, there’s cause and effect. Companies automating this layer, in some cases, report a 73% drop in credential incidents within six months. That’s a timeline any executive team can plan around.
Karl Triebes from Ivanti said it clearly: “Traditional IAM systems can’t even detect these identities.” That statement should be a call to action. If your systems can’t see it, they can’t protect it. And in cybersecurity, what you miss costs you the most.
Shadow AI unleashes unsanctioned, invisible risks
Shadow AI is not an emerging issue, it’s fully active and scaling. Every day, departments download new AI-powered tools that fall completely outside IT’s line of sight. These unsanctioned applications are easy to adopt, deliver instant utility, and require no approval process. That speed is useful for teams, but it’s a blind spot for security.
IT doesn’t have visibility into how these tools process sensitive corporate data or where that data ends up. And that lack of control directly translates into financial and regulatory risk. According to IBM’s 2025 projections, breaches related to shadow AI cost $4.63 million on average, 16% above baseline breaches. That’s higher cost, greater impact, and no preparation.
The numbers behind this risk are already massive. VentureBeat research shows over 74,500 unauthorized AI tools actively in use across enterprises by mid-2025. And that number is growing by 5% each month. ChatGPT accounts alone? 73.8% of them being used at work are unauthorized, according to Cyberhaven. These aren’t one-off incidents, they’re routine.
The issue is one of governance, not prevention. Total bans drive AI experimentation underground. That’s where security gets harder, not better. Executives need to create sanctioned pathways that enable innovation without creating unmanaged sprawl. Prompt Security’s CEO, Itamar Golan, says they’re already tracking more than 12,000 AI apps, as many as 50 new ones every day.
Vineet Arora, CTO of WinWire, points out the risk of ignoring this. He describes environments where dozens of random AI tools are actively processing company data with no compliance checks, and no one in security knows they exist. His recommendation: build governance frameworks that work with human behavior, not against it. Create an Office of Responsible AI. Deploy AI-aware security controls. Prioritize zero-trust principles specific to AI systems.
By acting now, enterprises can get ahead of regulatory momentum. The European Union’s AI Act is expected to outpace GDPR in terms of fines. But fines aren’t the main concern, trust and control are. Once AI applications take root without oversight, you don’t just have exposure, you have fragmented intelligence working across your business.
Leadership needs to treat shadow AI as a strategic issue, not a rogue technical problem. Start by mapping it. Put policies in place. And guide employees to sanctioned tools instead of restricting them into shadow paths. Golan and Arora are correct: the risk isn’t AI itself, it’s AI without guardrails.
Modern identity threats accelerate beyond the capabilities of static security practices
The threat landscape is changing. Fast. Deepfakes, AI agents, and machine identities aren’t technology trends, they’re the current attack surface. Traditional security practices, quarterly access reviews, static rules, and manual controls, don’t work at this scale. They can’t keep up with identities that operate at machine speed and change dozens of times in a single day.
These aren’t theoretical gaps. They’re practical failures. A deepfake doesn’t wait for a compliance cycle. An unmanaged AI agent doesn’t pause for an audit. Once compromised, these systems can move data, influence decisions, and carry out instructions faster than most security programs can detect. That means executives must shift thinking, from trying to prevent every breach to building systems that minimize the blast radius when breaches happen.
IBM’s 2024 Cost of a Data Breach Report supports this approach: assume breach, contain quickly, and limit lateral movement. Don’t rely on controls that expect perfect conditions. Security needs to be flexible, real-time, and integrated with how systems actually function in high-velocity environments.
This also means visibility has to come first. Before you build another security layer, you need to know what identities exist across your business, human, machine, and AI. Without full visibility, governance becomes reactive. That’s when mistakes become breaches and misconfigurations turn into multi-million dollar losses.
Static governance must evolve into dynamic identity security. Platforms must learn, adjust, and automate. Control structures must detect threats as they happen, not days or weeks later. Many systems already support this, but their value depends on strategic deployment, not checkboxes.
Executives need to lead that shift. Eric Hart, Global CISO at Cushman & Wakefield, said the quiet part out loud: “It’s not about not having any security events. It’s about minimizing damage when they inevitably occur.” That mindset doesn’t reflect defeat. It reflects clarity. You build systems that are ready, not hopeful.
AI-Powered security tools must be deployed urgently to combat emerging threats
The tools are here. They’re capable. And they work. The only problem is many organizations haven’t deployed them yet. That delay is the real exposure.
AI-powered security platforms are already handling the complexity, speed, and scale of deepfakes, AI agents, and machine identity sprawl. These systems don’t rely on human reaction speed. They run autonomously, with real-time threat detection, credential lifecycle automation, and behavioral analytics that identify issues as they happen, not after.
Today, companies like CrowdStrike, SentinelOne, Palo Alto Networks, Microsoft, CyberArk, and Venafi offer highly advanced platforms for identity security that unify human, machine, and AI components. Many of these solutions produce measurable results, like CyberArk’s reported 73% reduction in credential-related incidents after just six months of deployment.
These tools are not experimental. They’re operational. Microsoft’s Security Copilot, Venafi’s Control Plane, and ForgeRock’s Autonomous Identity are built for scale. Ivanti’s platforms manage short-lived machine identities that traditional systems completely miss. These platforms are already used in large enterprises that are serious about reducing breach probability, not just reporting on it.
For C-suite leaders, waiting is no longer an option. Each month of delay amplifies your exposure as AI tools propagate across your environment, in ways IT often doesn’t control. What matters now is having the infrastructure in place to govern these tools intentionally.
This isn’t about investing in hypothetical potential. It’s about adopting working systems that already align with where cybersecurity is going. The shift from static security to autonomous, AI-powered defense isn’t a choice anymore. It’s the current requirement.
This is where leaders make the real difference. The organizations that will stay secure are those that use these platforms strategically, understanding not only the tools, but the scale of the problems they were designed to solve. The winners will recognize that identity security is now central, and that machine-speed threats require machine-speed responses.
Key takeaways for leaders
- Deepfakes are rewriting the rules of trust: Enterprises face a $40B threat as deepfakes scale rapidly, with over 75M attempts blocked in one sector alone. Leaders must prioritize AI-driven identity verification to protect reputational and financial integrity.
- AI agents operate without oversight or limits: These autonomous systems often hold broad permissions and constant access. Leaders should enforce real-time governance frameworks before AI agents make decisions that outpace human control structures.
- Machine identities now outnumber human ones 45 to 1: With exploding machine credential sprawl and 68% of breaches tied to non-human identities, organizations must replace legacy IAM with dynamic, automated identity controls to mitigate critical exposure gaps.
- Shadow AI is inflating breach costs by 16%: With 74,500+ unauthorized AI apps active and rapid growth underway, C-suite teams must formalize AI governance, provide approved tools, and establish clear usage policies to avoid costly, invisible risk.
- Static security models can’t handle machine-speed threats: Traditional access reviews and manual controls are too slow. Executives should pivot to security strategies built for real-time containment, identity visibility, and automated response.
- AI-based security tools are available and delivering results: Organizations that deploy trusted, AI-enhanced platforms like CyberArk, SentinelOne, and Microsoft Security Copilot reduce breach likelihood and cut incidents significantly. Decision-makers should act now before threat velocity outpaces their defenses.
