Disinformation security protects organizations from deepfakes, brand impersonation, and synthetic fraud. Gartner predicts 50% enterprise adoption by 2028.
When Patrick Hillmann started receiving thank-you messages for meetings he’d never attended, he knew something was wrong. The Chief Communications Officer at Binance, one of the world’s largest cryptocurrency platforms, soon discovered that scammers had created an AI-generated deepfake of him using footage from his TV appearances and news interviews. The fake Hillmann was holding video meetings with crypto projects, complete with convincing voice and mannerisms, offering to list their tokens on Binance in exchange for fees.
“Other than the 15 pounds that I gained during COVID being noticeably absent,” Hillmann wrote in a 2022 company blog post, “this deepfake was refined enough to fool several highly intelligent crypto community members.“
The incident wasn’t isolated. It was an early signal of why disinformation security—Gartner’s term for an emerging category of defense against synthetic media, impersonation, and coordinated deception—has become one of the fastest-growing priorities in enterprise cybersecurity.
The problem legacy tools can't solve
Traditional cybersecurity focuses on protecting networks, endpoints, and data from unauthorized access through familiar mechanisms: firewalls block malicious traffic, antivirus software catches malware, and intrusion detection systems alert on suspicious activity inside the perimeter. These tools were built for a clear threat model: keep attackers out, detect breaches quickly, and limit damage.
But modern threats increasingly bypass the perimeter entirely. They don’t exploit technical vulnerabilities: they exploit trust. An attacker doesn’t need to breach your network when they can impersonate your CFO on a video call and convince an employee to transfer $25 million. They don’t need malware when a convincing fake website or social media profile can harvest credentials from thousands of customers.
According to Gartner’s research, current digital risk protection platforms operate on detection delays of nine hours or more. In that window, AI-powered attackers can register lookalike domains, build convincing phishing sites, deploy fake social media profiles, and harvest credentials before the first alert fires.
The gap between attacker speed and defender response time has become existential.
Enter Disinformation Security
Gartner has identified “disinformation security” as one of its Top 10 Strategic Technology Trends for 2025, defining it as an emerging category that systematically discerns trust and provides methodological systems for ensuring integrity, assessing authenticity, preventing impersonation, and tracking the spread of harmful information.
The category addresses three core challenges:
Deepfake detection: Identifying AI-generated or manipulated audio, video, and images used in fraud. Deepfake attacks targeting executives surged 3,000% between 2022 and 2024, according to Deloitte. The technology has moved from novelty to mainstream criminal tool.
Impersonation prevention: Detecting and removing fake accounts, spoofed domains, counterfeit applications, and fraudulent digital assets across all channels. This includes not just websites and email, but social media platforms, mobile app stores, dark web forums, and messaging applications.
Reputation protection: Proactive monitoring for brand impersonation, coordinated inauthentic behavior, and narrative attacks designed to damage organizational reputation and erode customer trust.
What distinguishes this from traditional brand protection? Speed, automation, and AI-native detection capabilities designed specifically for synthetic content that doesn’t match known attack patterns.
Why the category Is growing
The market trajectory tells the story. Gartner predicts that by 2028, 50% of enterprises will adopt disinformation security solutions, up from less than 5% today. Enterprise spending in the category is projected to exceed $30 billion, drawing budget from both marketing and traditional security functions.
Several forces are driving adoption:
The retail fraud crisis: Return fraud alone cost retailers $100 billion in 2023, according to Pindrop research. Synthetic voice attacks targeting the retail sector increased 107% in 2024. Ecommerce fraud overall is projected to reach $107 billion by 2029, a 141% increase from 2024 levels.
The acceleration of AI-powered attacks: Overall deepfake fraud surged 1,300% in 2024, based on Pindrop’s analysis of more than 1.2 billion customer calls. The FBI documented $16.6 billion in total cybercrime losses in 2024, a 33% increase from the previous year.
Regulatory and liability pressure: When brand impersonation leads to customer losses, organizations face not just reputational damage but potential regulatory action. Some jurisdictions are beginning to hold financial institutions accountable for inadequate fraud prevention.
The failure of security awareness training: When AI-generated content contains no spelling errors, no awkward phrasing, and perfect contextual awareness, teaching employees to “spot red flags” becomes increasingly futile. The traditional security awareness approach was built for a threat landscape that no longer exists.
What effective platforms do differently
Organizations evaluating disinformation security platforms should look for capabilities that legacy tools don’t provide. Modern platforms analyze billions of URLs daily, using machine learning classifiers and computer vision to identify brand impersonation as threats emerge, not hours or days later. This real-time detection at scale represents a fundamental departure from periodic scanning approaches.
Rather than generating alerts for human review, effective platforms initiate takedowns automatically through direct integrations with registrars, hosting providers, and social platforms. Leading vendors report median takedown times measured in hours rather than days. The speed differential matters enormously when each hour of phishing site operation enables additional victim compromise. For organizations building these capabilities, understanding how to take down fraudulent websites has become essential operational knowledge.
Some platforms go further by deploying decoy credentials and honeypot infrastructure that waste attacker resources, generate threat intelligence, and make stolen data less valuable on underground markets. These proactive countermeasures shift the economics of attacks by increasing attacker costs while reducing their success rates.
Perhaps most critically, effective platforms provide visibility across all channels where impersonation occurs. Attacks don’t respect channel boundaries; a single fraud campaign might span phishing emails, spoofed domains, fake social media accounts, and counterfeit mobile apps. Point solutions that monitor only one channel leave dangerous blind spots between monitored surfaces.
What security leaders should consider
For organizations assessing their exposure, several questions warrant attention:
How quickly can you detect a brand impersonation attack? If the answer is measured in days rather than minutes, the gap likely exceeds what current threats will tolerate. The Arup deepfake incident unfolded over a week before the company’s headquarters was contacted for verification. By then, $25 million had been transferred across 15 transactions.
Are your defenses reactive or preemptive? Pattern-based detection struggles against novel, AI-generated content. Platforms that analyze intent and context (rather than matching known signatures) tend to perform better against emerging threats.
Do your monitoring capabilities match the attack surface? If you’re protecting your domain but not monitoring lookalike domains, social media impersonation, or fake mobile apps, you’re leaving significant exposure unaddressed.
Can you measure actual outcomes? Detection rates matter less than time-to-takedown and customer impact. The best metrics focus on threat dwell time and the speed of remediation (not the volume of alerts generated).
The Bottom Line
Nobody knows what’s real online anymore. In this environment, organizations that can establish and maintain digital trust will thrive. Those that can’t will face an accelerating cycle of customer fraud, brand damage, and regulatory scrutiny.
Disinformation security represents a fundamental shift in how organizations think about external threats. The question isn’t whether attackers will impersonate your brand, executives, or customer service—it’s whether you’ll detect it before significant damage occurs.
The organizations investing early in AI-native disinformation defense are building capabilities that match the sophistication and speed of automated threats. Those waiting for legacy vendors to catch up may find themselves explaining to boards and customers why their defenses couldn’t keep pace with entirely predictable attacks.
For security leaders evaluating their readiness, the time to act is before the incident that proves the point.
Key Takeaways
Disinformation security is an emerging category of cybersecurity technology focused on protecting organizations from synthetic media, brand impersonation, deepfakes, and coordinated deception campaigns. Gartner has identified it as a Top 10 Strategic Technology Trend for 2025, predicting that 50% of enterprises will adopt solutions in this category by 2028.
Legacy digital risk protection tools rely on pattern matching against known threats and manual analyst review, creating detection delays of nine hours or more. Modern AI-powered attackers can launch sophisticated, multi-channel campaigns in minutes, exploiting this gap to complete fraud before defenses respond.
The three pillars are deepfake detection (identifying AI-generated audio, video, and images), impersonation prevention (monitoring and removing fake accounts, domains, and apps), and reputation protection (tracking brand abuse and coordinated inauthentic behavior).
Return fraud alone cost retailers $100 billion in 2023, while overall deepfake fraud surged 1,300% in 2024. The FBI documented $16.6 billion in total cybercrime losses in 2024. Ecommerce fraud is projected to reach $107 billion by 2029, a 141% increase from 2024.
Effective platforms provide real-time detection at scale using AI and machine learning, automated takedowns through direct integrations with hosting providers and platforms, proactive countermeasures like decoy credentials, and unified visibility across web, social, mobile, and dark web channels.
Several forces are driving adoption: the retail fraud crisis, the acceleration of AI-powered attacks that traditional tools can’t detect, increasing regulatory and liability pressure, and the failure of security awareness training against AI-generated content that contains no obvious red flags.
